Group_1_Retail_Exercise.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "Group 1 Retail Exercise.ipynb",
      "version": "0.3.2",
      "provenance": [],
      "collapsed_sections": [],
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/tbowling87/RetailExercise/blob/master/Group_1_Retail_Exercise.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "metadata": {
        "id": "xeNCmjFvTmX0",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "# Retail Data Exercise: Group 1"
      ]
    },
    {
      "metadata": {
        "id": "1YyYEojXTweP",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "##Data Preparation"
      ]
    },
    {
      "metadata": {
        "id": "FcMgSISjSxJg",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "#import libraries\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "import datetime\n",
        "import io\n",
        "import sklearn\n",
        "from google.colab import files\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "jTXroJ8oT_Bl",
        "colab_type": "code",
        "outputId": "bd5a830a-a003-4682-f794-a5180b58dee6",
        "colab": {
          "resources": {
            "http://localhost:8080/nbextensions/google.colab/files.js": {
              "data": 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              "ok": true,
              "headers": [
                [
                  "content-type",
                  "application/javascript"
                ]
              ],
              "status": 200,
              "status_text": ""
            }
          },
          "base_uri": "https://localhost:8080/",
          "height": 74
        }
      },
      "cell_type": "code",
      "source": [
        "#upload raw data from beacons exercise - didn't want to save this in a public github!\n",
        "uploaded = files.upload()"
      ],
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "\n",
              "     <input type=\"file\" id=\"files-dcba53d0-2a3a-42ea-abf2-cfc5608a93c8\" name=\"files[]\" multiple disabled />\n",
              "     <output id=\"result-dcba53d0-2a3a-42ea-abf2-cfc5608a93c8\">\n",
              "      Upload widget is only available when the cell has been executed in the\n",
              "      current browser session. Please rerun this cell to enable.\n",
              "      </output>\n",
              "      <script src=\"/nbextensions/google.colab/files.js\"></script> "
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "Saving beacon_data.csv to beacon_data.csv\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "ocE5ocr4UL8P",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "#load the data in to a pandas data frame\n",
        "raw_data = pd.read_csv(io.BytesIO(uploaded['beacon_data.csv']), sep = \";\")"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "Zg5hLZLkVEYF",
        "colab_type": "code",
        "outputId": "220e8562-1f34-4abc-e4e7-f499e70462af",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        }
      },
      "cell_type": "code",
      "source": [
        "#take a peak at the raw data\n",
        "raw_data.head()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
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              "\n",
              "    .dataframe thead th {\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>id</th>\n",
              "      <th>gtid</th>\n",
              "      <th>vendor_id</th>\n",
              "      <th>subscriber_id</th>\n",
              "      <th>merchant_id</th>\n",
              "      <th>signal_type</th>\n",
              "      <th>entry_date</th>\n",
              "      <th>exit_date</th>\n",
              "      <th>latitude</th>\n",
              "      <th>longitude</th>\n",
              "      <th>dongle_id</th>\n",
              "      <th>distance</th>\n",
              "      <th>proximity</th>\n",
              "      <th>rssi</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2339839562</td>\n",
              "      <td>F9341167-6D6C-4321-924F-2E6B9D9CEF7A</td>\n",
              "      <td>135</td>\n",
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              "      <td>2133</td>\n",
              "      <td>2</td>\n",
              "      <td>2017-05-01 09:54:14.000000</td>\n",
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              "      <td>40.992124</td>\n",
              "      <td>29.126770</td>\n",
              "      <td>5536</td>\n",
              "      <td>-100.000000</td>\n",
              "      <td>unknown</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2340151194</td>\n",
              "      <td>EB93B8DF-1F8D-4BD6-A4E5-67B9BACBF6CF</td>\n",
              "      <td>135</td>\n",
              "      <td>17090954</td>\n",
              "      <td>2133</td>\n",
              "      <td>2</td>\n",
              "      <td>2017-05-01 10:28:45.000000</td>\n",
              "      <td>NaN</td>\n",
              "      <td>40.992183</td>\n",
              "      <td>29.126761</td>\n",
              "      <td>5536</td>\n",
              "      <td>1668.100537</td>\n",
              "      <td>far</td>\n",
              "      <td>-86.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2340151210</td>\n",
              "      <td>5563AB7A-C5B0-492D-80E5-04E52A5C0C28</td>\n",
              "      <td>135</td>\n",
              "      <td>17090954</td>\n",
              "      <td>2133</td>\n",
              "      <td>2</td>\n",
              "      <td>2017-05-01 10:28:45.000000</td>\n",
              "      <td>NaN</td>\n",
              "      <td>40.992183</td>\n",
              "      <td>29.126761</td>\n",
              "      <td>5536</td>\n",
              "      <td>1668.100537</td>\n",
              "      <td>far</td>\n",
              "      <td>-86.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2340151187</td>\n",
              "      <td>55AFD11A-06A3-4E9D-8735-445F3DE654D7</td>\n",
              "      <td>178</td>\n",
              "      <td>15630196</td>\n",
              "      <td>2133</td>\n",
              "      <td>2</td>\n",
              "      <td>2017-05-01 10:28:45.000000</td>\n",
              "      <td>NaN</td>\n",
              "      <td>40.992183</td>\n",
              "      <td>29.126761</td>\n",
              "      <td>5536</td>\n",
              "      <td>1668.100537</td>\n",
              "      <td>far</td>\n",
              "      <td>-86.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2340185826</td>\n",
              "      <td>52DE3BD3-655B-40F2-AC37-1199E0CBD31B</td>\n",
              "      <td>178</td>\n",
              "      <td>15630196</td>\n",
              "      <td>2133</td>\n",
              "      <td>2</td>\n",
              "      <td>2017-05-01 10:32:19.000000</td>\n",
              "      <td>NaN</td>\n",
              "      <td>40.992110</td>\n",
              "      <td>29.126783</td>\n",
              "      <td>5536</td>\n",
              "      <td>1467.799268</td>\n",
              "      <td>far</td>\n",
              "      <td>-85.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "           id                                  gtid  vendor_id  subscriber_id  \\\n",
              "0  2339839562  F9341167-6D6C-4321-924F-2E6B9D9CEF7A        135       29253903   \n",
              "1  2340151194  EB93B8DF-1F8D-4BD6-A4E5-67B9BACBF6CF        135       17090954   \n",
              "2  2340151210  5563AB7A-C5B0-492D-80E5-04E52A5C0C28        135       17090954   \n",
              "3  2340151187  55AFD11A-06A3-4E9D-8735-445F3DE654D7        178       15630196   \n",
              "4  2340185826  52DE3BD3-655B-40F2-AC37-1199E0CBD31B        178       15630196   \n",
              "\n",
              "   merchant_id  signal_type                  entry_date exit_date   latitude  \\\n",
              "0         2133            2  2017-05-01 09:54:14.000000       NaN  40.992124   \n",
              "1         2133            2  2017-05-01 10:28:45.000000       NaN  40.992183   \n",
              "2         2133            2  2017-05-01 10:28:45.000000       NaN  40.992183   \n",
              "3         2133            2  2017-05-01 10:28:45.000000       NaN  40.992183   \n",
              "4         2133            2  2017-05-01 10:32:19.000000       NaN  40.992110   \n",
              "\n",
              "   longitude  dongle_id     distance proximity  rssi  \n",
              "0  29.126770       5536  -100.000000   unknown   0.0  \n",
              "1  29.126761       5536  1668.100537       far -86.0  \n",
              "2  29.126761       5536  1668.100537       far -86.0  \n",
              "3  29.126761       5536  1668.100537       far -86.0  \n",
              "4  29.126783       5536  1467.799268       far -85.0  "
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 4
        }
      ]
    },
    {
      "metadata": {
        "id": "PYckUbw2VY6O",
        "colab_type": "code",
        "outputId": "aeb29d43-cc63-4cc3-b86c-ae472d8c1d15",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 297
        }
      },
      "cell_type": "code",
      "source": [
        "#check out a quick summary\n",
        "raw_data.describe()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>id</th>\n",
              "      <th>vendor_id</th>\n",
              "      <th>subscriber_id</th>\n",
              "      <th>merchant_id</th>\n",
              "      <th>signal_type</th>\n",
              "      <th>latitude</th>\n",
              "      <th>longitude</th>\n",
              "      <th>dongle_id</th>\n",
              "      <th>distance</th>\n",
              "      <th>rssi</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>1.252500e+05</td>\n",
              "      <td>125250.000000</td>\n",
              "      <td>1.252500e+05</td>\n",
              "      <td>125250.0</td>\n",
              "      <td>125250.000000</td>\n",
              "      <td>124279.000000</td>\n",
              "      <td>124279.000000</td>\n",
              "      <td>125250.0</td>\n",
              "      <td>109285.000000</td>\n",
              "      <td>109285.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>3.315534e+09</td>\n",
              "      <td>175.660072</td>\n",
              "      <td>2.201998e+07</td>\n",
              "      <td>2133.0</td>\n",
              "      <td>2.254930</td>\n",
              "      <td>40.974975</td>\n",
              "      <td>29.110562</td>\n",
              "      <td>5536.0</td>\n",
              "      <td>1408.055217</td>\n",
              "      <td>-70.516118</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>4.886681e+08</td>\n",
              "      <td>108.902448</td>\n",
              "      <td>8.206018e+06</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.666989</td>\n",
              "      <td>0.877990</td>\n",
              "      <td>0.629768</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1334.454282</td>\n",
              "      <td>33.815410</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>2.338573e+09</td>\n",
              "      <td>6.000000</td>\n",
              "      <td>2.699200e+04</td>\n",
              "      <td>2133.0</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>5536.0</td>\n",
              "      <td>-100.000000</td>\n",
              "      <td>-116.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>2.884233e+09</td>\n",
              "      <td>110.000000</td>\n",
              "      <td>1.645927e+07</td>\n",
              "      <td>2133.0</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>40.992013</td>\n",
              "      <td>29.126663</td>\n",
              "      <td>5536.0</td>\n",
              "      <td>480.440266</td>\n",
              "      <td>-90.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>3.387717e+09</td>\n",
              "      <td>135.000000</td>\n",
              "      <td>2.362873e+07</td>\n",
              "      <td>2133.0</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>40.992139</td>\n",
              "      <td>29.126753</td>\n",
              "      <td>5536.0</td>\n",
              "      <td>1257.377629</td>\n",
              "      <td>-85.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>3.795225e+09</td>\n",
              "      <td>331.000000</td>\n",
              "      <td>2.879388e+07</td>\n",
              "      <td>2133.0</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>40.992211</td>\n",
              "      <td>29.126796</td>\n",
              "      <td>5536.0</td>\n",
              "      <td>2154.434690</td>\n",
              "      <td>-78.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>3.927653e+09</td>\n",
              "      <td>1055.000000</td>\n",
              "      <td>3.573413e+07</td>\n",
              "      <td>2133.0</td>\n",
              "      <td>4.000000</td>\n",
              "      <td>41.396288</td>\n",
              "      <td>32.435247</td>\n",
              "      <td>5536.0</td>\n",
              "      <td>94675.469987</td>\n",
              "      <td>127.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                 id      vendor_id  subscriber_id  merchant_id    signal_type  \\\n",
              "count  1.252500e+05  125250.000000   1.252500e+05     125250.0  125250.000000   \n",
              "mean   3.315534e+09     175.660072   2.201998e+07       2133.0       2.254930   \n",
              "std    4.886681e+08     108.902448   8.206018e+06          0.0       0.666989   \n",
              "min    2.338573e+09       6.000000   2.699200e+04       2133.0       2.000000   \n",
              "25%    2.884233e+09     110.000000   1.645927e+07       2133.0       2.000000   \n",
              "50%    3.387717e+09     135.000000   2.362873e+07       2133.0       2.000000   \n",
              "75%    3.795225e+09     331.000000   2.879388e+07       2133.0       2.000000   \n",
              "max    3.927653e+09    1055.000000   3.573413e+07       2133.0       4.000000   \n",
              "\n",
              "            latitude      longitude  dongle_id       distance           rssi  \n",
              "count  124279.000000  124279.000000   125250.0  109285.000000  109285.000000  \n",
              "mean       40.974975      29.110562     5536.0    1408.055217     -70.516118  \n",
              "std         0.877990       0.629768        0.0    1334.454282      33.815410  \n",
              "min         0.000000       0.000000     5536.0    -100.000000    -116.000000  \n",
              "25%        40.992013      29.126663     5536.0     480.440266     -90.000000  \n",
              "50%        40.992139      29.126753     5536.0    1257.377629     -85.000000  \n",
              "75%        40.992211      29.126796     5536.0    2154.434690     -78.000000  \n",
              "max        41.396288      32.435247     5536.0   94675.469987     127.000000  "
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 5
        }
      ]
    },
    {
      "metadata": {
        "id": "rVPBdJ7eVcvF",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "#replace 0's in lat and long with averages\n",
        "raw_data = raw_data.replace({'latitude': {0: raw_data['latitude'].median()}})\n",
        "raw_data = raw_data.replace({'longitude': {0: raw_data['longitude'].median()}})\n",
        "\n",
        "#replace -100's in distance with na's\n",
        "raw_data = raw_data.replace({'distance': {-100: np.nan}})\n",
        "#repalce 0's and 127 with na's in the rssi column\n",
        "raw_data = raw_data.replace({'rssi': {0: np.nan,\n",
        "                                      127: np.nan}})"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "SNBwq3N5VuEq",
        "colab_type": "code",
        "outputId": "41676802-8d99-4cdf-f3f5-d2ae16f6c8de",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 297
        }
      },
      "cell_type": "code",
      "source": [
        "#check how those changes have impacted the data\n",
        "raw_data.describe()"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>id</th>\n",
              "      <th>vendor_id</th>\n",
              "      <th>subscriber_id</th>\n",
              "      <th>merchant_id</th>\n",
              "      <th>signal_type</th>\n",
              "      <th>latitude</th>\n",
              "      <th>longitude</th>\n",
              "      <th>dongle_id</th>\n",
              "      <th>distance</th>\n",
              "      <th>rssi</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>1.252500e+05</td>\n",
              "      <td>125250.000000</td>\n",
              "      <td>1.252500e+05</td>\n",
              "      <td>125250.0</td>\n",
              "      <td>125250.000000</td>\n",
              "      <td>124279.000000</td>\n",
              "      <td>124279.000000</td>\n",
              "      <td>125250.0</td>\n",
              "      <td>89359.000000</td>\n",
              "      <td>89352.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>3.315534e+09</td>\n",
              "      <td>175.660072</td>\n",
              "      <td>2.201998e+07</td>\n",
              "      <td>2133.0</td>\n",
              "      <td>2.254930</td>\n",
              "      <td>40.993776</td>\n",
              "      <td>29.124155</td>\n",
              "      <td>5536.0</td>\n",
              "      <td>1744.333692</td>\n",
              "      <td>-86.252820</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>4.886681e+08</td>\n",
              "      <td>108.902448</td>\n",
              "      <td>8.206018e+06</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.666989</td>\n",
              "      <td>0.021561</td>\n",
              "      <td>0.030560</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1248.056995</td>\n",
              "      <td>6.332788</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>2.338573e+09</td>\n",
              "      <td>6.000000</td>\n",
              "      <td>2.699200e+04</td>\n",
              "      <td>2133.0</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>36.878855</td>\n",
              "      <td>27.535459</td>\n",
              "      <td>5536.0</td>\n",
              "      <td>0.027826</td>\n",
              "      <td>-116.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>2.884233e+09</td>\n",
              "      <td>110.000000</td>\n",
              "      <td>1.645927e+07</td>\n",
              "      <td>2133.0</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>40.992013</td>\n",
              "      <td>29.126663</td>\n",
              "      <td>5536.0</td>\n",
              "      <td>888.904863</td>\n",
              "      <td>-90.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>3.387717e+09</td>\n",
              "      <td>135.000000</td>\n",
              "      <td>2.362873e+07</td>\n",
              "      <td>2133.0</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>40.992139</td>\n",
              "      <td>29.126753</td>\n",
              "      <td>5536.0</td>\n",
              "      <td>1487.501330</td>\n",
              "      <td>-87.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>3.795225e+09</td>\n",
              "      <td>331.000000</td>\n",
              "      <td>2.879388e+07</td>\n",
              "      <td>2133.0</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>40.992211</td>\n",
              "      <td>29.126796</td>\n",
              "      <td>5536.0</td>\n",
              "      <td>2441.971093</td>\n",
              "      <td>-83.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>3.927653e+09</td>\n",
              "      <td>1055.000000</td>\n",
              "      <td>3.573413e+07</td>\n",
              "      <td>2133.0</td>\n",
              "      <td>4.000000</td>\n",
              "      <td>41.396288</td>\n",
              "      <td>32.435247</td>\n",
              "      <td>5536.0</td>\n",
              "      <td>94675.469987</td>\n",
              "      <td>-33.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                 id      vendor_id  subscriber_id  merchant_id    signal_type  \\\n",
              "count  1.252500e+05  125250.000000   1.252500e+05     125250.0  125250.000000   \n",
              "mean   3.315534e+09     175.660072   2.201998e+07       2133.0       2.254930   \n",
              "std    4.886681e+08     108.902448   8.206018e+06          0.0       0.666989   \n",
              "min    2.338573e+09       6.000000   2.699200e+04       2133.0       2.000000   \n",
              "25%    2.884233e+09     110.000000   1.645927e+07       2133.0       2.000000   \n",
              "50%    3.387717e+09     135.000000   2.362873e+07       2133.0       2.000000   \n",
              "75%    3.795225e+09     331.000000   2.879388e+07       2133.0       2.000000   \n",
              "max    3.927653e+09    1055.000000   3.573413e+07       2133.0       4.000000   \n",
              "\n",
              "            latitude      longitude  dongle_id      distance          rssi  \n",
              "count  124279.000000  124279.000000   125250.0  89359.000000  89352.000000  \n",
              "mean       40.993776      29.124155     5536.0   1744.333692    -86.252820  \n",
              "std         0.021561       0.030560        0.0   1248.056995      6.332788  \n",
              "min        36.878855      27.535459     5536.0      0.027826   -116.000000  \n",
              "25%        40.992013      29.126663     5536.0    888.904863    -90.000000  \n",
              "50%        40.992139      29.126753     5536.0   1487.501330    -87.000000  \n",
              "75%        40.992211      29.126796     5536.0   2441.971093    -83.000000  \n",
              "max        41.396288      32.435247     5536.0  94675.469987    -33.000000  "
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 5
        }
      ]
    },
    {
      "metadata": {
        "id": "agrzjmT9V15T",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "Now we've tidied up the data a little, we will start aggregating the data, getting some summary information at a daily level\n"
      ]
    },
    {
      "metadata": {
        "id": "B5_60U3NV0dJ",
        "colab_type": "code",
        "outputId": "883b19fa-591c-4cc9-eeef-749979c4add8",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 493
        }
      },
      "cell_type": "code",
      "source": [
        "#convert entry date to datetime format\n",
        "raw_data['entry_date'] = pd.to_datetime(raw_data['entry_date'])\n",
        "\n",
        "#create new column that just has date, not time\n",
        "raw_data['date_minus_time'] = raw_data[\"entry_date\"].apply( lambda df : \n",
        "datetime.datetime(year=df.year, month=df.month, day=df.day))\t\n",
        "\n",
        "#calculate number of unique subscriber_id's for each date \n",
        "visit_count = raw_data.groupby(['date_minus_time'])['subscriber_id'].nunique()\n",
        "#calculate number of unique exit stamps (could be sales) per day    \n",
        "exit_count = raw_data.groupby(['date_minus_time'])['exit_date'].nunique()\n",
        "#calculate means for distance and rssi by day\n",
        "avg_dist = raw_data.groupby(['date_minus_time'])['distance'].mean()\n",
        "avg_rssi = raw_data.groupby(['date_minus_time'])['rssi'].mean()\n",
        "\n",
        "print(visit_count.head())\n",
        "print(exit_count.head())\n",
        "print(avg_dist.head())\n",
        "print(avg_rssi.head())"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "date_minus_time\n",
            "2017-05-01    100\n",
            "2017-05-02    112\n",
            "2017-05-03    112\n",
            "2017-05-04    100\n",
            "2017-05-05    108\n",
            "Name: subscriber_id, dtype: int64\n",
            "date_minus_time\n",
            "2017-05-01    102\n",
            "2017-05-02    147\n",
            "2017-05-03    146\n",
            "2017-05-04    105\n",
            "2017-05-05    161\n",
            "Name: exit_date, dtype: int64\n",
            "date_minus_time\n",
            "2017-05-01    1569.171447\n",
            "2017-05-02    1764.830439\n",
            "2017-05-03    1649.324647\n",
            "2017-05-04    2297.451138\n",
            "2017-05-05    1545.418285\n",
            "Name: distance, dtype: float64\n",
            "date_minus_time\n",
            "2017-05-01   -84.957006\n",
            "2017-05-02   -86.655592\n",
            "2017-05-03   -86.161916\n",
            "2017-05-04   -85.913717\n",
            "2017-05-05   -87.802835\n",
            "Name: rssi, dtype: float64\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "ak96pi2mWS07",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "Next lets try and understand how long on average a customer spends in the shop each day"
      ]
    },
    {
      "metadata": {
        "id": "PS1EDlBQWRQT",
        "colab_type": "code",
        "outputId": "ee5e87fd-48e4-48b9-eaf6-756379b7c1d6",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 136
        }
      },
      "cell_type": "code",
      "source": [
        "#create unique subscriber/date column\n",
        "raw_data['unique_user_date'] = raw_data['subscriber_id'].map(str) + raw_data['date_minus_time'].map(str) \n",
        "\n",
        "#calculate time difference between subsequent entries for a user on a given day\n",
        "raw_data['diff'] = raw_data.sort_values(['unique_user_date', 'entry_date']).groupby('unique_user_date')['entry_date'].diff()\n",
        "\n",
        "#calculate total stay duration per user per day - checked a few rows against the raw data and it seems to have worked\n",
        "duration_calcs = raw_data.groupby(['unique_user_date', 'date_minus_time'])['diff'].sum()\n",
        "#expand the output back in to a dataframe for further aggregation\n",
        "duration_calcs = pd.DataFrame(duration_calcs).reset_index()\n",
        "#convert the time delta in to a numeric value (number of seconds) to enable mean calculation\n",
        "duration_calcs['diff_new'] = duration_calcs['diff'].dt.total_seconds()\n",
        "#calculate average stay duration per day\n",
        "avg_stay = duration_calcs.groupby(['date_minus_time'])['diff_new'].mean()\n",
        "\n",
        "print(avg_stay.head())"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "date_minus_time\n",
            "2017-05-01    1419.360000\n",
            "2017-05-02    2984.330357\n",
            "2017-05-03    3952.464286\n",
            "2017-05-04    2667.450000\n",
            "2017-05-05    3795.583333\n",
            "Name: diff_new, dtype: float64\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "nmwa0n9lXGxv",
        "colab_type": "code",
        "outputId": "3dbf2e1f-554c-4115-d9bc-2f864e088b99",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 235
        }
      },
      "cell_type": "code",
      "source": [
        "#combine visits, exits, stay length, distance and rssi in to one data frame\n",
        "model_data = pd.concat((visit_count.rename('Visits'),\n",
        "                        exit_count.rename('Exits'),\n",
        "                        avg_stay.rename('Avg_Stay'),\n",
        "                        avg_dist.rename('Avg_Dist'),\n",
        "                        avg_rssi.rename('Avg_rssi')), axis = 1)\n",
        "\n",
        "model_data.head()"
      ],
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
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              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Visits</th>\n",
              "      <th>Exits</th>\n",
              "      <th>Avg_Stay</th>\n",
              "      <th>Avg_Dist</th>\n",
              "      <th>Avg_rssi</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>date_minus_time</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2017-05-01</th>\n",
              "      <td>100</td>\n",
              "      <td>102</td>\n",
              "      <td>1419.360000</td>\n",
              "      <td>1569.171447</td>\n",
              "      <td>-84.957006</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2017-05-02</th>\n",
              "      <td>112</td>\n",
              "      <td>147</td>\n",
              "      <td>2984.330357</td>\n",
              "      <td>1764.830439</td>\n",
              "      <td>-86.655592</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2017-05-03</th>\n",
              "      <td>112</td>\n",
              "      <td>146</td>\n",
              "      <td>3952.464286</td>\n",
              "      <td>1649.324647</td>\n",
              "      <td>-86.161916</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2017-05-04</th>\n",
              "      <td>100</td>\n",
              "      <td>105</td>\n",
              "      <td>2667.450000</td>\n",
              "      <td>2297.451138</td>\n",
              "      <td>-85.913717</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2017-05-05</th>\n",
              "      <td>108</td>\n",
              "      <td>161</td>\n",
              "      <td>3795.583333</td>\n",
              "      <td>1545.418285</td>\n",
              "      <td>-87.802835</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                 Visits  Exits     Avg_Stay     Avg_Dist   Avg_rssi\n",
              "date_minus_time                                                    \n",
              "2017-05-01          100    102  1419.360000  1569.171447 -84.957006\n",
              "2017-05-02          112    147  2984.330357  1764.830439 -86.655592\n",
              "2017-05-03          112    146  3952.464286  1649.324647 -86.161916\n",
              "2017-05-04          100    105  2667.450000  2297.451138 -85.913717\n",
              "2017-05-05          108    161  3795.583333  1545.418285 -87.802835"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 8
        }
      ]
    },
    {
      "metadata": {
        "id": "NVPRLDIoXYa5",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "##Data augmentation"
      ]
    },
    {
      "metadata": {
        "id": "UPwpGaZsXayy",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "It's possible that there is other information that may help us in predicting visits and exits. Weather and economic data is freely available online.\n",
        "\n",
        "For ease of sharing with the group, I have saved this data in a public [github](https://github.com/tbowling87/RetailExercise) and will load this in below. The github also has the python script that was used to gather the weather data using the worldweatheronline.com APIs. The stock market data was obtained from https://www.investing.com/indices/ise-100-historical-data\n",
        " and I have filled any dates that were not in the table with the previous dates value (starting 28th April to get value for 1st May)\n"
      ]
    },
    {
      "metadata": {
        "id": "upyfrcvJY9Mj",
        "colab_type": "code",
        "outputId": "92c7bf9e-44d4-4c6c-dde0-aecce33b07d6",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        }
      },
      "cell_type": "code",
      "source": [
        "#read in csv created using weather API script\n",
        "weather_data_url = 'https://raw.githubusercontent.com/tbowling87/RetailExercise/master/weather_data.csv'\n",
        "weather_data = pd.read_csv(weather_data_url)\n",
        "\n",
        "#convert the date column to datetime format and set the date as the index\n",
        "weather_data['date'] = pd.to_datetime(weather_data['date'])\n",
        "weather_data = weather_data.set_index('date')\n",
        "#combine with the model data\n",
        "model_data = pd.concat((model_data, weather_data), axis =1)\n",
        "\n",
        "model_data.head()"
      ],
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Visits</th>\n",
              "      <th>Exits</th>\n",
              "      <th>Avg_Stay</th>\n",
              "      <th>Avg_Dist</th>\n",
              "      <th>Avg_rssi</th>\n",
              "      <th>Max_Temp</th>\n",
              "      <th>Min_Temp</th>\n",
              "      <th>Precipitation</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2017-05-01</th>\n",
              "      <td>100</td>\n",
              "      <td>102</td>\n",
              "      <td>1419.360000</td>\n",
              "      <td>1569.171447</td>\n",
              "      <td>-84.957006</td>\n",
              "      <td>21</td>\n",
              "      <td>17</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2017-05-02</th>\n",
              "      <td>112</td>\n",
              "      <td>147</td>\n",
              "      <td>2984.330357</td>\n",
              "      <td>1764.830439</td>\n",
              "      <td>-86.655592</td>\n",
              "      <td>21</td>\n",
              "      <td>19</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2017-05-03</th>\n",
              "      <td>112</td>\n",
              "      <td>146</td>\n",
              "      <td>3952.464286</td>\n",
              "      <td>1649.324647</td>\n",
              "      <td>-86.161916</td>\n",
              "      <td>24</td>\n",
              "      <td>20</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2017-05-04</th>\n",
              "      <td>100</td>\n",
              "      <td>105</td>\n",
              "      <td>2667.450000</td>\n",
              "      <td>2297.451138</td>\n",
              "      <td>-85.913717</td>\n",
              "      <td>24</td>\n",
              "      <td>21</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2017-05-05</th>\n",
              "      <td>108</td>\n",
              "      <td>161</td>\n",
              "      <td>3795.583333</td>\n",
              "      <td>1545.418285</td>\n",
              "      <td>-87.802835</td>\n",
              "      <td>26</td>\n",
              "      <td>22</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "            Visits  Exits     Avg_Stay     Avg_Dist   Avg_rssi  Max_Temp  \\\n",
              "2017-05-01     100    102  1419.360000  1569.171447 -84.957006        21   \n",
              "2017-05-02     112    147  2984.330357  1764.830439 -86.655592        21   \n",
              "2017-05-03     112    146  3952.464286  1649.324647 -86.161916        24   \n",
              "2017-05-04     100    105  2667.450000  2297.451138 -85.913717        24   \n",
              "2017-05-05     108    161  3795.583333  1545.418285 -87.802835        26   \n",
              "\n",
              "            Min_Temp  Precipitation  \n",
              "2017-05-01        17            0.0  \n",
              "2017-05-02        19            0.0  \n",
              "2017-05-03        20            0.0  \n",
              "2017-05-04        21            0.0  \n",
              "2017-05-05        22            0.0  "
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 9
        }
      ]
    },
    {
      "metadata": {
        "id": "fO8wvKpndjoF",
        "colab_type": "code",
        "outputId": "2e4f545f-e1a9-476b-c5f1-0e210b1fc5eb",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 235
        }
      },
      "cell_type": "code",
      "source": [
        "stock_data_url = 'https://raw.githubusercontent.com/tbowling87/RetailExercise/master/bist_100_price.csv'\n",
        "stock_data = pd.read_csv(stock_data_url)\n",
        "\n",
        "#perform similar steps as for weather data to combine with the model data\n",
        "stock_data['date'] = pd.to_datetime(stock_data['date'])\n",
        "stock_data['Price'] = pd.to_numeric(stock_data['Price'])\n",
        "stock_data = stock_data.set_index('date')\n",
        "model_data = pd.concat((model_data, stock_data), axis =1)\n",
        "model_data.head()"
      ],
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Visits</th>\n",
              "      <th>Exits</th>\n",
              "      <th>Avg_Stay</th>\n",
              "      <th>Avg_Dist</th>\n",
              "      <th>Avg_rssi</th>\n",
              "      <th>Max_Temp</th>\n",
              "      <th>Min_Temp</th>\n",
              "      <th>Precipitation</th>\n",
              "      <th>Price</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>date</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2017-05-01</th>\n",
              "      <td>100</td>\n",
              "      <td>102</td>\n",
              "      <td>1419.360000</td>\n",
              "      <td>1569.171447</td>\n",
              "      <td>-84.957006</td>\n",
              "      <td>21</td>\n",
              "      <td>17</td>\n",
              "      <td>0.0</td>\n",
              "      <td>94655.31</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2017-05-02</th>\n",
              "      <td>112</td>\n",
              "      <td>147</td>\n",
              "      <td>2984.330357</td>\n",
              "      <td>1764.830439</td>\n",
              "      <td>-86.655592</td>\n",
              "      <td>21</td>\n",
              "      <td>19</td>\n",
              "      <td>0.0</td>\n",
              "      <td>94355.91</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2017-05-03</th>\n",
              "      <td>112</td>\n",
              "      <td>146</td>\n",
              "      <td>3952.464286</td>\n",
              "      <td>1649.324647</td>\n",
              "      <td>-86.161916</td>\n",
              "      <td>24</td>\n",
              "      <td>20</td>\n",
              "      <td>0.0</td>\n",
              "      <td>93862.74</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2017-05-04</th>\n",
              "      <td>100</td>\n",
              "      <td>105</td>\n",
              "      <td>2667.450000</td>\n",
              "      <td>2297.451138</td>\n",
              "      <td>-85.913717</td>\n",
              "      <td>24</td>\n",
              "      <td>21</td>\n",
              "      <td>0.0</td>\n",
              "      <td>93038.49</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2017-05-05</th>\n",
              "      <td>108</td>\n",
              "      <td>161</td>\n",
              "      <td>3795.583333</td>\n",
              "      <td>1545.418285</td>\n",
              "      <td>-87.802835</td>\n",
              "      <td>26</td>\n",
              "      <td>22</td>\n",
              "      <td>0.0</td>\n",
              "      <td>93928.39</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "            Visits  Exits     Avg_Stay     Avg_Dist   Avg_rssi  Max_Temp  \\\n",
              "date                                                                       \n",
              "2017-05-01     100    102  1419.360000  1569.171447 -84.957006        21   \n",
              "2017-05-02     112    147  2984.330357  1764.830439 -86.655592        21   \n",
              "2017-05-03     112    146  3952.464286  1649.324647 -86.161916        24   \n",
              "2017-05-04     100    105  2667.450000  2297.451138 -85.913717        24   \n",
              "2017-05-05     108    161  3795.583333  1545.418285 -87.802835        26   \n",
              "\n",
              "            Min_Temp  Precipitation     Price  \n",
              "date                                           \n",
              "2017-05-01        17            0.0  94655.31  \n",
              "2017-05-02        19            0.0  94355.91  \n",
              "2017-05-03        20            0.0  93862.74  \n",
              "2017-05-04        21            0.0  93038.49  \n",
              "2017-05-05        22            0.0  93928.39  "
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 10
        }
      ]
    },
    {
      "metadata": {
        "id": "9DxjFBEMd9y2",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "###noticed something odd with the very last date in the dataset, recommend removing"
      ]
    },
    {
      "metadata": {
        "id": "-IX-BLMCdozD",
        "colab_type": "code",
        "outputId": "2e785fca-8f92-4a38-dc6a-0bf316c38cad",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 272
        }
      },
      "cell_type": "code",
      "source": [
        "print(model_data.tail())\n",
        "\n",
        "#drop last day as only one visit occurs\n",
        "model_data = model_data.drop(model_data.index[142])"
      ],
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "            Visits  Exits     Avg_Stay     Avg_Dist   Avg_rssi  Max_Temp  \\\n",
            "date                                                                       \n",
            "2017-09-16      89    106  2450.988764  1691.593434 -85.913420        28   \n",
            "2017-09-17     110    119  1996.081818  1753.105454 -85.996904        30   \n",
            "2017-09-18     107    135  2997.588785  1781.363043 -86.091967        31   \n",
            "2017-09-19     103    107  3511.349515  1876.940012 -86.093178        31   \n",
            "2017-09-20       1      0     0.000000          NaN        NaN        39   \n",
            "\n",
            "            Min_Temp  Precipitation      Price  \n",
            "date                                            \n",
            "2017-09-16        23            0.0  107741.70  \n",
            "2017-09-17        23            0.0  107741.70  \n",
            "2017-09-18        27            0.0  106534.60  \n",
            "2017-09-19        25            0.0  104917.77  \n",
            "2017-09-20        28            0.0  105323.67  \n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "PIcd1SeceoNX",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "It would probably be wise to standardise our features"
      ]
    },
    {
      "metadata": {
        "id": "RBTgsUrQenIq",
        "colab_type": "code",
        "outputId": "3a5ecdc8-1f20-4b71-90a8-f97275b95f7f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 105
        }
      },
      "cell_type": "code",
      "source": [
        "# import the appropriate library\n",
        "from sklearn.preprocessing import StandardScaler\n",
        "\n",
        "standardized = model_data\n",
        "\n",
        "# Create scaler\n",
        "scaler = StandardScaler()\n",
        "\n",
        "# transform the data using the fitted scaler - this applies the transform using the fit\n",
        "standardized = scaler.fit_transform(standardized)\n",
        "\n"
      ],
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/sklearn/preprocessing/data.py:625: DataConversionWarning: Data with input dtype int64, float64 were all converted to float64 by StandardScaler.\n",
            "  return self.partial_fit(X, y)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/base.py:462: DataConversionWarning: Data with input dtype int64, float64 were all converted to float64 by StandardScaler.\n",
            "  return self.fit(X, **fit_params).transform(X)\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "metadata": {
        "id": "c5YPZcmPiPFr",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "#ARIMA Modelling Section"
      ]
    },
    {
      "metadata": {
        "id": "RqmT_dNmiS5M",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "EHjw6avphWyn",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "# SVM\n"
      ]
    },
    {
      "metadata": {
        "id": "VO5Hlq_3hZ7F",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "#create x_train x_test, y_train and y_test - use last 31 records (a month) as training data\n",
        "svm_y = standardized[:,1]\n",
        "svm_y = svm_y.reshape(-1,1)\n",
        "svm_x = np.delete(standardized,[1], axis = 1)\n",
        "\n",
        "svm_y_train = svm_y[:111, :]\n",
        "svm_y_test = svm_y[111:, :]\n",
        "svm_X_train = svm_x[:111, :]\n",
        "svm_X_test = svm_x[111:, :]"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "_C5FeljLhl_H",
        "colab_type": "code",
        "outputId": "43e6d134-723e-4979-9df8-a09c80dffa0e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 122
        }
      },
      "cell_type": "code",
      "source": [
        "#fit vanilla SVR\n",
        "from sklearn.svm import LinearSVR\n",
        "\n",
        "svm_regr = LinearSVR()\n",
        "\n",
        "svm_regr.fit(svm_X_train, svm_y_train)"
      ],
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/sklearn/utils/validation.py:761: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  y = column_or_1d(y, warn=True)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "LinearSVR(C=1.0, dual=True, epsilon=0.0, fit_intercept=True,\n",
              "     intercept_scaling=1.0, loss='epsilon_insensitive', max_iter=1000,\n",
              "     random_state=None, tol=0.0001, verbose=0)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 14
        }
      ]
    },
    {
      "metadata": {
        "id": "APX4nyhRhrea",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "#make predictions\n",
        "svm_preds = svm_regr.predict(svm_X_test)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "Az4bexM_hvtK",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "# invert scaling for forecast to revert data into original scale\n",
        "inv_yhat = np.concatenate((svm_X_test[:,0].reshape(-1,1), svm_preds.reshape(-1,1), svm_X_test[:, 1:]), axis=1)\n",
        "inv_yhat = scaler.inverse_transform(inv_yhat)\n",
        "inv_yhat = inv_yhat[:,1]\n",
        "\n",
        "# invert scaling for actual\n",
        "inv_y = np.concatenate((svm_X_test[:,0].reshape(-1,1),svm_y_test, svm_X_test[:, 1:]), axis=1)\n",
        "inv_y = scaler.inverse_transform(inv_y)\n",
        "#Actual output\n",
        "inv_y = inv_y[:,1]"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "75TV_ol4h1M5",
        "colab_type": "code",
        "outputId": "a113561d-ddee-4b7b-f140-2c3117e8f80c",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "cell_type": "code",
      "source": [
        "# calculate RMSE\n",
        "import math\n",
        "from sklearn.metrics import mean_squared_error\n",
        "rmse = math.sqrt(mean_squared_error(inv_y, inv_yhat))\n",
        "print('Test RMSE: %.3f' % rmse)"
      ],
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Test RMSE: 15.615\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "HBE6-mnNh9ZY",
        "colab_type": "code",
        "outputId": "f17de1a7-c059-4683-acc0-253d641e5d77",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 376
        }
      },
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt\n",
        "#visualise the results\n",
        "plt.plot(inv_y, color='red', label='real exits')\n",
        "plt.plot(inv_yhat, color='blue', label='predicted exits')\n",
        "plt.title('Store Exits Prediction')\n",
        "plt.xlabel('Time')\n",
        "plt.ylabel('Exits')\n",
        "plt.legend()\n",
        "plt.show()"
      ],
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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1nzWfIKkqOePvKfJabYiT79xZcqKbA08nvPl8uByA7MlTin3dGSffu6emRCo3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\npkEuIiLiYhrkIiIiLvYHL5mPdrgAgZwAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 576x396 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "wEv6NTCo6HDr",
        "colab_type": "code",
        "outputId": "170d348b-e7f9-4dd4-9a81-78882d109dec",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 477
        }
      },
      "cell_type": "code",
      "source": [
        "from sklearn.svm import SVR\n",
        "\n",
        "svm_regr2 = SVR()\n",
        "\n",
        "svm_regr2.fit(svm_X_train, svm_y_train)\n",
        "\n",
        "svm_preds2 = svm_regr2.predict(svm_X_test)\n",
        "\n",
        "# invert scaling for forecast to revert data into original form\n",
        "inv_yhat2 = np.concatenate((svm_X_test[:,0].reshape(-1,1), svm_preds2.reshape(-1,1), svm_X_test[:, 1:]), axis=1)\n",
        "inv_yhat2 = scaler.inverse_transform(inv_yhat2)\n",
        "inv_yhat2 = inv_yhat2[:,1]\n",
        "\n",
        "\n",
        "# calculate RMSE\n",
        "rmse2 = math.sqrt(mean_squared_error(inv_y, inv_yhat2))\n",
        "print('Test RMSE: %.3f' % rmse2)\n",
        "\n",
        "\n",
        "#visualise the results\n",
        "plt.plot(inv_y, color='red', label='real exits')\n",
        "plt.plot(inv_yhat2, color='blue', label='predicted exits')\n",
        "plt.title('Store Exits Prediction')\n",
        "plt.xlabel('Time')\n",
        "plt.ylabel('Exits')\n",
        "plt.legend()\n",
        "plt.show()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/sklearn/utils/validation.py:761: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  y = column_or_1d(y, warn=True)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/svm/base.py:196: FutureWarning: The default value of gamma will change from 'auto' to 'scale' in version 0.22 to account better for unscaled features. Set gamma explicitly to 'auto' or 'scale' to avoid this warning.\n",
            "  \"avoid this warning.\", FutureWarning)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Test RMSE: 18.506\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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bel0GUC8LwwghFwBGwYs9dMdkMqJIj5i7Gk/wqurlmQkETpKbS+N/jMPvg/96\n9LZycrJRtzw8otqxdqvdVJeV1HTdiFb39aVw+Mjyc6pHueQbNphISZEZPdqKj49xrHVr4zvHmylo\nUmYGlrXfY2vfAVuPXhWOUdt3QPdvJCxyQf2lMPEch4mlZ08Nf384qrUzSjvWsWUhELhLwL+nY/lx\nLb4LF3j0vnJSIno1Vd3seMO1bjqwH+XoEQqvGV4q0tpOfcolX7rU7lYv3q9v29awyOPivBe57vPd\naqT8fCPITapkHiYT1h49MR05jJSdVbcTrAYh5ALQdQ4nBqGi0LWrSvv2cDSnOTqIgDdBg8Dn21X4\nfT4fANPxY1BQ4Jkb6zqm5GRUJyLWAVQvdECzFEWr599U3q0O9SeXPDcXvv1WoWVLjT59ij19dte6\nNwPeHJ3Obh5X5Thbz95Iuo6yZ3ddTMtphJALkNIvsMfaCYAuXTRiYiDHZiGRpiIFTVDvkRPiCXzs\nAaOIx8CrkFQV09EjHrm3lJ2FlJtTZfvSkuhhYeiyXHcWua7ju2Ipur8/hdcMr3CI2ryl0VPbyxb5\nunUKOTkSN99sRS6hPK1aeVfI5fizmP/YgLVvf7RWrasc6ygMs7N+udeFkAtKpZ517qwSE2McP0KM\nKAojqN+oKoH334Wcnk72f152VDRTDu73yO2rquq2c6fM0aNlDppMaOERdWaRK3t2YToZR8G1I8Df\nv+JBFgtaVFOvu9aLo9VLp8E1agRRURonT3pHjizLliDpOvllc8crwBG5vlsIuaCeUTJivVMnrbSQ\nizKtgnqM/ztv4rNpIwWjbiD/timonboAoBw84JH7V1bVraAA/v53fyZPLn+NFhllNE6pg+6BlhX2\nIjBjqhyntYw20km9lEt+7pzEzz+b6NpVJTa2fNW7Nm00zp6VPLYj4jS6ju+Sr9F9fCi44aZqh2ut\nWqMFB2MWFrmgviElJbGbHrQKzSQwEIeQH6UDJmGRC+opyrYt+L/2Emqz5mS9+Q5IEraOxhaRyWMW\necVV3Q4ckMnNldi9u3xihxYZiZSfj5SZ4ZE5VIquY1m1HC0gkMKhw6oc6sgl99JW2apVRqezMWOs\nFZ5v00ZD06Q6T0Ez7d+HcvCAESgYFFz9BZKErUcvTKdOIp0/V/sTdBIh5ALOHcskjXC6tMkGioX8\niCyKwgjqJ1JmBo3v+ydoGlnvfeyoLa6HhKJGRqF4qN1kZVXdduwwAUa1ztOnS0c511VfcuWvbZjO\nnKZwxHXg61vlWDXau5HrS5aYkSSdm2+u2CPQtq29eUrdRq47gtyccKvbsdbDBipCyAXsO2wkdHbp\nYri8QkMhOFjniKkTpngRtS7qBuqMAAAgAElEQVSoZ+g6AU89hun0KXIfeRzrgIGlTqudOmOKP4uU\nkV7jl6qsqtuuXSbHz0eOlP4a1SLqpkyrPVq9oJJo9VJzamkUhfFGwNupUxLbtpkYOFCladOKtxu8\nErmuqliWLUYLCqLwmmudvszWs6gwzG4h5IJ6xP7TjQHodJnFcaxtW43jtlZoaRfweu1EgaAElkX/\nw3fZYqyXXUHuE8+WO28r2ic3Hay5VS6nVGyR79xZ/NV5+LCp1Lk6ySXXNCyrVqA1CaLw6qHVDnfk\nknshBc3e6cxekrUiWreueyE3b/wdU3ISBTfcDBZL9RcUUR8j14WQC9iXYnzxdL7Cz3GsbVsNq27m\nFK0wJQr3uqB+IJ84TsAzT6AFBJL5wTwwm8uNsXXqDHgmcl1OSipX1S0zE44elWnRwhCfw4fLWORF\nol+brnVl6xZMiQkUXHc9jhJpVeAtIbd3OrNYdEaNqjzQzhsWucOtXklJ1srQopoa2zf1qMKbEHIB\n+7Ja4y/l0qp1sdurXTvjgyVS0AT1hsJCGt97J3JONtlz3qo051ftXBS5fqjmketychJ6aFipB4bd\nu03ousQNN9iwWCpwrddBBzTflZXXVq8IrXkLr+SS790rc/So0emscePKxwUEQHi4VndCnpODz3er\nUaNbYevT1+XLbb16Y0pKdGQ1eBsh5Jc4hbk2Dto60MU/DlMJD2EpIRcBb4J6QKNXX8S8ayf542+l\nYMz4SsfZOsSiyzImD6SgyUlJ5SLWd+40PiiXXabSsaNhnWslMqqKG6fUkpCrKj6rV6KFhGAdNNi5\naywWtKbN6jzYrbLc8Ypo00bjzBkJa+UeeI9hWfs9ck42+WPHU6o6jZPYetYv93r1xYMFFzXHt2dg\nJZhuYQlAtOO4vf7xEWIwiTKtAi+hqpCVBXk/bUF/9ycuNB1H4vB3yVymkJUlkZkpkZ1NqZ/HjVOY\n1KatkUuu65XXzq4GKTsLOScba1RZITe++Hv3VunSBXbvljhzRqJVK8OjZXfD15Zr3fznH5hSksmb\nPKXCrYXK0FpGo2zbAlarS9e5i73TWVCQztChzgi5ztatxlrao9hrC0uRW71gjGtudTslC8MUjhzl\nsXm5ixDyS5yDW41Ati4tLpQ6bt+zOkoH5IRtdT4vwaXL7t0y773nw48/KmRn20X4GmA3JAJTq77+\n2DGZWzt1Qfl2JXJiAlqz5m7No7Ic8p07TUREaDRrptPZ2I7n8GGZVq2KEsp9fNBCQ2vNte4oAuNE\ntHpJ1JbRmLf8iZwQX20pUk+wcaOJ5GSZyZMLndnGL9E8RaZt29rruiilpODz689Ye/ZC7RDj1j1s\nPY0OafWlMIwQ8kucA/uM/3fuULqkUkAANI1SOZIkLHJB7aNp8NNPJt57z4c//jC+llq31ujRXSX4\n6DaCU4/id3lH/Af2ICAAAgN1GjfWi/4PAQHG748+6suGDQoJ11xBG1aiHNxPodtCXlSetYRFnpws\nkZAgM2KEFUmihJCbuPbaYvHRIqJqp+GQzYblu5VoYeHl0u6qo2QueV0Iub3T2bhxzlWTKx3wVntC\n7rtyKZKqGp3O3EQPCUWNbm0EvNXA6+MpalXIjxw5wv33388dd9zBpEmTSExM5Nlnn8Vms6EoCnPm\nzCE8PJxVq1axYMECZFlm/PjxjBtXdQcagefYf8yIVO/UrfwbsV17nT+Soik4k1rX0xJcLOTlGfnO\nuo7asRO2mI5Gce0iCgpg6VKF99/3caRxDR5s4/77C7n6ahW/+R8R+OwTFA66mozFK0AurPLlBg5U\n2bBBYaM0iDYUpaANdT5HuCSO8qwRxUJud6v37GmIThcjrq7CgDfl4H6j5VdlNdDdwLzhN+Rz58ib\n8k9KBbU4QV3mkuflGZ3OWrQo3emsKuoqct2y+Gt0k4n8m8bW6D7WXr3xXbkM+dRJtNZtPDQ796g1\nIc/NzWX27Nn079/fceztt99m/PjxXHfddXz55Zd8+umnPPDAA8ydO5clS5ZgNpsZO3Ysw4YNIygo\nqLamJijB/vhgWnKaJu1CKRtj0ratxsaNCifjfYmqB0+dggZEYSG+X32B/1tzMCUmOA7rkoTWqjUp\nbfvwYcHtfLB3MMmZfiiKzrhxVu67r5CuXY0vdNOB/QTM+hdaSAhZcz90Kiipf39DNP5I7chkapaC\nVmyRF+eQ2wPdevUyXqdtW/Dx0cunoJXIJdfatHV7DmUpLgJTdW31iqjLFLR164xtkTvvLHQ6lqwu\ncslNR49g3rWTgmuuRQ8Pr9G9bD17w8plmHftoOBiFXIfHx8+/vhjPv74Y8ex5557DktR4n1wcDD7\n9+9n9+7ddOvWjcDAQAB69+7Njh07GDJkSG1NTVBEWppEUk4Trud31KiW5c7b96yO5regafoFRxlM\ngaBSVBXLkm9oNOcVTKdPovv5kfvAI6ht2mI6uJ8zuy4wd98Q5p/8BzkE0JgMnuQ1HuR9mu5rhO2/\nnVA7dsbWsTONXnoeqaCAzE8+L1eQpTJ69lSxWHQ2HQhB9/OrUeR6ccOU4qpu9tKsPXsaQq4o0L69\nxpEjRuS6XbTsQm7ypJAXFmL5bjVqVFOsfftXP74MdSnkS5ca0uJMtLqdJk0gNLR2U9AsS4qC3FzM\nHa8IW4lSre48WHmSWhNyRVFQlNK39y9yMamqyldffcW0adNIS0sjJKRYIEJCQkhNFa7cuuDAAeMD\n0509aJFXlDtfMgXt6vh4VCHkgsrQNHy+W0WjV19EOXIY3ceH3H/eQ+7DT6BHRrJzp8x7v/uweoeC\npkk0j7Ly7PD9TGm5juC4/SiHwjAdPFjOgs678y4Kh490ehq+vkZa2J9/mjjX5XJCj241On4prn/V\nlQ1203WjNGvbthrBJfprxMZqHDhg4uxZiejoosj1Wsgl9/ntZ+SMdHJvudWtlCmteQujV3otu9bP\nn4f16xW6dFHp2LF8p7OqaN1aZ/du2d1/sqrRNHyXLkZrFEDBiJpHmtu690CXpHpRGKbOg91UVeWp\np56iX79+9O/fn9WrV5c6rzvR+i842B9FcW1/qDrCwwM9er+GwOmiz3N3v2OEty4dmRseHsgVRdp+\nhBhCss/BJbhGdi7F90dllFoLXYcffoAZM2DnTmPfdupUpJkz8W/VCh8bjBkDq1YZw3v2hCeegPHj\nzZjNXYAuxffSNDh1Cvbtg717IScHvxkz8PPzwxWGDoVNm2BL5N8ZtW8D4RnJ0LGj63/oecOgCO3S\nHnx8OHoUMjJg1Cip1Br06mVm+XJITg7gssuKDnYwXK2Nc9I997lZY3xX+t8xGX9379m8OT7xZ2r1\n/fzLL4HYbHDHHSaXX6dTJ/jrL8jLC6St53YkDDZuhNOn4PbbCW8VWf346ggPhI4d8dmzi/AQ/wpj\nFurqe6POhfzZZ5+lVatWPPDAAwBERESQlpbmOJ+SkkLPnj2rvMeFC7kenVN4eCCpqVkevWdDYOtW\nX8BM58iUUn+/fT0CAsAkN+KIFkPWga3k97v01ggu3fdHRZRcC/MfG2j08mzMWzcDkH/zWHKffBa1\nXQdjcGoW69ebWLXKn169VP71rwIGDVKRJEivrJ9JQBj0u9r4DyDbBtmurX337ibAn/X5/RgFZPyx\nlcJQ1yPXg8/GI4eGci6jAChg/XoF8KNz53xSU42IkvDwQFq0yAP82Lo1nyuuMI4rvk0IBnKPnyLH\nE++d/HxCl69Ab9GS8+26gJv3bNK8Jeatm0mLP+dUaVdXCQ8P5NNPbUiSiWuvzSE11bV88GbNfAAL\n27fnEhjo2cj1gI/m4wekX38zVg99ngO79cT34EHO/7kDNbb0w6Knvzeqeiio08puq1atwmw289BD\nDzmO9ejRg71795KZmUlOTg47duzg8ssvr8tpXbIc2AcW8mnXsqDC8z4+0CoyzygKEy/KtAoMlB3b\naTL2RoL+Pgrz1s0UjLiO879sIuuD+cUiXsSiRUYK0iuv5HPVVWqdxEtefrmKouj8kRJrzPeAewFv\nRlW3ygPd7MTG2muuF1tknnat+/y8Hjk7i4Ib/l6joFOtZTSSptVatcaTJ2HrVoUrr6y801lV1Fbk\nupR+Ad+l36A2b4F14FUeu29xS1PvutdrzSLft28fr776KvHx8SiKwtq1azl37hwWi4XJkycD0K5d\nO2bNmsXjjz/O1KlTkSSJadOmOQLfBLWHzQaHj5joym7kZpW7mdq201ifGEFm3Hk8u5khaGjIyUnw\nz0kEF/nJCwf/jZxnZ2LrXfGDd0YG/PCDQocOqiNdqy5o1Ah69NDYuTuYHPyNCm+ukp2NnJ2FrUSg\n286dJhRFd0TV22nTRsNs1kuloHm6A5rFXlvdxSIwZXEEvJ05XSspU199Zfy/qk5nJTEdPICUkYGt\nnxG8V1tC7vv5Z0i5ueQ9Od3ltL2qsJdqNe/aQcEtEz12X1epNSHv2rUrX3zxhVNjR4wYwYgRI2pr\nKoIKOH5cpqBQNgLdqogIbtfJzPqNEBdnon0dzk9Q//B/41VYtQrrFX3Jmf5vrFcOqnL86tVmCgok\nxo+31XnmYr9+Kn/9ZWJT4LUMObjb5etNRXXS1aLPhtVqNADp1Emj7Ja9PXL98GG5uDaIvz9aYGPk\nFA+UabXZ8PlxHWp0a2w9etXoVlq0kUtuOnO6XLppTdF1WLgQLBad6693Llo98MF7UQ7u5/zW3WjN\nWziE/ORJDwq51YrfvA/RGgWQP+k2z90XsHXphq4oXrfIRdOUS5SSEetqmVrSJWlbpN7HEhtVOkZw\naWBPW8r4Zlm1Ig6waJGCJOmMGVMHXTDKMGCAISS/Bd2AfOok5OS4dL0jh7zIsj54UKagQCrnVrcT\nE6ORkyMRH1/8xKJFRnrEIlf270XOzqLwqsE1ruVgt8jl0ydrPK+y7Nsnc/AgDBtWdaczB7qOcuwI\nktWK33vvABAcDEFBOnFxnnvys6xajikxgfyJk9CbeLg+ia8vtk5dUPbthcKqixXVJkLIL1FKpZ5F\nNat0nCOX/EK40QVBcMkiJydDo0boAdVvfZ06JbF5s8LAgSotWtRuA4yK6NNHRZJ0NtgGIOk6ypFD\nLl1fnENuCLk9f7x374o/A/Z98rLudfn8+Rp/wZs3bwJwK3e8LMW55J5PQXOl0xmAlJqKlGsELvst\nXIBUlHbcpo3GyZOyZ75udB2/D+aiSxJ5d93ngRuWx9azN1JBgUfa5rqLEPJLlAMHjC8mQ8grt8jt\nueRH9fa12l9ZUP+Rk5OgqXOFWexf6uPG1b01DkZxkS5dNLamtqMAH5f3ycvmkO/aVbo0a1nsQn7o\nUEkhLwp4q6F73fxnkZD3v7JG94HiXHJPtzNVVVi2TCEoCKc6nQGYTsYZ10ZEIuXl4f/hXMAQ8sJC\niYSEmlvl5i1/Yt69k8LrRtdaGdWShWG8hRDyS5T9+2Wa+p4njHNoTSu3yJs10/E1FRp9yUXk+qWL\nzYZ0Ls0pIdd1I1rdz8/5vdLaoH9/lXybwjauwORiqVY5qUjIix5yd+404e+vOwS7LDExFVjkER4I\neNN1zFs2oTZvgdYyuvrx1WE2ozVr7vGiMBs2GJ3Oxo+HouKd1WI6ZQh53gMPo0ZE4jv/Y6T0Cx4N\nePN7/78A5N77QI3vVRmOlqZe3CcXQn4JcuECJCTIdPM9YtS+LuqfXBGyDO3CMwwhPyuE/FJFTk1B\n0nWnhHz7dpm4OJnrrrMREFAHk6uEfv0M3+zvXIVy8KBL15a0yLOzDUu7Rw+10oDntm01FEUvk4Jm\nF3L3LXLT0SPI584ZbnUPRQyqLaORExM8uqf72WeGB+aOO5y/xnTqJAC2mI7k3fcgcnYWfvM+8piQ\nyyeO47PmO6y9emPr07dG96oKNbYjuq+vV1uaCiG/BDl4sMitru9GDwsHs7nK8e1a5JNNIKmHM+pi\neoJ6iMOqdELI7bnj48d7x61uxy7kv/kOd7l5ikPIIyLZs8eErkv06lV5Cp3ZbGxD2SPXodiar4lF\nbv7zD8AzbnU7WstoJF33mIft7FmJNWsUevRQ6dfP+escrvXWbci7/U604GD8PnqPNlFGYGJNhdzv\nkw+QdJ28ex+o3YZPZjO2rt0xHTpgdLvzAkLIL0HsgW498ragVuFWt9Oug/HNdOKwd7+YBd7D7mqu\nTsgLCmDFCjORkRpXXeXd4MjwcJ2YGJVN1ivQUs8hlaggWR1ychJaSAhYLI7WpZVFrNuJjdXIzpZI\nTDREwxO55I5At34D3L5HWUrmknuCBQvMaJrE1KmFLuml6dRJdFlGa9ESAgLIu+s+5AsX6LTZSFuu\nSeS6lH4Bv68WojZvQcH1N7p9H2ex9uqNpKpG9LoXEEJ+CeIQ8sJtVQa62WnTzWh2c/yk50s6ChoG\nDjFqVvWD37p1ChkZEmPH2jxZd8Nt+vVTyVb92UVPl6zyklXdKqvoVhb7Prk94M0h5DUIdjNv3oQW\nGooaE+v2Pcqilsglryn5+bBwoZmQEI0bb3QtHkI+GWeIeFGp2Lx/3oMWEEjLBa8SGKjVKJfc94sF\nSLk55E29p1qPoydwFIbZ7R33uhDyS5D9+02YFY2OHKoy9cxO2+5GBYxjKc4khwouRpx1rS9ebNSY\n8rZb3Y69P7mxT+6kkOfkIGdlOqLOd+40ERam0bJl1Wl0ZVPQalqmVT5zGlP8Wax9PLc/DjiC5jyR\nS75ypcK5czL/+Ie1XKGcKsnNxZSchNqqteOQHhRM/p13oaQm065xKnFxRmtYlykqAKP7NyJ/8u1u\n3MB1bL2MbjnKzh2oKpw5I5GSUicvDQghv+RQVcNiiG2ehRmbUxZ527bGF9ix9MqD4gQXN46ArSqE\nPC1NYv16hW7dVDp1qruSrFXh2CdnMKZDzgW8OfbHo5qSmipx5oxMr15atVpaXHPd+FrVAxuj+/m5\nHexWvD/uObc6lLDIPZBLPn++D7Ksc/vtrj242b0BJYUcIPeeaeh+fnQ4v5X8fImkJNcfYCyrV2BK\niCfvH5M9XwCmiNxco0jQDz8ovP++mSc/7sII0zq6r3yZ6OgALrssgI4dce9BxA3qvPuZwLucPCmR\nlyfRNSIJTlFleVY7oaE6QUoWR62tIS8P1x69BRcDckoJi7wSD/OKFQo2m1RvrHGA5s11oqNVNpwe\nhHzgRaeuMaUUV3Urzh+vfr+/XOS6JKFFuF/dzbE/7sFANwCtWXN0k6nGrvUdO2R27jQxYoTV0Yfd\nWUoGupVEDw8nb9LtxHy8GxhNXJxMs2YuxFroOn4f/NcoAPPPe12aUyW34+efTezcaeLkSZmTJyVO\nnpRJSanIBh5GmJpK9x6FtGonM3Kk2Z228W4hhPwSw14Ipmtjo9ym5kQUsiRB+yYp7D7XHs7sg5gO\n1V4juLiQk5PQLRak4GBIy65wzKJFZkwmnb//3Xu54xXRv7/GN6dDOHxAoqmmUd23q1141aioaiu6\nlcTHxxDzI0eKa65rkVEo27carjAXgwbMmzehNQrA1qWbS9dVi6J4JJd83jxjb3vqVNcf3Ow55FoZ\nixwgb9rDtJs/B1SIOw5XuvAco2zZjHnXTgquG43WpuYNzb/6ysyjj/o6fpdlnRYtdK66ykarVhqt\nW+u0bq3RurVG529mE/XRq6Q/9y3WgVcRHm6mqFhdrSNc65cY+/cXlWZVjEpXaqRzlbraR2ZixYf4\n3RdqbW6C+oucnIwWEVnpXu3hwzK7dpkYMkQlIqLuS7JWhb3u+ob8y5GL6sVXhaM8a0SUI9DN2e5t\nMTEamZnFLmEtMspoG5rm2je6lJKCcuyokf+seN7eUltGG39nQcUtjKsjNVVi5UqF9u1Vt7IT5KIc\n8rKudTA8BtFDDRE+vf64S/f1/8BzBWCSkiSee85CQIDOwoW5bN6czZkz2WzfnsOSJXm88UYBDz5Y\nyOjRNrp10/Dr2wUw9snrGiHklxiOiHWb8WarqqpbSdq1MopHnNibVzsTE9RfNA05NcUQ8kqob0Fu\nJSldGKb6Uq32PW010hDy6GiN0FDnHk7skev2fXLVzYA385Y/Ac+mnZWkprnkX35pprBQYupUq1tx\neJW51u00fdhIGTv9RyKOxPxqkONO4PPDt1h79sLW14WE9grQdXjqKQuZmRL//ncB116r0ratXmUA\nvL0zndkLFd6EkF9iHDhgROA2TT+Ibjajh4Q4dV3bWOOtcvxobc5OUB+Rzp1Dstkc6VRlUVWjtnrj\nxjrXXlu/3OoArVvrNA3O5XeuwuSMkBdZ5HG2lly4IDnlVrfTsWNpIXc3l9y82fOFYEpSk1xym82o\n5Naoke72g5vp1Em0JkHoQcEVng+7vBWNTPkcy4rEZ90ap+7pyQIwq1crrFljZsAAG7fd5tzfqLWM\nRgsN9UqpViHklxCZmXD6tEznzhpyYqIR6ObkG75tD6ON6YkzThZSFlw0FJcrrdgi/+MPEwkJMjfe\n6GIKUh0hSdCvj5UkmhK3/Xy14+0W+V9njW2n6vLHS1K25rq7ZVrNm/9Et1gcdbw9TU1yyX/4QSEh\nQeaWW6wEVt8Irzyahun0qUqtcTD+zdq0UTlGe/zemlOtVS5lpOP35ReozZpTMPomNyZVzPnz8Mwz\nFnx9dd58M9/5gDVJwtqzN6Yzp10qPuQJhJBfQthLs3bupBqVq5yIWLfT6opQAI6l1k46h6D+Yo9Y\nr8wit5dkHTeu/lnjdvoNMQKzNu2t2AIsiZyciBYczI69RpBTVaVZy9KunYbJpBdb5BGuu9alzAyU\nfXuw9roMfH2rv8ANinPJq48ZKMv8+ca/9513umeNy8lJSPn5Fe6Pl6RNJx9yacS5HWcxb/ityrGe\nLAAzc6YvaWkyTz1V4Ei9dRZvFYYRQl6HmI4dJeCRachnz3jl9e37411apiOpqktCHhDhTzMpgWNZ\nzl8juDiwW5MVCXl2Nnz7rUJ0tEbfvvW3X33//oYY/5ESU22Al5ycjBbVlJ07ZWRZp1s35/8ui8Vo\nw3n4sAldd8+1bt62BUnXPZ4/XpLiXHLXhPzgQZk//lAYNMjm8D64ir1ZSkUR6yWxN085Rnv83369\n8oFWK36ffOCRAjA//WRi8WIzPXqo3Huv6w8qjpamdRzwJoS8DvH97BP8vvqCoJuvd+zD1SWOiPWw\neABUJ3tL22nvF88pWzPy8+pXVLKgdjFV4Vr//nuF3FyJcePcC3qqK2JiNEItWfyuD8J0rIpAj7w8\n5Ix0CsObsneviY4dNRo1cv21MjIkUlIkt1zrjv7jfWtPyLWmzdzKJbdb4+6knNmRqwl0s9OmjfE9\nc6jjDfhs/B1l65YKx1m+XWkUgJk4qdI9d2fIzoYnnvBFUXTefjvfrWQBaw/vtDQVQl6HmLduBoyI\nzSZjRiPVVZJhEQcOmDCZdDpaTgA4akk7S/uQc+jInNpbcR6x4OKkZEvPsixebHer179o9ZLIMgxo\nn8gpWpOwsXIr1P637vO9nLw81wLd7NgD3g4dktFDQtAVpbigjhOY//wDXZZrtfUmioLWvIVLueQZ\nGca/d4sWWo2CGh0R605a5Ie63QyA/9tzyg/Sdfzef9coAHPXfW7PCeCFFyzEx8s89FAhXbq4523Q\nIyNRmzU3Wpo6GW3vCYSQ1xU5OSh792C97HJy73sQ5egRgsbfhHSh+uAbT6BphlusQwcNvzTDInem\nPGtJ2jfNAuDEX6Kd6aWEIx0rovT7JTFR4vffTVxxheryXqI36F/k+t/ye+XibO/yts1mWFau7I/b\nKRXwJstF1d2ctMjz8lB27cDWvQd6gDuRZM6jtozG5EIu+TffmMnNlbjjDmuNUttNVeSQl8Qu5McL\noynsNwDL+nUoe3eXGmMvAFM4YlSNCsBs3mxi/nwfYmJUHn20Zn3abT17I6emwFnPtIl1BiHkdYR5\n519Iqoq1T39yZr1A3pR/ouzfS5MJNyNl1r4wnj4tkZMjGRHr9oIXTuaQ22lb9MGKO1C/rS+BZ5GT\nk9BlGT0srNTxJUvM6Hr9KslaFX1GNQFg04HK3a92y3lbptFtzJnSrGUpW3Ndi4w0OqA5YaGZd2xH\nslpr1a1uxx7wZoqvPmZH04y66haLzsSJNfv3Np2MQy/yCFRFZKSOn5/OiRMyuY88AYD/22+UGuP/\n4VwA8u5zvwBMXh488ogvkqTz1lv5WGqYmGMt2idn+/aa3cgFhJDXEeatm4mnGe9lTaagUCL75dfJ\nu3US5p07aDJxHOTk1Orr20uzlhJyF4LdANp1Nh7Djx+vx5uhAo8jJyejhUeUKjGq67BokYKPj86N\nNzYMIe8yIJBAKYuNyZW3BDUVfTb+Sm6Jn5/ucJO7Qrt2GrKsl8ollwoLnfK+1VZ99YpQHZHr1bvX\nf/3VxIkTMjfdZCMsrGbeF9Opk0b70mrMelmG1q014uJkCq8eirVHL3y+XYnpyGHjfNwJfL5fjbVn\nL6x9+7s9nzfe8OHECZm777ZyxRU173Jij1xn27Ya38tZhJDXEec3HuJqfuXJhVfw0Uc+IMtkv/ku\n+X8fg3nrZprcNsF4NKwl7IFuXbqoJYTcNdd6y+5NkFE5keDv8fkJ6im6jpySVG5/fO9emcOHTQwf\nbiOogWQkmkwwIPgAR2ztSDmeVeEYOTmZHPw5GB9Et26qW5lMvr5GERpH5HqE8wFvxYFu7guTs7hS\nFGb+fHtd9Zq5ncnORk5LrTbQzU6bNhrZ2RJp52RyH30SSdfxf+dNoEQBmHumuV0AZs8emblzfYiO\n1njmGffK1ZbF1qOn8YMQ8ouLzHSNGzbN5BgdkCSdjz82U1gImExk/fcjCq4bjc+G32g8dTLGCc9j\nTz3r3FnDlJSEFhDo8h6c0roZbYjj2LnQ2piioB4iZWYg5eeXi1i35443FLe6nQEdDDHduqLiQFM5\nKZGd9ELVZLf2x+3Exqqkp9sj153MJbdaMW/fii22I3po7X/G7Olf1aWgnTwp8eOPJi67THW65nxl\nFO+POyvkhvUfFydROGpC+NcAACAASURBVOI6bB07YVm6CGXvbny/WojatBkFN/zdrblYrYZLXVUl\n3ngj3+XshMrQg4KxtWlruNbrKOBNCHktk5cHk8dK7NK6M7XtT9x9t5WkJJnly4vcSmYzmR/Op2Do\nMCzr19H47ilGDUQPc+CAieBgnagoHTkpwWVrHAxXfAxHSS4IIjPT41MU1EMcOeQltmGsVli2TCE0\nVGPIkPqbO14RAwYY8928oeIvWDk5ma30AVyr6FYW+z75kSOyY+2qE3Jl726k3Jw62R+HEq71M1UL\n+Wef+aDrEnfeWXMjw9lANzv2gLe4OCNwMPfhx5FUlSYTxiDnZButSt0sADN3rg/79pmYOLGQwYM9\n+z4uHHk9NGkihPxiwGqFu+/24889jRnHIl6/7yB3312IyaTz3ns+xf/GFguZ8xdSOPAqLN+vJvCB\ne4wC1h4iO9t4qu7cWUUqLEA+d87lQDcAFIX2jRIAOHFCvHUuBRypZyUapvzyi4m0NJmbb7bVtIhW\nndNtWBh+5PLnwYotXjk5ka1mY3/aY0LusMirdq073Oq1WAimJFpUU3RFwVTFHnlurtHKMyxM44Yb\nam5gVNcspSylhBwouPFm1NZtkFNT0P393S4Ac/SozOuv+xARoTFrlmdc6iXJmfUCHD9ebctcTyG+\njWsJTTPcNmvXKgyN2M0XTEbr15eWLXVuuMHGwYMmfv21RH9iPz8yPv8aa59++C5bTMATDxs38QCH\nD8voelHEekrlVbqcoX2oEbAjmqdcGjjiKUq8XxqqWx1A7hJLf/5k34XmXKigI6+cnMQ2riA4WKd1\na/etKXsK2qFDcnFRmGpyyc1bioS8ljqelUNR0JpVnUu+fLmZ9HSJyZOtNY7mhuI+5G5Z5ACKQu7D\njwOQf6t7BWDs382FhRKvvVZQezEeLvafrwlCyGsBXYd//9vC4sVmLrtMZanPrZiD/FE7xABw332G\ni+r9931KXxgQQMZXi7H27IXfl58TMP1Jj7hm9u833lBduqjIie5FrNtp19IIyDuxR7QzvRQoW571\nwgVYu1YhNlale3fPPGjWKX5+DAzeh47Mls1lvmjz8jifbuKENZqePdUaVapr396IXDcscieC3TQN\n8+ZNqNGtqk3L8iRqdLRRuS8/v9w5XYd588yYTDq33+6ZhzZHedbWrZ0a36yZjsWiFws5hoBnfPYV\n2TOed2sO8+eb2bbNxA03WLnuuvrbH8AVhJDXAm+95cNHH/kQG6vyv7fiaHL2INY+/Rxulp49Na68\n0savvyqOaHI7euMmZHyzHFvnrvjN/5hGz8+ssZiXDHSTk+055G4KeVHhjxOHLo4PgKBqynY+W7wY\nCgokxo2z1euSrFUxINYIdNv8U2nxklOS2cYVQM3c6gB+ftCqlZGCpoaGo0tSlXvkpsOHkNPT684a\nL0KtIpd861YT+/aZuO46G82aeWavVz4ZhxYaih7Y2LnxMrRqpXHihFz8NSjLFF53Pe5Ep50+LfHC\nCxaCgnReesnzLnVvIYTcw3z6qZlXXrHQsqXGokV5RBwpcpf1Kd3o/v77K7HKAT04hPTFK7F1iMH/\nvXfwn/NyjeZ04IDR/CEmRsOUaOxxq25a5E07NsJCPsdP1qC0k6DBULbz2eefgyTpjB3b8Nzqdnr3\nM2GmkKKW3w7kpCSHkLtTmrUssbEq58/LpKWb0cPCHTnqFWH+s6j/eB0LuVZFLrkn6qqXQlUxnTnt\ntFvdTtu2GpmZUoVbIa6g60Yt9dxcidmz84mIqP/VCJ1FCLkHWb5c4ZlnLISFaSxenEvTprqjvrqt\njJAPHaoSE6OybJlCQkJ500YPDydj6WrU1m1o9Por+BVVMHIVXTci1tu21fD3Ly5B6a5rnRYt6MBR\nTiQF1GUpYYGXcLjWwyOIi5P44w8YNEj1mIXmDXy6daAPW9l9IojsEm0D5JQkR8R6TdOsoHTAmxoZ\nVWV1N8f+eB0FutmpLJc8OVli9WqFTp1U+vf3TOCtnJiAZLU6Hehmxx6rUNK97g7ffKPw668KQ4bY\nGD/+4vIoCiH3ED//bGLaNF8CAuCbb/IctafNWzejm81Ye/QqNV6W4b77rNhsEp98UnHorxbVlPSl\nq9FCQvD/vzfccrHHx0tkZhqBboDbVd0cc2rRghiOkFngS2pqA/WtCpxGTk5CCw0FHx+WLm0YDVKq\nQ+3cmav4HVWX2bateJ9cTkxkK31oEZLtEWutdMBbJFJuLlJ2BYVodB3zn5vQwiNQ27av8eu6QmW5\n5J9/bsZmk7jzTs91tXO2WUpZygW8uUFhIbz8sgV/f505c/Ib7LZQZQgh9wBbt8rceacfigILF+bR\nrVvR03xRoxRb957GplkZxoyxEh6usWCBD1kVF5pCaxlN4dVDkNPSMB0/5vLciiu6lRFyN6PW1WaG\nkINIQbsUkJOTHZXJdu40RG/YsIZtzait2zLIx2iJublEwNvZY4WkEkHvjp7p7lc6Ba3ygDf5ZBym\npETDrV7HClNRLnlhISxYYKZxY50xYzz30Fbch9w1i9wTQr5smUJioszkyVZatmy43qTKEN/E/9/e\nfcdHVaWPH//ce2cmk0lCT4L0moKgNEEQBAT7KoKrIoKCrB1WxYboWn6u3XUXFGVFRMVl4Sv2jmVR\nVpoUXSkhFEFKCAkQIGUyM/fe3x83M0kgPVNSnvfrxeuVTLlzcrkzz5xznvOcWtqyReW661wUFsK8\neQWlhqGKN0o5u8znOp3wpz95OXFC4V//Kn9Brvdsa12rfx6teu3z11i32qUezMBo1Qocp87NV4XZ\nogXd7NabfudOuXwatLw81NwTgUS39HSVhARo0SLC7aotTWNA0hFUdFatLL6GN2yzKh326RecD/pu\n3QwUxUp4q6i6m33NKgA8IRhW37NHYcsWlfR0lV27FPbsUdi/XyEzUyE7W+GIsw3HtWZ4dmfi9VpL\nsz7/3MahQyrXXuslNjZ4banuGnI/fyCvacfBNOGVVxxomsktt4SmcmakScZSLezerXDNNdEcO6bw\n8ssFXHhh6bkk//x4eYEc4IYbPMyaZWW5T5niLbPAhj8Bxr56Je6Jk6rVxpIZ62ANHxrVfCOVoih0\njc+BA7J5SkNXch/yggIr43fo0Ag3Kkhienakz6aNbNjQl4ICa8Bs/V6r19xniDMor+FyQYcORUvQ\nLvP3yMsI5P5EtyBXdJs/386DD1blbzkKG4G2pW+dPDm4QU+t5hpyv7ZtTex2k927axbIv/1WIy1N\n449/9NKuXcPrjYME8hrLzFS4+moXmZkqf/2ru8zkCf83be9ZA8s9TosWcO21XubPd/DJJzbGjj31\nOHpSMkaLFoGdkapjyxaVuDiTdu1MlNwTqHm5eGu49MyvWwePFcjTG+abQli0EsWDrOU/CqmpEW5U\nkPhSrHny9d7+bNyoMXiwzrojXVAwOOOs4H0sJicbLFtm41B0R+Ioe2jdvnolRpOm6D1OD9rr7tih\n8PjjUbRsaTB6tA+fzyoW6fMpJX62/ilr12EcOUb+Oeehmyq6DuecE/w95rU9uzEdjmpXlbTZrC9E\nv/1Ws47Dyy9bo4933NEwe+MggbxaDAN++UXl889tvPeenX37VKZPL+Tmm8uYR9J1bOt+wtelK2ZC\nQoXHvflmDwsW2HnlFQdjxpSxPldV8Q4cTNQXn6Lu22ttAVgFBQXW8PdZZ1nFLWqdsV6kRedYmq7O\nYdf2IJR6EnVWyTXk27dbvaEGE8hTezCMBfyd6axapTFwoM6G/FR6OHYQG1u790dJyck6y5bZ2Oru\nRFdO7ZGrmQex/baLwvMvDFolMF2HadOicbsV5sxxc9llFec0xN71KtGLFnLk+fXo3boHpQ1l0Xb/\nht6hY43KlnbpYvD11zZycqhWJbb161VWrrQy1f15Qg1RSCc509PTGTVqFO+88w4AGRkZTJw4kfHj\nx3PnnXfiKdrp6+OPP+bKK6/kqquu4t133w1lk6rN47H24n3ggSj69InhwgtjmDUriuxshT//uZAH\nHij7W56WthX1xPFTlp2VpXNnk0su8fG//2msXFn2m7nk8HpVbdumYhhKcaJb0Rrymia6+Zlt25JE\nOr/tdQSzJLyoY/xBR09sTXq69VGRkhLJFgWP3uN0hvBfAFat0kjf5COPWPo33xnU1/Fnrm/NsXqh\nJwfywP7jQRxWnzPHwfr1GmPHeisN4lByLXnFm6fUhnIsB/Xo0WoPq/v558mrO7w+Z47VG586teH2\nxiGEgTw/P58nnniCQYOK99WdPXs248ePZ9GiRXTs2JGlS5eSn5/PnDlzePPNN1m4cCFvvfUWOTk5\noWpWleTmwief2LjtNic9esRy9dUuFixw4HYrXHONlzffLGDr1lwefthTbpJpVebHS/IXiHnllbKT\n0PzrS/0bK1TFKfPj/oz1mmyYUoLRrj1JpOPxaezbJ/PkDVVgDXlCa3bsaFg9ciMhkeYt4HT7Ntat\n0/jpu3wA+rY5ENTXSUkpWoKWYdUE9+914BeYHw9SotvWrSrPPWdtBvL006eWXS1LdfYlryn/8raa\n5ufUJHN91y6Fzz6z0bu3zjnnNOweR8gCucPhYN68eSSUGFZes2YNI0eOBGDEiBGsWrWKX375hV69\nehEXF4fT6aRv375s2LAhVM0q16FDCu+8Y+e666JJTY1lypRo3nvPWoJx000e3n8/n82bc3npJTeX\nXOKrtDpgdQN5//4GAwb4+PprG9u2nfrf4ut5BkZMbKBwRFWckrEeqLNeux653qatLEFrBEoOraen\nq7hcJu3CVwY8tBQFX+rpnOv9lvx8hbeWWCVD+3UPbieiW7eiJWg77RjNmp3aI1+1EjM6Gt9JdSZq\nwuuFqVOtzUBefNFN8yruJ6J36ARUvi95bag1XEPuV5NAbu0wqTB1avkdroYiZJ/CNpsNp7N0xmRB\nQQGOomVPLVu2JCsri+zsbFqUWM/SokULsrKyQtWsU6SnqwwdCr16xTB9upOvv7bRtavBPfcU8u23\neaxfn8eTTxYyZIiOrRoZBfaf1mA0b16tOafbb7fm2ufOLSN13WbDd9YAbOnbULKzq3Q8f4/c3yso\nrrMenB45SCBvyPxBx9uqNTt3qkUbgUS4UUGkp6QyjO8B+HVXHFG4SU0Nbs8tJgY6dDCKlqC1LpXs\nphw9gpa2BW+/s2q8HLSkf/zDwa+/aowb5+WCC6r+dxgdqrYveW1ou3cDoFdzDblfp07VW4J26JDC\nkiV2OnY0uPTS+l33oCoiluxmllOlrLzbS2re3IXNFpzEkO++g5UrYcgQhTFjYPRo6NJFAzSghslc\nBw7A73vgD38gPrFplZ82YQI8+SS8+66DF15wcErHedR5sPw7WqX9DGPGVHgs04StW6FrV+jc2Vof\ny1HrC0Dz07tDfFyFz4+v6P6YFLpj7WN64ICT+PjgLNepyyo8Hw3V4Sxo2pQ8M5HCQujVy3rPNZhz\nMaAf585/NPBrHzbSokenSt8bJ6vsfPTqBZ99BsdSU2m5LY34WJu13m31cjBNHCNH1PqcbtgAf/87\ntGtndQSaNq3GRvEtuoPdjjNjP84g/N+W+bcc2g9A0749q31+wUpw0zTYt89OfHzlf9usWVBYCPff\nr9C6deSu13C9V8IayF0uF263G6fTSWZmJgkJCSQkJJBdood56NAhevfuXeFxjh7ND1qbzjsPvN44\nDh8uLq1W2wEBxxff0BTI7d2fgqxySraV46ab7Nx/v5PnnivkwQdLJ2jYe/WjGZD/1TfkDRlV4XEy\nMhQOH47l7LO9ZGVZc2XNdv+OzWYjGydU0K74+DiyKml31+aH4Shs2uQjK6thb2lalfPRELU8cAAj\nIZE1a/IBFx06FAJRDeZc2Np14TQO0q3pIXYcS+AsfiInOhlvNf6+qlwbnTpFAQ42Kmcwig84vHkH\nRsdOxHz1LS4gp1e/ar3myQoLYcIEFz6fxt/+lo/Ho1f7M6xF23You37jcC3/b8s7H023puMAsmJb\nVfjZU5H27WNIT4esrLwKH5ebC3PmxNKypcmll+bV+vO8poL9uVHRl4IqjVN4vV4OFi1dSktL48MP\nP6SgoPof3oMHD+arr74CYNmyZQwdOpQzzzyTX3/9lePHj5OXl8eGDRvo379/tY9dG8EeLixvo5Sq\nuPpqLy1bGixY4CDvpOvV26cfpsNRpYS3//7XPz9evORCzTxoZawH4Q+OaduU1hyU6m4NlceDeuQI\nRomM9e7dG9byHT3FytwbGv0TAANYi17LFR1lSU62hrm30AMoTiK0r/4R02azhtZr4YUXHGzdqnHD\nDR5GjKjZ1IDeviNq1iFrzWoIaHt+Q09IrNHWo35duhhkZ6vllrP2W7TITk6OwpQpXlyuGr9cvVKl\nT+EZM2bw888/k5mZybRp00hPT2fGjBkVPmfTpk1MnDiRDz74gLfffpuJEycydepUPvzwQ8aPH09O\nTg5XXHEFTqeTe+65hylTpjB58mTuuOMO4uLq99Cdfc1qTIcDb+++1X6uywWTJ3vJyVFYvPikISSn\nE1+fftg2/Q/lxPEyn3/okMKddzq54w6rtvu55xa9sQ3DKs9ay0Q3P2vzlG3s3atQ2HC29RVF/NnV\nRkLxGvKGFsjNuCbo7TvwZ8/zXJvwDZfzca2XZpbFX3N9a2EXoCj3IC8P2y8/4zuzd62C2/r1Ki+9\n5KBDB4NHH635G1EvmifX9p26L3mteb1W/YsaJrr5VWUJmtcLc+c6cLlMbryxYS85K6lKQ+uZmZlc\ndNFFLFiwgPHjxzN58mQmTZpU4XN69uzJwoULT7l9wYIFp9x20UUXcdFFF1WtxXVdbi62Tf/D16ef\nVUy9BiZP9vLyyw7mznUwaZK3VJ0Iz6BzsK9Zhe2nNXjPOz9wu9drlWR8/vkoTpxQSE3VefrpQgYO\ntAK5cuQIiteL0bp2iW5+Rltr85QfzGHs3q0GPqxEw1CyPOv2dRqaZgY+SBsSX2oPei/7koWJ21Di\nFApD0IXzfwHa4l9Lfugg9vU/ofh8gX0UaqKgAKZNc2IYCrNnF9SqLrpRYvMUvXtSzQ9UBnX/PhRd\nr3HGul/JzPXAxlQn+egjG/v2qfzpT576vydANVSpR+7xeDBNk6+//prhw4cDkHfyuK8AKt8opSri\n402uvtrLnj1WFbmSvGdb6/Ltq1cFbvvhB43zznPxyCNONA2eftrNt9/mM3hw8TBb8falwelxlNwF\nTYbXGx7/8K+e0Jrt21U6dzaCkVhd5+ipVllULfNg0N4bJ4uNhfbtDbZlWpFFzcwsXj9+ds3Xjz/1\nVBQ7dmjcfLOn1Hu9JvQOHQHQfg/+WnL/rmfV3SzlZJVtnmKaVjlWTTO59dbG0xuHKgbyAQMG0K9f\nP+Lj4+ncuTNvvvkmXbp0CXXb6qXqrh8vz623elAUs2gtZPHtvrMGYqoqjlU/8vvvCpMnO/njH12k\np6tcf72HVavymDLFe8pSOe2gVehCr2V5Vj//vuRgFV4QDYv/i1+mqyM5OUpgPXRD40vtEfg5FMPq\nfklJBplHnRyhOdrBDOxrVmEqCt6BNfucWL1a47XX7HTpYjBzZu3ntvT2/kAe/CVoNd2H/GSVrSX/\nz380tmzRGD3aR4cOjWsfiCoNrY8ZM4abb76ZJk2sogkjR46kZ8+eIW1YfRUI5BVslFIV3bqZXHih\njy+/tLN2rRYYIjfjmnDi9AG8uO4inj0nBnehwoABPp5+urDc4SYIXp11P9mXvGFTD1nXyzaP9YXd\nX2q0ofGlFm9UEspAnpxs8O23VsLbgH17sa9bi556OmazKlZtKSE31xpSVxR46aWCoCR0Fa8lD2GP\nvIZryP06dDBR1fI3T/GXY23Im6OUp8JP4OPHj/P7778zc+ZMjh07xt69e9m7dy9er5eHHnooXG2s\nP0pulBIfX+vD+QvEvPKKlfRmmvDppzZ6/fYpj+uP0MxVyJw5BXzySUGFQRxK1FkPYo+8C7tQ0WVo\nvQHyD62nHbPmdRtaopuf3rUbZtHwVWgDufVFfJOjL/Y1q1Dc7sA0WXU98UQUe/ao3H67h7POCs7/\ni5HYGtNuRwtBURh/IDc61y6QOxzQrp1ZZo/8559VVqywce65vko/CxuiCnvkGzdu5K233mLr1q3c\ncMMNgdtVVWXIkCEhb1x9498oxXPpZUE53sCBOn376nz5pY0vvrAxf76dH36wYdcc3M+z3D3Jg3bV\nXVU6VqBHXsuqbn5GYmscqo+O9oPs3Bm83aIqc/QoPPKIkzPP1LnqKi9Nq15vR1SDP9ltW5Y1r9tQ\nAzkOB3r3JGxbt4RsjhyKRzQ2O/uiHLe+oHsHVT/R7YcfNBYscJCcrHP//UHseaoqerv2IZkjV3f/\nhhkdjZGQWOtjde5s8P33NvLySif7N5bNUcpTYSAfNmwYw4YN49///jfXXnttuNpUbwVrftxPUazN\nVP70p2huuMFaTjZypI8n78lg4CUz8Gw8j2NUMZBnBjfZDZsN47Q2dM/ezrJDbTlxAsKxanDhQgdL\nlthZssTOE09EMXq0jxtu8NC3r9Hg6ymHk5qZiRkdzfY91nXXYAM51jy5beuWkA+tA2xRiofyq5vo\ndvw43HWXE00zeflld00XxZTLaN8R2w//gfx8grYA2zSt7Us7diIYb1ArkFtL0Py7Ov72m8Inn9jo\n2VNn2LCGvTlKeSocE33vvfcAa/nZrFmzTvknSgt2IAe45BIfvXrpdO5ssHBhPosWFdClfzN8ScnY\nfloLvqrVEVYzMjBdLsy4JkFrm9G2HUmeX4HwzJObJixdasPhMHnwwUISEkwWL7Zz8cUxnHeeiwUL\n7JUWixBVo2YeDKwhP+00Iyxf0iLFO3Q4pqriO71XyF4jNhbatjUCa8l9nbtU+4vDI49EsW+fyl13\neTjzzOB/sQrFWnLl6BHUE8drnejmV1bC29y5DgyjcWyOUp4KP33VogpgNpsNTdNO+SdKs69djdGi\nRVDXYdps8PXX+axZk8eFF+qBC9V79jmoedaa9arQDmZYGetBvNL1tm1JNrcB4VmCtnmzSlqaxvnn\n+7j7bg9r1uSxZEk+l17qJS1N5YEHnPTqFcs990Txyy8yb19juo6ancXxVp3Zv19tsBnrfu7xEzm8\nZSd6UnJIXyc52SDD3ZIcmlZ7WP3rrzUWLXLQs6fO3XeHZvjYCGxnGrx58uJEt05BOd7JS9Cys63C\nWe3bG1x+ecPfHKU8FQ6tjynamGPkyJGknrQR8ffffx+6VtVDasYBtL2/U3jhxUENllB2RVXv2YOI\nfvsN7KtW4qusgpzXi5KdhRHkQg9G2/YksRkITyB/7z0r6e/KK603rKrCiBE6I0boZGYqLFpk5513\n7Cxc6GDhQgdnnqlz/fVexozx1qpYRmOjZmehGAZpLmtrzYaasR6gKJgtWob8ZZKSDL77zspc71nF\nYfXff1eYM8fBokV27HZrSD1U6/n9a8nVIM6TB2sNuV/nztayst27rc/YN96wU1CgcNtthdXanbKh\nqdKn7/3338/cuXMxDIP8/Hweeugh5s2bF+q21SvFy86CN6xeEf83en9hiYqohzJRTDPoyTx627Zh\nKwqj6/D++zaaNjUZNerUb96JiSZ33+1h7do8/v3vfC66yMuvv6rcc4+TM86I5b77okhLk156VfgT\n3bao1nxuQ54fDyf/PPn6sY9TeOXVFT42PV1l6lQnAwfGsGCBg8REk7lz3aX2Tgi2UKwl968hr215\nVr+OHQ0Uxcpcz8uzAnnz5ibXXusNyvHrqyp9sr333nv4fD4mTpzI+PHjOeOMM3jnnXdC3bZ6xRaC\n+fGKGG3bobfvgH3tKjAqWXoWqOoWnIz14ja0pz17idJ85RZpCJZVqzQyMlQuu8xbYZKPpsHIkTpv\nv+1mw4Y87ruvkCZNTN56y8HIkS7eeqsa2zs2Uv5AnubtBkggDxb/ErTN8cPAXvZ1+PPPKpMnOxk6\n1MX//Z+dbt0M5swpYPXqPC67LLRDx6FYS64GaQ25n9MJbdtagXzxYjtHjqhMnuypTbn6BqFKn76a\npuFwOPB6rW89UVE13Ke7AbOvXYPpcODr3Sdsr+k9ezDqkSNo6dsqfJyaEeSM9SJG27ZoGHSNy2Tn\nTpUqbCVfY++9Z42b+YfVq6JNG5P77vOwbl0e8+cXEBdnct99Tu65J0o2eqlAYA15bjugEQyth4n/\nPG7bVvpj1zRh5UqNq6+O5oILYvjsMzu9exu8+WYB33+fz1VX+cIybGwkJGI6HKGZIy8atg+Gzp0N\nDhxQefllB06nyZQpjbs3DlUM5GPHjiUvL49//etfvPPOO6xZs4YpU6aEum31h3+jlDN613ijlJrw\nL1+xr654W9PA0rMgrSH309taH/TdHHs4flwhOzs0KaNuN3zyiZ02bQwGDar+8hKbDS67zMeyZfn0\n7KmzcKGDMWNcHDzYSFNcK+HvkW8/3IomTUwSEhpXuctQadIE2rQxAoHcNGHZMo1LL3VxxRUuli+3\nMXSoj3ffzefLL/O55BJf0LdYrpB/LXkQe+Ta7t/QT2sT1M/FTp2sL0T796uMG+clPl6uzwovkzfe\neAOAv/71r9x9992kpaURGxvL008/ze7du8PRvnohGBul1ERgnnx1xfPkWlGPXE8MbuEWs3kLzOho\nuhuhzVz/+msbx48rjB3rrdUHW4cOJp9+ms/YsV7WrdMYNcrFTz/JvPnJ1MyDeLGx82As3bvL+vxg\nSkoyyMhQ+de/7IwY4WLCBBfr1mlcdJGXzz/P4733Chg2TI/YOTfad0DNzoZgbIrl8aDu3xe0RDc/\nf+a6qja+zVHKU+Gn2PLlywHo1ctaX/n8888H7mvTJri9u/osFOvHq0Lv2g2jVTz2VSupaFw72Duf\nBSgKett2JBdsBAjZkq+aDKufoqAAJecoLhe8+qqbxx5zk52tcMUVLhYulHnzktTMTHbSFZ+uyvx4\nkPkT3u6+20laYQAhiAAAIABJREFUmsrYsV6WL8/j7bfd9O8f+XMd2AUtCGvJtb17rCTbICW6+XXt\nan3W/eEPPrp0kd44VBLIzZOCQ8nfFfmaHhCpQI6i4D17MFrGAdQKMk2DvWFKSUbbdlyW939EOUzm\nzXNUtT5NleXkwDff2EhN1QOVnKrN7abZ5RfR/NyzwTCKKuZ5WbLE2sP5nnuc3HtvFB75cg9YG6Zs\n1axNkSSQB9dFF/lo1cpg4kRrp8JQZ6JXVyCQB2GeXA3yGnK/kSN9zJhRyBNPSKKLX4WBvKJgfXKQ\nb7R0HdtPa/F17YbZqlXYX947qGievIJlaOrBAxjNm4dk/l5v247WZDL+0mx+/13lww+Dm5XzySd2\nPB6lVr3xmCcfw/7LRrSDGYEPF4Bhw3SWLcujRw+dt992MHZsNJmZ8gVVzcxkS0x/AJKSGmfJy1A5\n5xydLVvy+NvfCgNrousSf1GYYKwl14qmX4M9tO5wwPTpHk47re6dv0ip1lhoycDeUHrk9tUrYcQI\nbL9srNHzta1bUHNPhL83XiSQ8LZmVbmPUQ8eDPrSMz+jKOHtzpE/o2kms2c7KlsNVy3+YfWxY2uW\nmer4dhmuf74S+N22La3U/R07mnz2WT5XXOFl7Vob55/vYv36RjxvbpqohzLZarOm06RH3rjo/upu\nQVhLHqx9yEXlKvzE2rhxI8OHDw/88/8+bNgwfv7553C1MbTy8+H772k2+mIcy76o9tP9w+q+CAVy\nX4+eGHFNyu+R5+WhHj8Wsp2d/IG8i287Y8f6SEvT+Oqr4PTK9+9XWLnSxqBBPtq1q/63byUzk7hp\nt2I6HOTNeBgAW9qWUx4XEwP//KebRx5xc+iQwujRLhYtapxlopSjR1A8Hrbp3XE4TDp0kF5PY2IE\nhtaD0CMP8hpyUb4KP62+/PLLcLUjYrznjYL334fx42ly/bXkPvkc7ik3V/n5EZsf99M0vAPPJuqb\nZSiZmZiJpbcK1IqWnukhmB+H4iVo6r69/PnPHt59186sWQ4uushX68zb998vXZK1WgyDJtNuQc3O\nJvevz1B4wcXEPPNXtLStZT5cUWDqVC+nn25wyy3R3HVXNP/7n6da83CmCYWFkJurEBtrhnMlYtCo\nmZmYwLb89nTtZjTqspeNkRGfgBkVhRqEOXJtz26MmNiITDk2NhW+Tdu2bRuudkTWFVeQ88FnNJ1w\nDXEP3ou2Zzd5j/217CLnJ7H/tMbaKKVb9zA0tGzeswcT9c0y7GtW4rl8TKn7ivchD00g9/fI1QP7\nSU42uOQSL59/bmfFCo1zz63d/Kp/p7PLL6/+sHr0P1/Bsfw7CkddQMFNt4FpYrpc2MoJ5H4jRuh8\n9VUekyZF88YbDjZvVhkzBg4edJCbqxT9I/BzXl7xzydOgM9nfXtp1szk9dcLan0Owk3NPMh+2nLC\nG023blJoo9FRVfQuXbGlbUXJOYrZrHnNjuPfvrRT56DvPSFO1YgnA0vz9e3P0S++xZeUjGvuyzSZ\ncr017F4B/0Yp3rMGRvRi9Z5trSd3lDG8rmYcAMAI8hpyP72N9WVP27cPgDvvtFK/Z82q3c4OW7ao\nbN2qMXKkj2bNqvdc2/9+Juavj2LEJ3Bi1qvW/42q4ktKRtuRXunWr507W/Pml13mZc0aGzNmwD/+\nEcXrrztYvNjOp5/aWb7cxs8/q2RkWBXtEhMNzjzTYNgwHxdf7CU/H8aNi2bx4vrVpVUzD5JGCiDz\n442V+8prUNxunP/37xofQ8nORsnPC3qimyhb/fqUCTGjYydyPl1Gk8kTiPrsY5pl7OfY20swExLK\nfHy4N0opj693H0ynE/vqUxPeinvkIVr373JhtGiBesAK5H36WMHs++9trFun1nhtrD/J7Y9/rOaw\nem4ucbfciOL1cuzlf2LGxwfu0lN6YP95I9pvuyrdajY2Fl5/3c2PP3qJinKh6/nExJjExprExhIY\nOi/v+9uqVRo33BDNn/8czZ49hdx/f/3YK1nNPMhWrJ0OpTRr4+QeP5GY557E+eZ8azSrBheutnsX\nIIlu4SI98pOYzZpzbMkHuK8ah33DeppfMgpte3qZjw33Rinlcjjw9jsLbcsmlGM5pe4KWTGYEvS2\n7dH27wsUpbnrLqtXPnt2zXrlhmHNj8fFmZx/fvUCeezDD2DbuYP826bhHTGy1H2+ZCtAlTdPfjJF\ngSFDdC65BM4+W6dXL4POnU3i402ioyv+fBs0SOfzz/Po0MHgb3+LYupUZ71Yp14ykEuPvHEyW7Wi\n8PIx2HZsx/7fH2p0jGDvQy4qJoG8LA4HJ17+J3n3PYj2+26aXTIK+8r/nvKwSGyUUh7vwEEophkY\nJfArDuShGVoHa55cKShAOXIEgMGDdfr31/nySztbtlT/Elu9WmP//sp3OjtZ1EfvE71oId4zepP3\n0KOn3O9LtQJUWZnrodCtm8kXX+TTr5/Ou+/aueaaaHJyKn9eJKmZmaSRgqKYdO0qgbyxKpj0JwCi\n35xfo+cHex9yUTEJ5OVRFPLve5DjL81Fyc+j6VWjiXp3cfH9/o1SzuwT1o1SylO8P3npDVS0gxmY\nqooRX/b0QDAYRUmRWtHwuqLAXXdZ2d416ZXXpCSruvd3Yu+5E9Pl4sTc+VbViJPo/h75SWvJQyk+\n3uT99/O59FIvP/5o49JLXezZU3fH2LWiHnn7dgYuV6RbIyLFd9YAfKf3wvH5J4HOQHUE9iHv1CnI\nLRNlkUBeicJrxnNsyQeY0S6a3HEzrr89C6aJfcO6iGyUUh5v/wGYNtsp68nVjAyMhERro+4Q0du2\nt16rKOEN4PzzdXr00PnwQxu//Vb1wFVYCB9/bKd1a4PBg6uY8e3z0eS2P6EeP0buU8+Xu4LAaNMW\nI64Jtm1VG1oPluhomD/fzW23edi+XePii11s2FA333rHD+RxkNPo1l3WjzdqikLBpCkouo5z4ZvV\nfrq6ZzemqqK36xD8tolT1M1PkzrGO+Rccj77Gr19B2KefZK4O2/HvnKFdV8dCeTExOA740yrQp0/\n2940UTMzQrb0zM8/fGbfsC5wm6JYGeyGofDyy1XvlX/zjY1jxxTGjvVV+buH68XnsK9djXv0WNzX\nTij/gYqCnpyCtnMH4Z6wVlV4/PFCnn7azZEjCmPGuPj88zqWa2qabDvUApD5cQHuK6/GiI2zArm3\neksRtT27raWpZYyMieCTQF5FenIKRz//Fm+fvjgX/wvX318AsJae1RHes89B8fmwr/8JACXnKEph\nYciWnvl5zhuF0awZzn+9XSpAXn65j86dDZYssZORUbVeefGwetU+OOyrV+J68Tn0du3JfeEflWbY\n+lJ7oPh8VjCPgClTvLz9dgGKApMnO3nttbqz85qSe4JthZ0AyVgXQGwshVePQzuYgeOralS9LChA\nyzggiW5hJIG8GszERHI++JzCi/+AYpoR2yilPIG666uteXI1I/QZ6wC4XLivuQ41O4uozz4O3Kxp\nMG2aB49H4dVXK/9mfuyYtfd4crJOz56VBxIl5yhxt1lJOcdfnY/ZtPIF53qytUY6XAlvZbngAp2P\nPsonPt7k4YedPPRQFHodqBujZmZKxroopSZJb/7yrpLoFj4SyKvL5eL4GwvJffwpcp98LtKtKcU7\n0BrmDwRyf8Z6qNaQl+CedCMAzgWvl7r9qqu8nHaawdtv2ylKai/Xp5/aKSy0djqrdOmqaRJ7711o\n+/eRf88D+AZWbYrDl9IDAC3M8+QnO/NMgy+/zCclRWfePAeTJzvJy4tok2TpmTiFnpKKZ/AQHD/8\nB23n9io9R9sjm6WEmwTymtA0Cm6batVpr0PM5i3wpZ6Ofd1a8HjQDoa2znpJetfueM4dgWP1SrSt\nxb3dqCi4/XYP+fkK8+ZV3Cuvzk5nzkULcX78Ad6Bg8i/+74qt9O/lty2NbKBHKBdO5NPP81n6FAf\nX35pZ8wYF4cORS6j3V/VrWVMPi1bSrKbsLgnTQHA+eYbVXp8IGNdAnnYSCBvYLxnD0IpKMD2v5/D\nsoa8pILJ/mG40r3yCRO8tGhh8PrrDnJzy37ugQMKP/6oMXCgr9Idt7Tt6cQ+dD9Gk6Ycf/V1qrOz\nh5mQgNGiRcR75H5NmsC//13Atdd6+flnjdtuc/rr6oSdZ182u+hCUpty/pNEo1R4yWUY8Qk4F/+r\n0rLVYGWsgwyth5ME8gam5HrycAdyz4UXo5/Whqh3l6DkngjcHhMDN9/s5dgxhTffLDu56/33bZim\nUvna8cJC4m6dgpKfz4kXZ2O0a1+9RioKvuRUtN92QUFB9Z5bReqe3dh/XFHlxzsc8I9/uBk1yseK\nFTaWLIlMNvuu7QYGGt27ymYpogSHg4IJ16Mey8H54XuVPlz2IQ8/CeQNTCDhbc3KsJRnLcVmwz1x\nEmruCaKW/l+pu2680UNsrMmrrzpwu0996nvv2bHZKt/pLPqNedh//YWC8RNP2emtqvTkFBTTxLaj\n7NK7tRV3z500vWo0Ss7RKj9HUeC559y4XCaPPuokKyv8Q+zbd0cB0L1H6GoOiPrJPXEypqriPGm0\nrSzant0YTZrWfOc0UW0SyBsYo/Vp6J06Y1+zGvXAAUynM6xvKPeEGzA1zcpyLTFG3KwZTJ7sIStL\n5d//Lt0rT0tT2bxZY9QoHy1alH9s5cRxXLNewGjSlLxHn6hxGwMJb1tDkLnu82Fft8Za4pZevS8K\n7dqZzJxZyNGjCn/5S1Tw21aJ9IymAHTvHR321xZ1m9GuPZ4LLsL+80ZsG9eX/0DTRNuzW7YvDTMJ\n5A2QZ9A5qMdysG36H0Zi67C+oYzWp+G55DJsWzZhW7um1H233OLF6TSZM8dRqr5EVUuyRs+dg3rk\nCAV3/BmzeQURvxJ6SlHCWwhKtdq2bkYpmke0bd9W7edPmeKlb1+d99+38+234e0ZbzuaCEA36ZGL\nMviXojkrWoqWkYHidkuiW5hJIG+A/MPrimGEbX68pPKS3hISTMaP9/L77yoffGAFb8OwhtVjY00u\nuKD8QK4cPkz0qy9jtGpF/k231ap9xbugBb9HbvtpbeBnLb36gVzT4G9/c2Ozmdx/v7Pc5MBQ2JbX\nHpeST7t2krEuTuUdfh56p844P1iKcrSctaS7irYvlUS3sAprIM/Ly2Pq1KlMnDiRcePGsWLFCtLS\n0hg3bhzjxo3j0UdP3bFKVJ8/kAPoIS7PWubrnzMUX/ckoj75ECUrq9R9d9zhwWYzmT3bgWHA2rUa\n+/apXHqpj+gKRnRds19EzT1B/l33WpuF14LZsiVGfEJIeuT2dSUCeQ165ACnn25wxx0e9u5VefbZ\n8AyxG3kFbDO6kRSzH1W+3ouyqCoFN0xBcbtxLllU9mN2yT7kkRDWt+wHH3xA586dWbhwIbNmzeLJ\nJ5/kySefZObMmSxevJjc3Fy+//77cDapQTI6dUZPtBLcQl2etUyKgnvSFBSPB+e/F5a6q317kyuv\n9JGervHFFzaWLrV65n/8Y/lJbuqB/US/8Rp623YU3DAlKE30pfRA+30Pwe7y2tetxWjaDKNVK2zV\nnCMvafp0D507G8ybZ2fjxtC/Tff/coQCXCQ1PxTy1xL1l/va6zCjoqzhdaOMokE7dwISyMMtrIG8\nefPm5BRtyHz8+HGaNWvG/v37OeOMMwAYMWIEq1atCmeTGiZFwTvI6pWHo6pbWdxXX4vpchH99gJO\nrj86bZoHRTF58UUHH39sJzHRYMiQ8muUul58HqWwkPz7HrQqzASBL6WoVGt68HrlSlYW2u7f8PXr\njy8pBXXvnhovcYuOtobYDUNh+nRndfesqLYdG612Jrc5FtoXEvWa2aIlhaPHYtu1E/uKMjpdMrQe\nGWaY3XjjjeaoUaPMAQMGmOvXrzdHjx4duG/lypXm9OnTKz2G1+sLZRMbhvnzTRNM85NPIteGP/3J\nasOnn55y15VXWneBad59dwXH2L7dNG0200xONk2vN3hte+0168Xnzw/eMT/6yDrm44+b5q23Wj9v\n3FirQ954o3WYZ54JUhvL8bcbfjHBNN+9IYLXi6gfVq+2LsoxY069b9Ag09S04L5XRaXCWnnio48+\nok2bNsyfP5+0tDTuuOMO4uLiSn6pqNJxjh6tvLpQdcTHx5GVdaLyB9Ynl16J7bMO+PoPgGr+bcE6\nH7Zx19P89dcpnPUSxwecW+q+W29Vee+9GKupl+aRlVV2be+4B2bi9Pk4dt9MPEeDV8DF1qYzzYH8\nnzaSd1nFf2tVz0fMt9/jAnJSz8RmcxILHF+zgcK2XWvczgcegI8/juGxxxSGD8+jS5fQJKL9ryjv\nr10nX4V/a4N8r9RCozwfnVNpdkZvbB99xJFf0jDatA3cFb9rF3q79hwJ4nu1vgr2tREfH1fufWEd\nWt+wYQNDhgwBICUlhcLCQo4eLS6akZmZSUJCQjib1HCpKr6zBkZ0LafvjN54+/XH8c2yQNlGvzPP\nNLjuOg8XXuijV6+yg7i2eRNRHyzF2+tMPH8YHdS26SnB3wXNtv4nTEWxhta7JwM1y1wvqXlzeOqp\nQtxuhfvuC1351vR9cajodO4la8hFJfw5MIZh7VXul5cHmZkyrB4BYQ3kHTt25JdffgFg//79xMTE\n0LVrV9atWwfAsmXLGDp0aDibJEKsYNKfUEyT6JJv+CJ//3shCxcWlPtdI+aZJ1BMk/yZfyHYqdRm\nk6bobdqiBStz3efDvnE9ekoqZlwT9CQrkNu217563OjRPs4/P7TlW9MPt6IrO7G3ky/SonLuMX/E\naNIU5ztv4U/g0Pw11jtKIA+3sAbya665hv379zNhwgTuueceHnvsMWbOnMmLL77IuHHj6NChA4MH\nD678QKLeKBw9FqN5c5z/egsKC6v8PNtPa4j66gs8Zw/Gc975IWmbnpKKlnEA5VhOrY/lLwTj7XcW\nYCUZGrFxNV6CVpKiwLPPuomJMXnkEWfQd0jLzlY47IkjhTT0hDCV8xX1W0wM7muuRcs8iOPLz4AS\ngVx65GEX1kAeExPDrFmzeOedd1i8eDGDBg2iW7duLFq0iMWLF/Pggw+GszkiHJxO3NdORD18mKhP\nP6rac0yTmKf+HwD5Mx8J2fRAcWGY2vfK/YVgfP0HWDcoCnr37mg7d4Cvko1gqsBfvjUnJ/jlW7dv\ntz4GUuw7a71GXzQe7qJKb9ELrMJPsg955EjpBxFyBddPBorf8JWxf/8fHD+uoHDk+aWK2wSbL9Wq\nuW4Lwpam/kIwXn8gB/TuySheb+ADrrZuvNFLv346H3xg55tvgldG1R/Ik5tlBO2YouHTuyfhGXIu\njv/+gJa+rXgf8k6dItuwRkgCuQg5o0tXPCNGYl+7Gm3zpoofbJrEPPU4UNQbDyE92Up4C0apVn8h\nGL1b98BtviR/wltwdlkLVfnW7UUDEkmJsoZcVI+/HLPzrfnF+5BLjzzsJJCLsPBvuBBd0YYLgOPz\nT7H/vBH36LH4ep0Z0jb5kooy12uZ8KZkZwcKwZRMytP9metBmCf369HDYOpUD/v2qTzzTHCG2Ldv\nsYb+kzrJkiFRPZ6LLkVPbI1zyb+t91HLlphNmka6WY2OBHIRFp7zL0Rv2w7nu4tRThwv+0G6Tswz\nT2BqGvkPPBT6RsXEoHfohK2W25na1/8ElB5WB9CTkgCw1XIJ2smmT/fQpYtVvnXDhtq/hbfvtHEa\nB4hr2yQIrRONit2Oe8INqMePoe3bC126RLpFjZIEchEeNhvuiZNQ8vOIendJmQ+JWroE27Y03OOu\nKzVEHUq+1FTU7CyU7OwaH6Os+XGwluGYDkdQe+QATqc1xG6aCnffXbvyrXl5sDfTSQpp1pa3QlST\ne+IkTK0oZ6NrzYsfiZqTQC7CpuC6GzBtNmt705Mrm3g8xDz/NKbDQf49D4StTXqyf2/ymie82dat\ntQrB9O130h029K7drDnyIFdyOeccneuu87B1q8YrrzhqfJydO62PgFS2YiQmBqt5ohEx2rTFc+El\n1i/SI48ICeQibMzERAr/cDm2tK3Y15TeHMe58E203/dQMGkKRrv2YWuTL5DwVsNA7i8Ek5xS5tyg\nr3syal4u6oH9tWlmmR59tJD4eIMXXnDw6681eyv7M9atQC49clEz+VPvxGjSFM47L9JNaZQkkIuw\n8q89dS6YV3xjXh4xLz6H6Yoh/857w9oeX0rRErQaBvJAIZiThtX99O7WPHltS7WWpVkz+Pvf3RQW\nKtx4YzQ5NahrI4FcBIOv/wAOb/8dRo6MdFMaJQnkIqy8g87Bl5xC1Kcfoxyy9r6Onv8aatYh8m+9\nHTM+Pqzt0bsnYaoqWg2H1k8pBHPy8QOlWoMfyAEuuEBn+vRC9uxRuf326DK3iK5IenpRMRjSZGhd\n1E4E93Vo7CSQi/BSFAomTUHxeole9DbKsRxcL/8do1kzCm6bFv72OJ3onbtYm6fUYB67vIx1v+LN\nU4Kzlrws993nYfhwH998Y+PFF6s3X759u0qcmstpjsOYTZuFqIVCiFCSQC7CrvCqcZiuGJxvL8D1\n0j9Qc3LInzY9YoFET05FzclBPZRZ7efayigEU+rYXbthKkrQM9dL0jSYO7eA9u0Nnn/ewbffVq3q\nm88Hu3appGjpmK1bS49KiHpKArkIO7NJU9xXXo22by/RL/0dPSGRgik3R6w9vpSimuvVXE+uZGdj\n+22Xla1e3u5s0dEYHTqGbGjdr0ULmD+/AIcDbrstmj17Kg/Ke/YoeL0Kqb7NGAkyrC5EfSWBXERE\nwaQpANY2pdPvB5crYm3RU2q2BK2yYXU/X1IyanY2ypHDNWtgFfXubfDMM9bGKjfeGE1BJYXa0tOt\nnnuquVkS3YSoxySQi4jQe51B4cjz8aWejnvCDRFtiz9zvbpL0MorBHMyPQzz5H7XXedlwgQPv/6q\n8cADzgqn/UtnrEuPXIj6SgK5iJjji5Zy9D8/gqPmBU2CQe/SFdNmq/YStEAhmH79Kz5+iDPXT/bU\nU4X07q2zeLGdhQvt5T4usH2pVHUTol6TQC4iR1HKn1sOJ4cDvVt3tG1pVc9c9/mwb9xQbiGYUg8N\n4Vrysjid1nx5ixYGM2dGlVuPfft2Fbum05WdEsiFqMfqwKeoEJHnS05FzT2Bun9flR6vbd2Ckp9X\n6bA6hL9HDtC+vcncuW68XpgyJZrs7NLJb6ZpBfKuzQ9jQ5ehdSHqMQnkQlAi4a2Ke5P758fLKwRT\nktm0GXpCItr20M+RlzR8uM6DD3rYv1/llluc+HzF92VmKpw4oZAcY31x0ROkRy5EfSWBXAisHjmA\nlla1vckDiW79zqrS4/WkZLS9v1vbjYXRn//s4aKLvKxYYeOZZ4pzEQIV3ew7AGRoXYh6TAK5EICe\nWr0laIFCMEXz35Uev+hxtp3ba9bAGlJVeOklN507G8yeHcVnn9mAEhnr+iZMTcNs1Sqs7RJCBI8E\nciEAvVMXzKioKi1Bq1IhmJP4kvxL0MI3T+7XtCksWFBAdLTJtGlOdu5UigN5wQarGExdSDoUQtSI\nvHuFANA0fN2TsaWnUdnOI/YNVSsEU1JgLXkYE95K6tHD4MUX3eTmKkyaFM0vv1jFYHocWSmJbkLU\ncxLIhSiiJ6egFBSg7tld4eNs62oQyP2Z62EoClOeK6/0cdNNHrZt01i/XqNdGx+xnqMyPy5EPSeB\nXIgivtSivcm3VZzwZq9iIZiSjMTWGHFNItYj93v00UIGDLDS15PaWYl3hmSsC1GvSSAXooieXIUl\naD4f9g3rq1QIphRFQU9KQtu1E7zeWra05hwOeP11N/376/xxwC4AGVoXop6TQC5EkcAuaBUkvAUK\nwVRx2Vmp4yeloPh8aL/tqnEbg6F1a5PPP8/nutR1gCw9E6K+k0AuRBGjfQdMl6vCmuvVKQRzsuLN\nUyI7vO6nZlr7r0sgF6J+k0AuhJ+q4ktKRtuRTqkyaCVUdcezsuhJRWvJIzxP7qdmHgRkaF2I+k4C\nuRAl6Ck9UDyecoe/bet/qlYhmJJ8da1HfsgfyKVHLkR9JoFciBKKS7WeOryuHD6MbdfOahWCKcno\n0NEqOhPmmuvlUTMzMRUFIz4h0k0RQtSCBHIhSvCllp+5bl9f82F1ADQNvWt3bDvSKy06Ew5q5kHM\nli3BXv6e5UKIuk8CuRAl+JegaWWsJQ8UgqlBxrqfLykJJT+/ytulhpKamSlryIVoACSQC1GC0aYt\nRlyTsnvk/oz1ahSCOVmkS7UG5OWh5p6QRDchGgAJ5EKUpCjoySlW4ZbCwuLbiwrB+JJTMJs2q/Hh\ni0u1RjaQF2esS49ciPpOArkQJ/Gl9rAKt+zcEbhNS9tqFYKp6fy4/9iBHnlkE960Q7KGXIiGQgK5\nECfRk1OA0nuT16YQTKljd+2Gqap1pkeuy9C6EPWeBHIhTuJLsTZP0UrMk9emEEwpUVHoHTtFfI5c\nhtaFaDgkkAtxEl9g85TizHXburUYTZrWqBDMyfSkZNQjR1Cys2t9rJoqLs96WsTaIIQIjrAH8o8/\n/pjLL7+csWPHsnz5cjIyMpg4cSLjx4/nzjvvxOPxhLtJQpRiJiRgtGiB5h9az86uVSGYk/kz1yNZ\nqlU9mAFIeVYhGoKwBvKjR48yZ84cFi1axNy5c/n222+ZPXs248ePZ9GiRXTs2JGlS5eGs0lCnEpR\n8CWnWmVaCwpg9WogCMPqRXxJkS/VKhumCNFwhDWQr1q1ikGDBhEbG0tCQgJPPPEEa9asYeTIkQCM\nGDGCVatWhbNJQpRJT0lFMU2rClvRNRmsQO4fno/kPLl66CBG02bgdEasDUKI4LCF88X27duH2+3m\n1ltv5fjx40ybNo2CggIcDgcALVu2JCsrq9LjNG/uwmbTgtq2+Pi4oB6vvmv056N/H1gAzff/FuiR\nN7twBDQLwnkZ1A8A1+6duCJ1ng9lQpvTavT/3OivjZPI+ShNzkexcJ2LsAZygJycHF5++WUOHDjA\n9ddfj2kiLNZkAAAPgklEQVSagftK/lyRo0fzg9qm+Pg4srJOBPWY9ZmcD7C360IzIH/1Olxr1+JL\nTuGoV4OgnBeVFq1Pg81bOBKJ81xYSPyRI3hO78Wxar6+XBulyfkoTc5HsWCfi4q+FIR1aL1ly5b0\n6dMHm81Ghw4diImJISYmBrfbDUBmZiYJCbITk4g8X9Fa8qiPP4Dc3KANq/vp3ZPR9u+D3NygHrcq\n/HXejQRJdBOiIQhrIB8yZAirV6/GMAyOHj1Kfn4+gwcP5quvvgJg2bJlDB06NJxNEqJMZouW6AmJ\naPv2AuCrxUYpZdGTrHly247wV3hzrF4JYGXhCyHqvbAOrScmJnLhhRdy9dVXA/Dwww/Tq1cvHnjg\nAZYsWUKbNm244oorwtkkIcqlJ6cGSpkGu0fuS7J6/Fr6Nny9+wb12JWx/7AcAM/Q4WF9XSFEaIR9\njnzcuHGMGzeu1G0LFiwIdzOEqJQvJQXHiuXQtGlgs5NgKbl5SmEljw0q08T+3x/QExIDpWiFEPWb\nVHYTohx6UalWBg4MSiGYkgKbp4R5Lbm2LQ3tUCbeIeeCooT1tYUQoSGBXIhyeM8aiKmqcPHFQT+2\nGR+P0axZ2NeSO1YsB8B77vCwvq4QInTCPrQuRH2hp6RyZOMWWp7eDY4Ed8kjioLePRnbhnXg8UBR\nLYVQs6/4HgDP0GFheT0hROhJj1yIChintQEtuMWH/HxJySi6jrZrZ0iOf+oL+rD/+F98nbtgtO8Q\nntcUQoScBHIhIsS/eUq4htdtP29APXEcr2SrC9GgSCAXIkICa8nDlPDm8A+rnyvD6kI0JBLIhYgQ\nX5h75P75ce8554bl9YQQ4SGBXIgIMdp3wIyORksPQ3W3/Hzsa1fj7XkGZsuWoX89IUTYSCAXIlJU\nFV/X7th2bgfDCOlL2deuRvF48Eq2uhANjgRyISJIT0pCKShA3ft7SF8nMD8+bHhIX0cIEX4SyIWI\nIH/mui3E8+T2Fcsx7Xa8AweH9HWEEOEngVyICPIl+Uu1hm6eXMk5iu2Xn/H2OwtiYkL2OkKIyJBA\nLkQEhWMtuf3H/6KYpsyPC9FASSAXIoL0Ll0xNS2ka8n99dVl21IhGiYJ5EJEksOB3qmz1SM3zZC8\nhP2H5Rgxsfj69Q/J8YUQkSWBXIgI07sno+bkoGRlBf3YasYBbDu24x00GOz2oB9fCBF5EsiFiDA9\nKXSZ6/YflgNIfXUhGjAJ5EJEWHHmevADuUO2LRWiwZNALkSE+XvkQc9cN03sK77HaNkSvcfpwT22\nEKLOkEAuRIT5uhXtgrYtuIFc27kDLeMAniHDQJW3uhANlby7hYi02Fj0tu2C3iO3f/8fALznDg/q\ncYUQdYsEciHqAL17EtrBDJTjx4J2TJkfF6JxkEAuRB0QSHjbHqRSrbqO/ccV6B06YnTqHJxjCiHq\nJAnkQtQBxaVagxPIbb/+gnosR3rjQjQCEsiFqAP8mev29euCcjz7D9awutRXF6Lhk0AuRB3g7dsf\nvUNHnIveRtu1o9bHcxQVgvEMkUAuREMngVyIuiAqitxHn0Dxeol59KHaHcvtxr52Fb7U0zETEoLT\nPiFEnSWBXIg6wvOH0XgGDyHqqy+wf/dNjY9jX7cWxe3Gc670xoVoDCSQC1FXKAq5TzyDqarEPvIg\neL01Ooy9aNtSmR8XonGQQC5EHaL3OgP3hEnY0rfhfGt+jY7h+OF7TE3DO+icILdOCFEXSSAXoo7J\nm/EwRpOmxDz7FMrhw9V6rnL8GLafN+Dr0w8zrkmIWiiEqEskkAtRx5itWpF/7wOox3KIee7Jaj3X\nvvJHFF3HI2VZhWg0JJALUQcV3Hgzvm7dcb71BtqWzVV+XmB+XAK5EI2GBHIh6iKHg7wnnkYxDGL/\nMgNMs2pPW/E9ZnQ03n5nhbiBQoi6QgK5EHWUZ+QFFI66AMeK73F88Vmlj1cyM7GlbcU7cBBERYWh\nhUKIukACuRB1WN7/exrTZiP20Zngdlf4WMd//budDQ9Dy4QQdYUEciHqML1bdwqm3IK2ZzfRr71S\n4WPtRduWeqUQjBCNigRyIeq4/HsfwGjZkpgXn0c9mFH2g0wTxw/LMZo3x9fzjPA2UAgRUREJ5G63\nm1GjRvH++++TkZHBxIkTGT9+PHfeeScejycSTRKizjKbNiPvwUdQ8vOIefLxMh+j/rYLbd9evOec\nC5oW5hYKISIpIoH81VdfpWnTpgDMnj2b8ePHs2jRIjp27MjSpUsj0SQh6jT3ddfjO70XziWLsG1c\nf8r9jhX++XEZVheisQl7IN+5cyc7duxg+PDhAKxZs4aRI0cCMGLECFatWhXuJglR92kauX99BoDY\nmfefshxN5seFaLxs4X7BZ599lr/85S98+OGHABQUFOBwOABo2bIlWVlZlR6jeXMXNltwhw/j4+OC\nerz6Ts5HaXXifFxxCfzxj9iXLiX+60/guuus2w0DfvwB2rWjxcA+oCghbUadOBd1iJyP0uR8FAvX\nuQhrIP/www/p3bs37du3L/N+s4pFL44ezQ9ms4iPjyMr60RQj1mfyfkorS6dD3XGo7T45BOMe+/j\nyODzIDYW26+/0PzwYdzXjOdEdm5IX78unYu6QM5HaXI+igX7XFT0pSCsgXz58uXs3buX5cuXc/Dg\nQRwOBy6XC7fbjdPpJDMzk4SEhHA2SYh6xejQkfw7/kzMi8/jevnv5M/4C/YfiubHpSyrEI1SWAP5\nP/7xj8DPL730Em3btmXjxo189dVXjB49mmXLljF06NBwNkmIeid/6t04F72D65WXcI+/HofsPy5E\noxbxdeTTpk3jww8/ZPz48eTk5HDFFVdEuklC1G2xseT95XEUt5vYh2dgX70SX1IyRuvTIt0yIUQE\nhD3ZzW/atGmBnxcsWBCpZghRLxVeeTXeN+YR9aVVg11640I0XhHvkQshakBVyX3y2cCvUl9diMZL\nArkQ9ZSvb38KJv8Jo1U83iGSWyJEYyWBXIh6LPeZv3H413TMJk0j3RQhRIRIIBeiPlMUqa0uRCMn\ngVwIIYSoxySQCyGEEPWYBHIhhBCiHpNALoQQQtRjEsiFEEKIekwCuRBCCFGPSSAXQggh6jEJ5EII\nIUQ9JoFcCCGEqMckkAshhBD1mARyIYQQoh5TTNM0I90IIYQQQtSM9MiFEEKIekwCuRBCCFGPSSAX\nQggh6jEJ5EIIIUQ9JoFcCCGEqMckkAshhBD1mC3SDYi0p556il9++QVFUZg5cyZnnHFGpJsUMWvW\nrOHOO++ke/fuACQlJfGXv/wlwq0Kv/T0dG6//XYmTZrEhAkTyMjI4P7770fXdeLj43n++edxOByR\nbmZYnHwuZsyYwebNm2nWrBkAU6ZMYfjw4ZFtZBg999xzrF+/Hp/Pxy233EKvXr0a7bVx8rn47rvv\nGu21UVBQwIwZMzh8+DCFhYXcfvvtpKSkhO3aaNSBfO3atezZs4clS5awc+dOZs6cyZIlSyLdrIga\nMGAAs2fPjnQzIiY/P58nnniCQYMGBW6bPXs248eP5+KLL+bFF19k6dKljB8/PoKtDI+yzgXA9OnT\nGTFiRIRaFTmrV69m+/btLFmyhKNHjzJmzBgGDRrUKK+Nss7F2Wef3Wivjf/85z/07NmTm266if37\n93PjjTfSt2/fsF0bjXpofdWqVYwaNQqArl27cuzYMXJzcyPcKhFJDoeDefPmkZCQELhtzZo1jBw5\nEoARI0awatWqSDUvrMo6F43ZWWedxaxZswBo0qQJBQUFjfbaKOtc6Loe4VZFziWXXMJNN90EQEZG\nBomJiWG9Nhp1IM/OzqZ58+aB31u0aEFWVlYEWxR5O3bs4NZbb+Xaa6/lxx9/jHRzws5ms+F0Okvd\nVlBQEBgSa9myZaO5Rso6FwDvvPMO119/PXfffTdHjhyJQMsiQ9M0XC4XAEuXLuXcc89ttNdGWedC\n07RGe234jRs3jnvvvZeZM2eG9dpo1EPrJ2vs1Wo7derE1KlTufjii9m7dy/XX389y5YtazRzflXR\n2K+R0aNH06xZM1JTU3nttdd4+eWXeeSRRyLdrLD65ptvWLp0KW+88QYXXHBB4PbGeG2UPBebNm1q\n9NfG4sWL2bp1K/fdd1+p6yHU10aj7pEnJCSQnZ0d+P3QoUPEx8dHsEWRlZiYyCWXXIKiKHTo0IFW\nrVqRmZkZ6WZFnMvlwu12A5CZmdmoh5oHDRpEamoqAOeddx7p6ekRblF4rVixgrlz5zJv3jzi4uIa\n9bVx8rlozNfGpk2byMjIACA1NRVd14mJiQnbtdGoA/k555zDV199BcDmzZtJSEggNjY2wq2KnI8/\n/pj58+cDkJWVxeHDh0lMTIxwqyJv8ODBgetk2bJlDB06NMItipxp06axd+9ewMod8K9waAxOnDjB\nc889xz//+c9AZnZjvTbKOheN+dpYt24db7zxBmBN2ebn54f12mj0u5+98MILrFu3DkVRePTRR0lJ\nSYl0kyImNzeXe++9l+PHj+P1epk6dSrDhg2LdLPCatOmTTz77LPs378fm81GYmIiL7zwAjNmzKCw\nsJA2bdrw9NNPY7fbI93UkCvrXEyYMIHXXnuN6OhoXC4XTz/9NC1btox0U8NiyZIlvPTSS3Tu3Dlw\n2zPPPMPDDz/c6K6Nss7F2LFjeeeddxrlteF2u3nooYfIyMjA7XYzdepUevbsyQMPPBCWa6PRB3Ih\nhBCiPmvUQ+tCCCFEfSeBXAghhKjHJJALIYQQ9ZgEciGEEKIek0AuhBBC1GNS2U2IRuy5557j119/\npbCwkC1bttCnTx8Azj77bBISErjqqqsi3EIhRGVk+ZkQgn379jF+/Hh++OGHSDdFCFFN0iMXQpzi\npZdewufzcffdd9OnTx9uu+02vvvuO7xeL7feeiv/93//x2+//cZjjz3GkCFDOHDgAI8//jgFBQXk\n5+czffp0Bg8eHOk/Q4hGQebIhRAVys/Pp2fPnixevBiXy8V3333HvHnzuP3221m0aBEAjz32GJMn\nT+btt9/m1Vdf5eGHH8bn80W45UI0DtIjF0JUql+/foC1sU7fvn0BaN26NSdOnACs2tp5eXnMmTMH\nsLZAlVr9QoSHBHIhRKU0TSvzZz+Hw8FLL71EixYtwtksIQQytC6ECIJ+/frxxRdfAHDkyBGefPLJ\nCLdIiMZDeuRCiFp76KGHeOSRR/jss8/weDzcdtttkW6SEI2GLD8TQggh6jEZWhdCCCHqMQnkQggh\nRD0mgVwIIYSoxySQCyGEEPWYBHIhhBCiHpNALoQQQtRjEsiFEEKIekwCuRBCCFGP/X8SpbJBnZuY\nRQAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 576x396 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "kU9GvQI43I81",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 6242
        },
        "outputId": "2aac2a1e-feb8-4f09-d254-cbc76cab2461"
      },
      "cell_type": "code",
      "source": [
        "#implement grid search over \n",
        "from sklearn import svm, model_selection\n",
        "\n",
        "kernels = ['linear', 'rbf', 'poly']\n",
        "Cs = [0.001, 0.01, 0.1, 1, 10]\n",
        "gammas = [0.001, 0.01, 0.1, 1]\n",
        "param_grid = {'kernel': kernels, 'C': Cs, 'gamma' : gammas}\n",
        "   \n",
        "grid_search = model_selection.GridSearchCV(svm.SVR(), param_grid)\n",
        "grid_search.fit(svm_X_train, svm_y_train)\n",
        "grid_search.best_params_"
      ],
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/sklearn/model_selection/_split.py:2053: FutureWarning: You should specify a value for 'cv' instead of relying on the default value. The default value will change from 3 to 5 in version 0.22.\n",
            "  warnings.warn(CV_WARNING, FutureWarning)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/utils/validation.py:761: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  y = column_or_1d(y, warn=True)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/utils/validation.py:761: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  y = column_or_1d(y, warn=True)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/utils/validation.py:761: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  y = column_or_1d(y, warn=True)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/utils/validation.py:761: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  y = column_or_1d(y, warn=True)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/utils/validation.py:761: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  y = column_or_1d(y, warn=True)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/utils/validation.py:761: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  y = column_or_1d(y, warn=True)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/utils/validation.py:761: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  y = column_or_1d(y, warn=True)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/utils/validation.py:761: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  y = column_or_1d(y, warn=True)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/utils/validation.py:761: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  y = column_or_1d(y, warn=True)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/utils/validation.py:761: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  y = column_or_1d(y, warn=True)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/utils/validation.py:761: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  y = column_or_1d(y, warn=True)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/utils/validation.py:761: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  y = column_or_1d(y, warn=True)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/utils/validation.py:761: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  y = column_or_1d(y, warn=True)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/utils/validation.py:761: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  y = column_or_1d(y, warn=True)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/utils/validation.py:761: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  y = column_or_1d(y, warn=True)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/utils/validation.py:761: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  y = column_or_1d(y, warn=True)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/utils/validation.py:761: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  y = column_or_1d(y, warn=True)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/utils/validation.py:761: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  y = column_or_1d(y, warn=True)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/utils/validation.py:761: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  y = column_or_1d(y, warn=True)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/utils/validation.py:761: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  y = column_or_1d(y, warn=True)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/utils/validation.py:761: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  y = column_or_1d(y, warn=True)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/utils/validation.py:761: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  y = column_or_1d(y, warn=True)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/utils/validation.py:761: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  y = column_or_1d(y, warn=True)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/utils/validation.py:761: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  y = column_or_1d(y, warn=True)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/utils/validation.py:761: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  y = column_or_1d(y, warn=True)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/utils/validation.py:761: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  y = column_or_1d(y, warn=True)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/utils/validation.py:761: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  y = column_or_1d(y, warn=True)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/utils/validation.py:761: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  y = column_or_1d(y, warn=True)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/utils/validation.py:761: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  y = column_or_1d(y, warn=True)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/utils/validation.py:761: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  y = column_or_1d(y, warn=True)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/utils/validation.py:761: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  y = column_or_1d(y, warn=True)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/utils/validation.py:761: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  y = column_or_1d(y, warn=True)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/utils/validation.py:761: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  y = column_or_1d(y, warn=True)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/utils/validation.py:761: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  y = column_or_1d(y, warn=True)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/utils/validation.py:761: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  y = column_or_1d(y, warn=True)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/utils/validation.py:761: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  y = column_or_1d(y, warn=True)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/utils/validation.py:761: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  y = column_or_1d(y, warn=True)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/utils/validation.py:761: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  y = column_or_1d(y, warn=True)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/utils/validation.py:761: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  y = column_or_1d(y, warn=True)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/utils/validation.py:761: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  y = column_or_1d(y, warn=True)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/utils/validation.py:761: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  y = column_or_1d(y, warn=True)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/utils/validation.py:761: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  y = column_or_1d(y, warn=True)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/utils/validation.py:761: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  y = column_or_1d(y, warn=True)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/utils/validation.py:761: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  y = column_or_1d(y, warn=True)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/utils/validation.py:761: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  y = column_or_1d(y, warn=True)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/utils/validation.py:761: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  y = column_or_1d(y, warn=True)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/utils/validation.py:761: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  y = column_or_1d(y, warn=True)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/utils/validation.py:761: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  y = column_or_1d(y, warn=True)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/utils/validation.py:761: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  y = column_or_1d(y, warn=True)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/utils/validation.py:761: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  y = column_or_1d(y, warn=True)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/utils/validation.py:761: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  y = column_or_1d(y, warn=True)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/utils/validation.py:761: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  y = column_or_1d(y, warn=True)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/utils/validation.py:761: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  y = column_or_1d(y, warn=True)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/utils/validation.py:761: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  y = column_or_1d(y, warn=True)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/utils/validation.py:761: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  y = column_or_1d(y, warn=True)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/utils/validation.py:761: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  y = column_or_1d(y, warn=True)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/utils/validation.py:761: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  y = column_or_1d(y, warn=True)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/utils/validation.py:761: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  y = column_or_1d(y, warn=True)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/utils/validation.py:761: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  y = column_or_1d(y, warn=True)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/utils/validation.py:761: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  y = column_or_1d(y, warn=True)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/utils/validation.py:761: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  y = column_or_1d(y, warn=True)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/utils/validation.py:761: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  y = column_or_1d(y, warn=True)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/utils/validation.py:761: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  y = column_or_1d(y, warn=True)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/utils/validation.py:761: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  y = column_or_1d(y, warn=True)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/utils/validation.py:761: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  y = column_or_1d(y, warn=True)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/utils/validation.py:761: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  y = column_or_1d(y, warn=True)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/utils/validation.py:761: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  y = column_or_1d(y, warn=True)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/utils/validation.py:761: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  y = column_or_1d(y, warn=True)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/utils/validation.py:761: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  y = column_or_1d(y, warn=True)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/utils/validation.py:761: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  y = column_or_1d(y, warn=True)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/utils/validation.py:761: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  y = column_or_1d(y, warn=True)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/utils/validation.py:761: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  y = column_or_1d(y, warn=True)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/utils/validation.py:761: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  y = column_or_1d(y, warn=True)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/utils/validation.py:761: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  y = column_or_1d(y, warn=True)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/utils/validation.py:761: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  y = column_or_1d(y, warn=True)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/utils/validation.py:761: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  y = column_or_1d(y, warn=True)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/utils/validation.py:761: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  y = column_or_1d(y, warn=True)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/utils/validation.py:761: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  y = column_or_1d(y, warn=True)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/utils/validation.py:761: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  y = column_or_1d(y, warn=True)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/utils/validation.py:761: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  y = column_or_1d(y, warn=True)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/utils/validation.py:761: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  y = column_or_1d(y, warn=True)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/utils/validation.py:761: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  y = column_or_1d(y, warn=True)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/utils/validation.py:761: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  y = column_or_1d(y, warn=True)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/utils/validation.py:761: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  y = column_or_1d(y, warn=True)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/utils/validation.py:761: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  y = column_or_1d(y, warn=True)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/utils/validation.py:761: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  y = column_or_1d(y, warn=True)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/utils/validation.py:761: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  y = column_or_1d(y, warn=True)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/utils/validation.py:761: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  y = column_or_1d(y, warn=True)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/utils/validation.py:761: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  y = column_or_1d(y, warn=True)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/utils/validation.py:761: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  y = column_or_1d(y, warn=True)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/utils/validation.py:761: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  y = column_or_1d(y, warn=True)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/utils/validation.py:761: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  y = column_or_1d(y, warn=True)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/utils/validation.py:761: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  y = column_or_1d(y, warn=True)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/utils/validation.py:761: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  y = column_or_1d(y, warn=True)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/utils/validation.py:761: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  y = column_or_1d(y, warn=True)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/utils/validation.py:761: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  y = column_or_1d(y, warn=True)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/utils/validation.py:761: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  y = column_or_1d(y, warn=True)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/utils/validation.py:761: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  y = column_or_1d(y, warn=True)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/utils/validation.py:761: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  y = column_or_1d(y, warn=True)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/utils/validation.py:761: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  y = column_or_1d(y, warn=True)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/utils/validation.py:761: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  y = column_or_1d(y, warn=True)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/utils/validation.py:761: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  y = column_or_1d(y, warn=True)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/utils/validation.py:761: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  y = column_or_1d(y, warn=True)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/utils/validation.py:761: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  y = column_or_1d(y, warn=True)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/utils/validation.py:761: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  y = column_or_1d(y, warn=True)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/utils/validation.py:761: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  y = column_or_1d(y, warn=True)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/utils/validation.py:761: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  y = column_or_1d(y, warn=True)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/utils/validation.py:761: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  y = column_or_1d(y, warn=True)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/utils/validation.py:761: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  y = column_or_1d(y, warn=True)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/utils/validation.py:761: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  y = column_or_1d(y, warn=True)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/utils/validation.py:761: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  y = column_or_1d(y, warn=True)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/utils/validation.py:761: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  y = column_or_1d(y, warn=True)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/utils/validation.py:761: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  y = column_or_1d(y, warn=True)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/utils/validation.py:761: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  y = column_or_1d(y, warn=True)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/utils/validation.py:761: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  y = column_or_1d(y, warn=True)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/utils/validation.py:761: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  y = column_or_1d(y, warn=True)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/utils/validation.py:761: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  y = column_or_1d(y, warn=True)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/utils/validation.py:761: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  y = column_or_1d(y, warn=True)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/utils/validation.py:761: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  y = column_or_1d(y, warn=True)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/utils/validation.py:761: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  y = column_or_1d(y, warn=True)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/utils/validation.py:761: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  y = column_or_1d(y, warn=True)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/utils/validation.py:761: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  y = column_or_1d(y, warn=True)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/utils/validation.py:761: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  y = column_or_1d(y, warn=True)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/utils/validation.py:761: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  y = column_or_1d(y, warn=True)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/utils/validation.py:761: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  y = column_or_1d(y, warn=True)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/utils/validation.py:761: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  y = column_or_1d(y, warn=True)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/utils/validation.py:761: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  y = column_or_1d(y, warn=True)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/utils/validation.py:761: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  y = column_or_1d(y, warn=True)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/utils/validation.py:761: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  y = column_or_1d(y, warn=True)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/utils/validation.py:761: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  y = column_or_1d(y, warn=True)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/utils/validation.py:761: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  y = column_or_1d(y, warn=True)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/utils/validation.py:761: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  y = column_or_1d(y, warn=True)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/utils/validation.py:761: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  y = column_or_1d(y, warn=True)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/utils/validation.py:761: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  y = column_or_1d(y, warn=True)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/utils/validation.py:761: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  y = column_or_1d(y, warn=True)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/utils/validation.py:761: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  y = column_or_1d(y, warn=True)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/utils/validation.py:761: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  y = column_or_1d(y, warn=True)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/utils/validation.py:761: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  y = column_or_1d(y, warn=True)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/utils/validation.py:761: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  y = column_or_1d(y, warn=True)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/utils/validation.py:761: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  y = column_or_1d(y, warn=True)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/utils/validation.py:761: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  y = column_or_1d(y, warn=True)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/utils/validation.py:761: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  y = column_or_1d(y, warn=True)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/utils/validation.py:761: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  y = column_or_1d(y, warn=True)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/utils/validation.py:761: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  y = column_or_1d(y, warn=True)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/utils/validation.py:761: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  y = column_or_1d(y, warn=True)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/utils/validation.py:761: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  y = column_or_1d(y, warn=True)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/utils/validation.py:761: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  y = column_or_1d(y, warn=True)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/utils/validation.py:761: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  y = column_or_1d(y, warn=True)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/utils/validation.py:761: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  y = column_or_1d(y, warn=True)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/utils/validation.py:761: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  y = column_or_1d(y, warn=True)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/utils/validation.py:761: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  y = column_or_1d(y, warn=True)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/utils/validation.py:761: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  y = column_or_1d(y, warn=True)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/utils/validation.py:761: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  y = column_or_1d(y, warn=True)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/utils/validation.py:761: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  y = column_or_1d(y, warn=True)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/utils/validation.py:761: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  y = column_or_1d(y, warn=True)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/utils/validation.py:761: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  y = column_or_1d(y, warn=True)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/utils/validation.py:761: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  y = column_or_1d(y, warn=True)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/utils/validation.py:761: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  y = column_or_1d(y, warn=True)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/utils/validation.py:761: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  y = column_or_1d(y, warn=True)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/utils/validation.py:761: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  y = column_or_1d(y, warn=True)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/utils/validation.py:761: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  y = column_or_1d(y, warn=True)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/utils/validation.py:761: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  y = column_or_1d(y, warn=True)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/utils/validation.py:761: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  y = column_or_1d(y, warn=True)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/utils/validation.py:761: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  y = column_or_1d(y, warn=True)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/utils/validation.py:761: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  y = column_or_1d(y, warn=True)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/utils/validation.py:761: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  y = column_or_1d(y, warn=True)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/utils/validation.py:761: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  y = column_or_1d(y, warn=True)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/utils/validation.py:761: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  y = column_or_1d(y, warn=True)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/utils/validation.py:761: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  y = column_or_1d(y, warn=True)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/utils/validation.py:761: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  y = column_or_1d(y, warn=True)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/utils/validation.py:761: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  y = column_or_1d(y, warn=True)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/utils/validation.py:761: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  y = column_or_1d(y, warn=True)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/utils/validation.py:761: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  y = column_or_1d(y, warn=True)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/utils/validation.py:761: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  y = column_or_1d(y, warn=True)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/utils/validation.py:761: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  y = column_or_1d(y, warn=True)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/utils/validation.py:761: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  y = column_or_1d(y, warn=True)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/utils/validation.py:761: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  y = column_or_1d(y, warn=True)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/utils/validation.py:761: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  y = column_or_1d(y, warn=True)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/utils/validation.py:761: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  y = column_or_1d(y, warn=True)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/utils/validation.py:761: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  y = column_or_1d(y, warn=True)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/utils/validation.py:761: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  y = column_or_1d(y, warn=True)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'C': 0.1, 'gamma': 0.001, 'kernel': 'linear'}"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 22
        }
      ]
    },
    {
      "metadata": {
        "id": "OdOcHgMg5LlA",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 447
        },
        "outputId": "e883a9fd-df9d-474d-d0bd-bdfd5f4046b0"
      },
      "cell_type": "code",
      "source": [
        "svm_regrBest = svm.SVR(kernel = 'linear', C = 0.1, gamma = 0.001)\n",
        "\n",
        "svm_regrBest.fit(svm_X_train, svm_y_train)\n",
        "\n",
        "svm_predsBest = svm_regrBest.predict(svm_X_test)\n",
        "\n",
        "# invert scaling for forecast to revert data into original form\n",
        "inv_yhatBest = np.concatenate((svm_X_test[:,0].reshape(-1,1), svm_predsBest.reshape(-1,1), svm_X_test[:, 1:]), axis=1)\n",
        "inv_yhatBest = scaler.inverse_transform(inv_yhatBest)\n",
        "inv_yhatBest = inv_yhatBest[:,1]\n",
        "\n",
        "\n",
        "# calculate RMSE\n",
        "rmse2 = math.sqrt(mean_squared_error(inv_y, inv_yhatBest))\n",
        "print('Test RMSE: %.3f' % rmse2)\n",
        "\n",
        "\n",
        "#visualise the results\n",
        "plt.plot(inv_y, color='red', label='real exits')\n",
        "plt.plot(inv_yhatBest, color='blue', label='predicted exits')\n",
        "plt.title('Store Exits Prediction')\n",
        "plt.xlabel('Time')\n",
        "plt.ylabel('Exits')\n",
        "plt.legend()\n",
        "plt.show()"
      ],
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/sklearn/utils/validation.py:761: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  y = column_or_1d(y, warn=True)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Test RMSE: 14.306\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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8+GC5r1MaQpELBAKBE9gUtxqaq8gT4sFicbFUgvzccstg+va9odjPzGYzP/64oNpkefXV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\neePqfgAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 576x396 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "PFz9oCW_9_23",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "# LSTM"
      ]
    },
    {
      "metadata": {
        "id": "FE8Do6Av-CLt",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "##############################################################################\n",
        "#LSTM take 1\n",
        "##############################################################################\n",
        "\n",
        "def convert_timeseries(data, n_in=1, n_out=1, dropnan=True):\n",
        "\t#covert timeseries data to t-n to t-1 form\n",
        "\t#n defines how many previous value should be taken into consideration\n",
        "\tn_vars = 1 if type(data) is list else data.shape[1]\n",
        "\tdf = pd.DataFrame(data)\n",
        "\tcols, names = list(), list()\n",
        "\t# input sequence (t-n, ... t-1)\n",
        "\tfor i in range(n_in, 0, -1):\n",
        "\t\tcols.append(df.shift(i))\n",
        "\t\tnames += [('var%d(t-%d)' % (j+1, i)) for j in range(n_vars)]\n",
        "\t# forecast sequence (t, t+1, ... t+n)\n",
        "\tfor i in range(0, n_out):\n",
        "\t\tcols.append(df.shift(-i))\n",
        "\t\tif i == 0:\n",
        "\t\t\tnames += [('var%d(t)' % (j+1)) for j in range(n_vars)]\n",
        "\t\telse:\n",
        "\t\t\tnames += [('var%d(t+%d)' % (j+1, i)) for j in range(n_vars)]\n",
        "\t# put it all together\n",
        "\tagg = pd.concat(cols, axis=1)\n",
        "\tagg.columns = names\n",
        "\t# drop rows with NaN values\n",
        "\tif dropnan:\n",
        "\t\tagg.dropna(inplace=True)\n",
        "\treturn agg\n",
        "\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "hLKg3ZE3-K8m",
        "colab_type": "code",
        "outputId": "b73df146-7a5e-4a4e-d6b6-f4154d19b64f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 357
        }
      },
      "cell_type": "code",
      "source": [
        "# import the appropriate library\n",
        "from sklearn.preprocessing import MinMaxScaler\n",
        "\n",
        "values = model_data.values\n",
        "\n",
        "values = values.astype('float32')\n",
        "# normalize features\n",
        "mm_scaler = MinMaxScaler(feature_range=(0, 1))\n",
        "scaled = mm_scaler.fit_transform(values)\n",
        "# use predefined function to store previous rows values of each input in separate cols\n",
        "reframed = convert_timeseries(scaled, 1, 1)\n",
        "\n",
        "print(reframed.head())"
      ],
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "   var1(t-1)  var2(t-1)  var3(t-1)  var4(t-1)  var5(t-1)  var6(t-1)  \\\n",
            "1   0.629630   0.360215   0.046196   0.430737   0.301413   0.142857   \n",
            "2   0.740741   0.602151   0.411826   0.583675   0.187705   0.142857   \n",
            "3   0.740741   0.596774   0.638016   0.493389   0.220753   0.285714   \n",
            "4   0.629630   0.376344   0.337792   1.000000   0.237368   0.285714   \n",
            "5   0.703704   0.677419   0.601363   0.412170   0.110906   0.380952   \n",
            "\n",
            "   var7(t-1)  var8(t-1)  var9(t-1)   var1(t)   var2(t)   var3(t)   var4(t)  \\\n",
            "1   0.190476        0.0   0.093003  0.740741  0.602151  0.411826  0.583675   \n",
            "2   0.285714        0.0   0.075780  0.740741  0.596774  0.638016  0.493389   \n",
            "3   0.333333        0.0   0.047413  0.629630  0.376344  0.337792  1.000000   \n",
            "4   0.380952        0.0   0.000000  0.703704  0.677419  0.601363  0.412170   \n",
            "5   0.428571        0.0   0.051189  0.583333  0.317204  0.275226  0.571129   \n",
            "\n",
            "    var5(t)   var6(t)   var7(t)   var8(t)   var9(t)  \n",
            "1  0.187705  0.142857  0.285714  0.000000  0.075780  \n",
            "2  0.220753  0.285714  0.333333  0.000000  0.047413  \n",
            "3  0.237368  0.285714  0.380952  0.000000  0.000000  \n",
            "4  0.110906  0.380952  0.428571  0.000000  0.051189  \n",
            "5  0.265390  0.190476  0.095238  0.081169  0.051189  \n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "J6M5iLsY-1Ff",
        "colab_type": "code",
        "outputId": "883470b0-ee7a-4fa2-d596-7badd5e7b9aa",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 238
        }
      },
      "cell_type": "code",
      "source": [
        "#drop the current values from the predictor variables\n",
        "reframed.drop(reframed.columns[[9,11,12,13,14,15,16,17]], axis=1, inplace=True)\n",
        "print(reframed.head())"
      ],
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "   var1(t-1)  var2(t-1)  var3(t-1)  var4(t-1)  var5(t-1)  var6(t-1)  \\\n",
            "1   0.629630   0.360215   0.046196   0.430737   0.301413   0.142857   \n",
            "2   0.740741   0.602151   0.411826   0.583675   0.187705   0.142857   \n",
            "3   0.740741   0.596774   0.638016   0.493389   0.220753   0.285714   \n",
            "4   0.629630   0.376344   0.337792   1.000000   0.237368   0.285714   \n",
            "5   0.703704   0.677419   0.601363   0.412170   0.110906   0.380952   \n",
            "\n",
            "   var7(t-1)  var8(t-1)  var9(t-1)   var2(t)  \n",
            "1   0.190476        0.0   0.093003  0.602151  \n",
            "2   0.285714        0.0   0.075780  0.596774  \n",
            "3   0.333333        0.0   0.047413  0.376344  \n",
            "4   0.380952        0.0   0.000000  0.677419  \n",
            "5   0.428571        0.0   0.051189  0.317204  \n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "dvEbnHfq-x_y",
        "colab_type": "code",
        "outputId": "745151d2-3dfb-4d26-cc16-c765ac789599",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "cell_type": "code",
      "source": [
        "values = reframed.values\n",
        "#leave last 30 days for test\n",
        "train = values[:111, :]\n",
        "test = values[111:, :]\n",
        "# split into input and outputs\n",
        "train_X= train[:, :-1]\n",
        "train_y= train[:, -1]\n",
        "test_X= test[:, :-1]\n",
        "test_y= test[:, -1]\n",
        "# reshape input to be 3D [samples, timesteps, features]\n",
        "train_X = train_X.reshape((train_X.shape[0], 1, train_X.shape[1]))\n",
        "test_X = test_X.reshape((test_X.shape[0], 1, test_X.shape[1]))\n",
        "print(train_X.shape, train_y.shape, test_X.shape, test_y.shape)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(111, 1, 9) (111,) (30, 1, 9) (30,)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "B-7NiTLGC0qL",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "ab7f83e2-ed10-4acd-9ed9-8df1a5860b40"
      },
      "cell_type": "code",
      "source": [
        "#import the keras libraries\n",
        "#sequential model for RNN structure\n",
        "from keras.models import Sequential\n",
        "#Dense for output layer\n",
        "from keras.layers import Dense\n",
        "#For LSTM layers\n",
        "from keras.layers import LSTM\n",
        "#For dropout regularisation to prevent overfitting\n",
        "from keras.layers import Dropout"
      ],
      "execution_count": 33,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Using TensorFlow backend.\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "metadata": {
        "id": "sRZpD7S2C1fA",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "#build the lstm network\n",
        "lstm_model = Sequential()\n",
        "lstm_model.add(LSTM(50, return_sequences=True, input_shape=(train_X.shape[1], train_X.shape[2])))\n",
        "lstm_model.add(Dropout(rate=0.2))\n",
        "lstm_model.add(LSTM(units=50, return_sequences=True))\n",
        "lstm_model.add(Dropout(rate=0.2))\n",
        "lstm_model.add(LSTM(units=50))\n",
        "lstm_model.add(Dense(1))\n",
        "lstm_model.compile(loss='mae', optimizer='adam')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "8WeBN1ajC_k7",
        "colab_type": "code",
        "outputId": "b23413eb-d819-4792-87b5-231ede8ff091",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 2064
        }
      },
      "cell_type": "code",
      "source": [
        "# fit network\n",
        "history = lstm_model.fit(train_X, train_y, epochs=50, batch_size=1, validation_data=(test_X, test_y), verbose=2, shuffle=False)\n",
        "# plot history\n",
        "plt.plot(history.history['loss'], label='Training Loss')\n",
        "plt.plot(history.history['val_loss'], label='Validation Loss')\n",
        "plt.legend()\n",
        "plt.show()\n"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Train on 111 samples, validate on 30 samples\n",
            "Epoch 1/50\n",
            " - 3s - loss: 0.2003 - val_loss: 0.1245\n",
            "Epoch 2/50\n",
            " - 1s - loss: 0.1088 - val_loss: 0.1260\n",
            "Epoch 3/50\n",
            " - 1s - loss: 0.1105 - val_loss: 0.1303\n",
            "Epoch 4/50\n",
            " - 1s - loss: 0.1077 - val_loss: 0.1249\n",
            "Epoch 5/50\n",
            " - 1s - loss: 0.1085 - val_loss: 0.1257\n",
            "Epoch 6/50\n",
            " - 1s - loss: 0.1111 - val_loss: 0.1273\n",
            "Epoch 7/50\n",
            " - 1s - loss: 0.1076 - val_loss: 0.1274\n",
            "Epoch 8/50\n",
            " - 1s - loss: 0.1028 - val_loss: 0.1241\n",
            "Epoch 9/50\n",
            " - 1s - loss: 0.1020 - val_loss: 0.1221\n",
            "Epoch 10/50\n",
            " - 1s - loss: 0.1038 - val_loss: 0.1230\n",
            "Epoch 11/50\n",
            " - 1s - loss: 0.1007 - val_loss: 0.1209\n",
            "Epoch 12/50\n",
            " - 1s - loss: 0.0988 - val_loss: 0.1210\n",
            "Epoch 13/50\n",
            " - 1s - loss: 0.0996 - val_loss: 0.1208\n",
            "Epoch 14/50\n",
            " - 1s - loss: 0.1035 - val_loss: 0.1206\n",
            "Epoch 15/50\n",
            " - 1s - loss: 0.0986 - val_loss: 0.1200\n",
            "Epoch 16/50\n",
            " - 1s - loss: 0.0985 - val_loss: 0.1206\n",
            "Epoch 17/50\n",
            " - 1s - loss: 0.1014 - val_loss: 0.1234\n",
            "Epoch 18/50\n",
            " - 1s - loss: 0.0984 - val_loss: 0.1211\n",
            "Epoch 19/50\n",
            " - 1s - loss: 0.0976 - val_loss: 0.1207\n",
            "Epoch 20/50\n",
            " - 1s - loss: 0.0992 - val_loss: 0.1210\n",
            "Epoch 21/50\n",
            " - 1s - loss: 0.0997 - val_loss: 0.1207\n",
            "Epoch 22/50\n",
            " - 1s - loss: 0.1000 - val_loss: 0.1210\n",
            "Epoch 23/50\n",
            " - 1s - loss: 0.0976 - val_loss: 0.1215\n",
            "Epoch 24/50\n",
            " - 1s - loss: 0.0989 - val_loss: 0.1210\n",
            "Epoch 25/50\n",
            " - 1s - loss: 0.0976 - val_loss: 0.1206\n",
            "Epoch 26/50\n",
            " - 1s - loss: 0.0993 - val_loss: 0.1202\n",
            "Epoch 27/50\n",
            " - 1s - loss: 0.1005 - val_loss: 0.1208\n",
            "Epoch 28/50\n",
            " - 1s - loss: 0.0975 - val_loss: 0.1202\n",
            "Epoch 29/50\n",
            " - 1s - loss: 0.0990 - val_loss: 0.1214\n",
            "Epoch 30/50\n",
            " - 1s - loss: 0.0979 - val_loss: 0.1208\n",
            "Epoch 31/50\n",
            " - 1s - loss: 0.1003 - val_loss: 0.1223\n",
            "Epoch 32/50\n",
            " - 1s - loss: 0.0980 - val_loss: 0.1204\n",
            "Epoch 33/50\n",
            " - 1s - loss: 0.1004 - val_loss: 0.1224\n",
            "Epoch 34/50\n",
            " - 1s - loss: 0.0956 - val_loss: 0.1210\n",
            "Epoch 35/50\n",
            " - 1s - loss: 0.0978 - val_loss: 0.1209\n",
            "Epoch 36/50\n",
            " - 1s - loss: 0.1009 - val_loss: 0.1215\n",
            "Epoch 37/50\n",
            " - 1s - loss: 0.1008 - val_loss: 0.1204\n",
            "Epoch 38/50\n",
            " - 1s - loss: 0.0993 - val_loss: 0.1206\n",
            "Epoch 39/50\n",
            " - 1s - loss: 0.0993 - val_loss: 0.1205\n",
            "Epoch 40/50\n",
            " - 1s - loss: 0.1006 - val_loss: 0.1195\n",
            "Epoch 41/50\n",
            " - 1s - loss: 0.1007 - val_loss: 0.1218\n",
            "Epoch 42/50\n",
            " - 1s - loss: 0.0987 - val_loss: 0.1197\n",
            "Epoch 43/50\n",
            " - 1s - loss: 0.1008 - val_loss: 0.1198\n",
            "Epoch 44/50\n",
            " - 1s - loss: 0.0992 - val_loss: 0.1207\n",
            "Epoch 45/50\n",
            " - 1s - loss: 0.0982 - val_loss: 0.1199\n",
            "Epoch 46/50\n",
            " - 1s - loss: 0.0983 - val_loss: 0.1203\n",
            "Epoch 47/50\n",
            " - 1s - loss: 0.1013 - val_loss: 0.1224\n",
            "Epoch 48/50\n",
            " - 1s - loss: 0.0987 - val_loss: 0.1201\n",
            "Epoch 49/50\n",
            " - 1s - loss: 0.1017 - val_loss: 0.1217\n",
            "Epoch 50/50\n",
            " - 1s - loss: 0.1008 - val_loss: 0.1240\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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Ord3asgiJEEIIH+PH4Szd2kIIIXyT34azLEIihBDCV/ltOEvLWQghhK/SduVFTz31FJmZ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\n/wDHsvxJ5cAFDAAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 576x396 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "9ig3yg9IyJwA",
        "colab_type": "code",
        "outputId": "867cd569-02d4-42bd-c3f4-6545eccdb4f7",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1258
        }
      },
      "cell_type": "code",
      "source": [
        "# make a prediction\n",
        "yhat = lstm_model.predict(test_X)\n",
        "test_X = test_X.reshape((test_X.shape[0], test_X.shape[2]))\n",
        "# invert scaling for forecast to revert data into original form\n",
        "inv_yhat = np.concatenate((test_X[:,0].reshape(-1,1), yhat, test_X[:, 2:]), axis=1)\n",
        "inv_yhat = mm_scaler.inverse_transform(inv_yhat)\n",
        "# Actual Input\n",
        "inv_xp=np.delete(inv_yhat,[1], axis = 1)\n",
        "#predicted output\n",
        "inv_yhat = inv_yhat[:,1]\n",
        "# invert scaling for actual\n",
        "test_y = test_y.reshape((len(test_y), 1))\n",
        "inv_y = np.concatenate((test_X[:,0].reshape(-1,1),test_y, test_X[:, 2:]), axis=1)\n",
        "inv_y = mm_scaler.inverse_transform(inv_y)\n",
        "#Actual output\n",
        "inv_y = inv_y[:,1]\n",
        "print(\"Actial Input:\")\n",
        "print(inv_xp)\n",
        "print(\"Actual output:\")\n",
        "print(inv_y)\n",
        "#predicted output will be offset by 1\n",
        "print(\"Predicted output:\")\n",
        "print(inv_yhat)\n"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Actial Input:\n",
            "[[ 9.80000000e+01  2.31584692e+03  1.65973083e+03 -8.61509399e+01\n",
            "   3.00000000e+01  2.70000000e+01  0.00000000e+00  1.07202430e+05]\n",
            " [ 8.50000000e+01  3.54336450e+03  1.71163184e+03 -8.62829361e+01\n",
            "   3.00000000e+01  2.30000000e+01  6.99999988e-01  1.08614867e+05]\n",
            " [ 8.80000000e+01  5.50182959e+03  1.90239722e+03 -8.61668930e+01\n",
            "   2.30000000e+01  2.10000000e+01  4.09999990e+00  1.08594453e+05]\n",
            " [ 7.80000000e+01  3.60634595e+03  1.61191907e+03 -8.53140488e+01\n",
            "   2.70000000e+01  2.40000000e+01  1.00000001e-01  1.08952727e+05]\n",
            " [ 8.40000000e+01  3.00353564e+03  1.72651843e+03 -8.64812698e+01\n",
            "   2.70000000e+01  2.50000000e+01  0.00000000e+00  1.09153906e+05]\n",
            " [ 1.03000000e+02  3.45549512e+03  1.81541736e+03 -8.75453033e+01\n",
            "   2.70000000e+01  2.40000000e+01  0.00000000e+00  1.09755141e+05]\n",
            " [ 8.70000000e+01  2.26887354e+03  1.97574438e+03 -8.66130829e+01\n",
            "   2.70000000e+01  2.40000000e+01  0.00000000e+00  1.09755141e+05]\n",
            " [ 5.70000000e+01  3.54464893e+03  1.90899097e+03 -8.66519012e+01\n",
            "   2.70000000e+01  2.50000000e+01  0.00000000e+00  1.09755141e+05]\n",
            " [ 7.80000000e+01  2.23860254e+03  2.07902979e+03 -8.79032288e+01\n",
            "   2.80000000e+01  2.50000000e+01  0.00000000e+00  1.10339992e+05]\n",
            " [ 6.70000000e+01  4.23204492e+03  1.91719409e+03 -8.68711929e+01\n",
            "   2.89999981e+01  2.40000000e+01  3.00000012e-01  1.10423109e+05]\n",
            " [ 7.00000000e+01  1.73527148e+03  1.83819824e+03 -8.68355408e+01\n",
            "   2.40000000e+01  2.10000000e+01  1.00000000e+00  1.10423109e+05]\n",
            " [ 7.10000000e+01  2.40367603e+03  1.76187939e+03 -8.67021255e+01\n",
            "   2.50000000e+01  2.10000000e+01  2.00000003e-01  1.10010492e+05]\n",
            " [ 3.80000000e+01  2.89413159e+03  1.76309314e+03 -8.66828232e+01\n",
            "   2.60000000e+01  2.10000000e+01  0.00000000e+00  1.10010492e+05]\n",
            " [ 3.90000000e+01  1.69105127e+03  1.92233105e+03 -8.67169800e+01\n",
            "   2.89999981e+01  2.40000000e+01  0.00000000e+00  1.10010492e+05]\n",
            " [ 5.60000000e+01  2.29787500e+03  1.74235913e+03 -8.61070480e+01\n",
            "   3.40000000e+01  2.50000000e+01  0.00000000e+00  1.10010492e+05]\n",
            " [ 8.80000000e+01  2.77337500e+03  1.66941296e+03 -8.66653366e+01\n",
            "   2.70000000e+01  2.40000000e+01  0.00000000e+00  1.10010492e+05]\n",
            " [ 1.08999992e+02  2.77362378e+03  1.60679626e+03 -8.63556747e+01\n",
            "   2.60000000e+01  2.30000000e+01  0.00000000e+00  1.08872984e+05]\n",
            " [ 1.04000000e+02  3.81930762e+03  1.95399280e+03 -8.67151642e+01\n",
            "   2.40000000e+01  2.10000000e+01  0.00000000e+00  1.09611781e+05]\n",
            " [ 1.13000008e+02  3.16836279e+03  1.89609058e+03 -8.64179688e+01\n",
            "   2.80000000e+01  2.10000000e+01  0.00000000e+00  1.09742953e+05]\n",
            " [ 9.70000000e+01  2.96710303e+03  1.64773438e+03 -8.53080368e+01\n",
            "   3.09999981e+01  2.50000000e+01  0.00000000e+00  1.08451703e+05]\n",
            " [ 1.13000008e+02  3.20683179e+03  1.75191223e+03 -8.67739258e+01\n",
            "   3.20000000e+01  2.60000000e+01  0.00000000e+00  1.08451703e+05]\n",
            " [ 8.90000000e+01  2.85486523e+03  1.83922522e+03 -8.61687469e+01\n",
            "   3.40000000e+01  2.70000000e+01  0.00000000e+00  1.08451703e+05]\n",
            " [ 9.20000000e+01  2.60284790e+03  1.74927222e+03 -8.64368896e+01\n",
            "   3.50000000e+01  2.80000000e+01  0.00000000e+00  1.09458008e+05]\n",
            " [ 1.10000008e+02  2.42713647e+03  1.67562976e+03 -8.56260605e+01\n",
            "   3.20000000e+01  2.89999981e+01  0.00000000e+00  1.09129648e+05]\n",
            " [ 9.70000000e+01  2.03217529e+03  1.77317517e+03 -8.69597092e+01\n",
            "   3.00000000e+01  2.50000000e+01  0.00000000e+00  1.08155531e+05]\n",
            " [ 1.00000000e+02  3.20204004e+03  1.70328821e+03 -8.64540100e+01\n",
            "   2.80000000e+01  2.40000000e+01  0.00000000e+00  1.08244219e+05]\n",
            " [ 1.11000000e+02  3.27760352e+03  1.49555286e+03 -8.70157471e+01\n",
            "   2.80000000e+01  2.50000000e+01  0.00000000e+00  1.07741703e+05]\n",
            " [ 8.90000000e+01  2.45098877e+03  1.69159338e+03 -8.59134216e+01\n",
            "   2.80000000e+01  2.30000000e+01  0.00000000e+00  1.07741703e+05]\n",
            " [ 1.10000008e+02  1.99608179e+03  1.75310547e+03 -8.59969025e+01\n",
            "   3.00000000e+01  2.30000000e+01  0.00000000e+00  1.07741703e+05]\n",
            " [ 1.07000000e+02  2.99758887e+03  1.78136304e+03 -8.60919647e+01\n",
            "   3.09999981e+01  2.70000000e+01  0.00000000e+00  1.06534602e+05]]\n",
            "Actual output:\n",
            "[ 85.      109.99999 104.       80.      136.       86.       72.\n",
            "  83.       91.       57.       83.       47.       37.       59.\n",
            " 118.      120.99999 120.      128.       95.      153.       94.\n",
            " 118.      135.       89.       75.      118.      106.      119.\n",
            " 135.      107.     ]\n",
            "Predicted output:\n",
            "[88.723274 84.58697  86.76319  86.1271   86.6476   91.35337  89.07455\n",
            " 80.938286 85.20175  80.925316 88.123985 87.99159  79.73534  77.76934\n",
            " 77.082146 88.90964  95.9439   95.14531  95.21452  87.1675   90.17383\n",
            " 82.61216  82.41626  88.752014 89.26986  89.46926  92.50482  90.74217\n",
            " 95.43318  90.624565]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "73SHpDg7yVF-",
        "colab_type": "code",
        "outputId": "30df60c2-b2f8-4c07-f6ad-867157acee22",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "cell_type": "code",
      "source": [
        "# calculate RMSE\n",
        "rmse = math.sqrt(mean_squared_error(inv_y, inv_yhat))\n",
        "print('Test RMSE: %.3f' % rmse)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Test RMSE: 28.755\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "E2zxTC0oyXAn",
        "colab_type": "code",
        "outputId": "b0352980-c6cd-4927-f0e7-b81654789071",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 376
        }
      },
      "cell_type": "code",
      "source": [
        "#visualise the results\n",
        "plt.plot(inv_y, color='red', label='real exits')\n",
        "plt.plot(inv_yhat, color='blue', label='predicted exits')\n",
        "plt.title('Store Exits Prediction')\n",
        "plt.xlabel('Time')\n",
        "plt.ylabel('Exits')\n",
        "plt.legend()\n",
        "plt.show()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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QhEiEoJ1j+ulHOtxzO5Yf1mH+1b/ekdEwxYNkN3AafEnXncY8iDS7re5C1JJ7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\nAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 576x396 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "HXpPVLQIzlzR",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "# Take 2 at LSTM\n"
      ]
    },
    {
      "metadata": {
        "id": "M008amSRzlD6",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "x_data = model_data.drop(['Exits'], axis =1)\n",
        "y_data = model_data['Exits']\n",
        "x_values = x_data.values\n",
        "x_values = x_values.astype('float32')\n",
        "y_values = y_data.values\n",
        "y_values = y_values.astype('float32')\n",
        "x_scaler = MinMaxScaler(feature_range=(0, 1))\n",
        "y_scaler = MinMaxScaler(feature_range=(0, 1))\n",
        "x_scaled = x_scaler.fit_transform(x_values)\n",
        "y_scaled = y_scaler.fit_transform(y_values.reshape(-1,1))"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "Eohu9pOQz1rP",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "X_train1 = []\n",
        "X_train2=[]\n",
        "X_train3=[]\n",
        "X_train4=[]\n",
        "X_train5=[]\n",
        "X_train6=[]\n",
        "X_train7=[]\n",
        "X_train8=[]\n",
        "Y_train = []\n",
        "\n",
        "for i in range(7,112):\n",
        "    X_train1.append(x_scaled[i-7:i,0])\n",
        "    X_train2.append(x_scaled[i-7:i,1])\n",
        "    X_train3.append(x_scaled[i-7:i,2])\n",
        "    X_train4.append(x_scaled[i-7:i,3])\n",
        "    X_train5.append(x_scaled[i-7:i,4])\n",
        "    X_train6.append(x_scaled[i-7:i,5])\n",
        "    X_train7.append(x_scaled[i-7:i,6])\n",
        "    X_train8.append(x_scaled[i-7:i,7])\n",
        "    Y_train.append(y_scaled[i,0])\n",
        "    \n",
        "X_train1, X_train2, X_train3, X_train4, X_train5, X_train6, X_train7, X_train8, Y_train =np.array(X_train1), np.array(X_train2), np.array(X_train3), np.array(X_train4), np.array(X_train5), np.array(X_train6), np.array(X_train7), np.array(X_train8), np.array(Y_train)\n",
        "\n",
        "X_trainComb = np.stack([X_train1,X_train2,X_train3,X_train4,X_train5,X_train6,X_train7,X_train8],axis=2)\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "clIaSgp6z55k",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "#format test data as we did for training set\n",
        "X_test1 = []\n",
        "X_test2 = []\n",
        "X_test3 = []\n",
        "X_test4 = []\n",
        "X_test5 = []\n",
        "X_test6 = []\n",
        "X_test7 = []\n",
        "X_test8 = []\n",
        "for i in range(112,142):\n",
        "    X_test1.append(x_scaled[i-7:i,0])\n",
        "    X_test2.append(x_scaled[i-7:i,1])\n",
        "    X_test3.append(x_scaled[i-7:i,2])\n",
        "    X_test4.append(x_scaled[i-7:i,3])\n",
        "    X_test5.append(x_scaled[i-7:i,4])\n",
        "    X_test6.append(x_scaled[i-7:i,5])\n",
        "    X_test7.append(x_scaled[i-7:i,6])\n",
        "    X_test8.append(x_scaled[i-7:i,7])\n",
        "   \n",
        "X_test1, X_test2, X_test3, X_test4, X_test5, X_test6, X_test7, X_test8 =np.array(X_test1), np.array(X_test2), np.array(X_test3), np.array(X_test4), np.array(X_test5), np.array(X_test6), np.array(X_test7), np.array(X_test8)\n",
        "#X_test = np.reshape(X_test, (X_test.shape[0],X_test.shape[1],1))\n",
        "X_testcomb = np.stack([X_test1,X_test2,X_test3,X_test4,X_test5,X_test6,X_test7,X_test8],axis=2)\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "uR4GMAwAz9xq",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "y_test = model_data.iloc[112:,1]"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "FV66wszl0CGu",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "lstm_model2 = Sequential()\n",
        "lstm_model2.add(LSTM(units=50, return_sequences = True, input_shape = (X_trainComb.shape[1],8)))\n",
        "#adding dropout reg - number of neurons to ignore @ each pass in training - standard value is 0.2\n",
        "lstm_model.add(Dropout(rate=0.2))\n",
        "\n",
        "#Adding second LSTM layers with dropout reg - don't need to specify shape - picks it up from 1st layer\n",
        "lstm_model2.add(LSTM(units=50, return_sequences = True))\n",
        "lstm_model2.add(Dropout(rate=0.2))\n",
        "\n",
        "#Adding third LSTM layers with dropout reg\n",
        "lstm_model2.add(LSTM(units=50, return_sequences = True))\n",
        "lstm_model2.add(Dropout(rate=0.2))\n",
        "\n",
        "#Adding fourth LSTM layers with dropout reg - as last layer we don't want to return a sequence\n",
        "lstm_model2.add(LSTM(units=50))\n",
        "lstm_model2.add(Dropout(rate=0.2))\n",
        "\n",
        "#Add the output layer\n",
        "lstm_model2.add(Dense(units=1))"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "AF-gawNh0JLX",
        "colab_type": "code",
        "outputId": "f999a61b-ca04-4121-fd8d-02ea3015e8f6",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 731
        }
      },
      "cell_type": "code",
      "source": [
        "lstm_model2.compile(optimizer='adam', loss='mse')\n",
        "\n",
        "#fitting the RNN to the training set\n",
        "#submit data in batches of 32\n",
        "lstm_model2.fit(x=X_trainComb, y=Y_train,epochs=20,batch_size=1, validation_data = (X_testcomb, y_test))\n"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Train on 105 samples, validate on 30 samples\n",
            "Epoch 1/20\n",
            "105/105 [==============================] - 8s 73ms/step - loss: 0.0369 - val_loss: 10449.0173\n",
            "Epoch 2/20\n",
            "105/105 [==============================] - 2s 19ms/step - loss: 0.0241 - val_loss: 10449.1192\n",
            "Epoch 3/20\n",
            "105/105 [==============================] - 2s 19ms/step - loss: 0.0236 - val_loss: 10430.9856\n",
            "Epoch 4/20\n",
            "105/105 [==============================] - 2s 19ms/step - loss: 0.0206 - val_loss: 10460.9881\n",
            "Epoch 5/20\n",
            "105/105 [==============================] - 2s 19ms/step - loss: 0.0210 - val_loss: 10425.7269\n",
            "Epoch 6/20\n",
            "105/105 [==============================] - 2s 19ms/step - loss: 0.0196 - val_loss: 10447.9715\n",
            "Epoch 7/20\n",
            "105/105 [==============================] - 2s 19ms/step - loss: 0.0212 - val_loss: 10441.0820\n",
            "Epoch 8/20\n",
            "105/105 [==============================] - 2s 19ms/step - loss: 0.0199 - val_loss: 10437.2164\n",
            "Epoch 9/20\n",
            "105/105 [==============================] - 2s 19ms/step - loss: 0.0211 - val_loss: 10447.1529\n",
            "Epoch 10/20\n",
            "105/105 [==============================] - 2s 20ms/step - loss: 0.0200 - val_loss: 10439.7327\n",
            "Epoch 11/20\n",
            "105/105 [==============================] - 2s 20ms/step - loss: 0.0194 - val_loss: 10450.8430\n",
            "Epoch 12/20\n",
            "105/105 [==============================] - 2s 21ms/step - loss: 0.0184 - val_loss: 10454.1436\n",
            "Epoch 13/20\n",
            "105/105 [==============================] - 2s 20ms/step - loss: 0.0180 - val_loss: 10443.6717\n",
            "Epoch 14/20\n",
            "105/105 [==============================] - 2s 20ms/step - loss: 0.0177 - val_loss: 10440.4312\n",
            "Epoch 15/20\n",
            "105/105 [==============================] - 2s 19ms/step - loss: 0.0192 - val_loss: 10447.3086\n",
            "Epoch 16/20\n",
            "105/105 [==============================] - 2s 19ms/step - loss: 0.0184 - val_loss: 10448.5997\n",
            "Epoch 17/20\n",
            "105/105 [==============================] - 2s 19ms/step - loss: 0.0194 - val_loss: 10441.7156\n",
            "Epoch 18/20\n",
            "105/105 [==============================] - 2s 19ms/step - loss: 0.0179 - val_loss: 10435.2869\n",
            "Epoch 19/20\n",
            "105/105 [==============================] - 2s 19ms/step - loss: 0.0189 - val_loss: 10445.7902\n",
            "Epoch 20/20\n",
            "105/105 [==============================] - 2s 19ms/step - loss: 0.0178 - val_loss: 10436.6638\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<keras.callbacks.History at 0x7f80634f76a0>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 79
        }
      ]
    },
    {
      "metadata": {
        "id": "kswKVwQo0Qgh",
        "colab_type": "code",
        "outputId": "dba26c25-286d-4e59-9c12-55a7f4c59365",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 376
        }
      },
      "cell_type": "code",
      "source": [
        "#create predictions\n",
        "predicted_exits = lstm_model2.predict(X_testcomb)\n",
        "\n",
        "#reverse the scaling so the predictions are on the original scale\n",
        "predicted_exits = y_scaler.inverse_transform(predicted_exits)\n",
        "\n",
        "\n",
        "#visualise results\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "\n",
        "predicted_exits = pd.DataFrame(predicted_exits, index = y_test.index)\n",
        "\n",
        "plt.plot(y_test, color='red', label='real exits')\n",
        "plt.plot(predicted_exits, color='blue', label='predicted exits')\n",
        "plt.title('Store Exits Prediction')\n",
        "plt.xlabel('Time')\n",
        "plt.ylabel('Exits')\n",
        "plt.legend()\n",
        "plt.show()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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UOayeg+gl9x4pJQXbhnW4WrVBbdW6yP3cHauvw5sQ8hqKHG+4umk5Puu5GbkQ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\n+o3oFZAAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 576x396 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "Idx5EsAx0STg",
        "colab_type": "code",
        "outputId": "47838b30-dfa7-4a73-96d6-81bf15b7cb1f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "cell_type": "code",
      "source": [
        "import math\n",
        "from sklearn.metrics import mean_squared_error\n",
        "rmse = math.sqrt(mean_squared_error(y_test, predicted_exits))\n",
        "print('Test RMSE: %.3f' % rmse)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Test RMSE: 28.155\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "ADmK511z-RTz",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "#Take 3"
      ]
    },
    {
      "metadata": {
        "id": "I-Ocr1OO9qHN",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 119
        },
        "outputId": "8a332080-043a-4609-b10c-22fe7748b687"
      },
      "cell_type": "code",
      "source": [
        "# import the appropriate library\n",
        "from sklearn.preprocessing import MinMaxScaler\n",
        "\n",
        "\n",
        "#values = model_data[:,1]\n",
        "values = model_data['Exits'].values\n",
        "\n",
        "values = values.astype('float32')\n",
        "values = values.reshape(-1,1)\n",
        "# normalize features\n",
        "mm_scaler = MinMaxScaler(feature_range=(0, 1))\n",
        "scaled = mm_scaler.fit_transform(values)\n",
        "# use predefined function to store previous rows values of each input in separate cols\n",
        "reframed = convert_timeseries(scaled, 1, 1)\n",
        "\n",
        "print(reframed.head())\n",
        "#model_data['Exits']"
      ],
      "execution_count": 52,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "   var1(t-1)   var1(t)\n",
            "1   0.360215  0.602151\n",
            "2   0.602151  0.596774\n",
            "3   0.596774  0.376344\n",
            "4   0.376344  0.677419\n",
            "5   0.677419  0.317204\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "-nulG5717bdY",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "e07c1752-ac38-48f9-e2ac-53fc2b7468bd"
      },
      "cell_type": "code",
      "source": [
        "values = reframed.values\n",
        "#leave last 30 days for test\n",
        "train = values[:111, :]\n",
        "test = values[111:, :]\n",
        "# split into input and outputs\n",
        "train_X= train[:, 0]\n",
        "train_y= train[:, -1]\n",
        "test_X= test[:, 0]\n",
        "test_y= test[:, -1]\n",
        "# reshape input to be 3D [samples, timesteps, features]\n",
        "train_X = train_X.reshape((train_X.shape[0], 1, 1))\n",
        "test_X = test_X.reshape((test_X.shape[0], 1, 1))\n",
        "print(train_X.shape, train_y.shape, test_X.shape, test_y.shape)"
      ],
      "execution_count": 53,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(111, 1, 1) (111,) (30, 1, 1) (30,)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "fILdRap67bqZ",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "#build the lstm network\n",
        "lstm_model3 = Sequential()\n",
        "lstm_model3.add(LSTM(50, return_sequences=True, input_shape=(train_X.shape[1], train_X.shape[2])))\n",
        "lstm_model3.add(Dropout(rate=0.2))\n",
        "lstm_model3.add(LSTM(units=50, return_sequences=True))\n",
        "lstm_model3.add(Dropout(rate=0.2))\n",
        "lstm_model3.add(LSTM(units=50))\n",
        "lstm_model3.add(Dense(1))\n",
        "lstm_model3.compile(loss='mae', optimizer='adam')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "xydeQqbC7byz",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 2064
        },
        "outputId": "39ce1918-635c-4756-d83c-b268ea2d9031"
      },
      "cell_type": "code",
      "source": [
        "# fit network\n",
        "history = lstm_model3.fit(train_X, train_y, epochs=50, batch_size=1, validation_data=(test_X, test_y), verbose=2, shuffle=False)\n",
        "# plot history\n",
        "plt.plot(history.history['loss'], label='Training Loss')\n",
        "plt.plot(history.history['val_loss'], label='Validation Loss')\n",
        "plt.legend()\n",
        "plt.show()"
      ],
      "execution_count": 55,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Train on 111 samples, validate on 30 samples\n",
            "Epoch 1/50\n",
            " - 4s - loss: 0.2138 - val_loss: 0.1313\n",
            "Epoch 2/50\n",
            " - 1s - loss: 0.1097 - val_loss: 0.1281\n",
            "Epoch 3/50\n",
            " - 1s - loss: 0.1086 - val_loss: 0.1276\n",
            "Epoch 4/50\n",
            " - 1s - loss: 0.1079 - val_loss: 0.1239\n",
            "Epoch 5/50\n",
            " - 1s - loss: 0.1096 - val_loss: 0.1277\n",
            "Epoch 6/50\n",
            " - 1s - loss: 0.1139 - val_loss: 0.1283\n",
            "Epoch 7/50\n",
            " - 1s - loss: 0.1112 - val_loss: 0.1291\n",
            "Epoch 8/50\n",
            " - 1s - loss: 0.1122 - val_loss: 0.1260\n",
            "Epoch 9/50\n",
            " - 1s - loss: 0.1111 - val_loss: 0.1286\n",
            "Epoch 10/50\n",
            " - 1s - loss: 0.1147 - val_loss: 0.1276\n",
            "Epoch 11/50\n",
            " - 1s - loss: 0.1127 - val_loss: 0.1273\n",
            "Epoch 12/50\n",
            " - 1s - loss: 0.1142 - val_loss: 0.1274\n",
            "Epoch 13/50\n",
            " - 1s - loss: 0.1141 - val_loss: 0.1246\n",
            "Epoch 14/50\n",
            " - 1s - loss: 0.1116 - val_loss: 0.1263\n",
            "Epoch 15/50\n",
            " - 1s - loss: 0.1183 - val_loss: 0.1251\n",
            "Epoch 16/50\n",
            " - 1s - loss: 0.1139 - val_loss: 0.1240\n",
            "Epoch 17/50\n",
            " - 1s - loss: 0.1132 - val_loss: 0.1246\n",
            "Epoch 18/50\n",
            " - 1s - loss: 0.1157 - val_loss: 0.1260\n",
            "Epoch 19/50\n",
            " - 1s - loss: 0.1170 - val_loss: 0.1240\n",
            "Epoch 20/50\n",
            " - 1s - loss: 0.1162 - val_loss: 0.1248\n",
            "Epoch 21/50\n",
            " - 1s - loss: 0.1178 - val_loss: 0.1239\n",
            "Epoch 22/50\n",
            " - 1s - loss: 0.1185 - val_loss: 0.1228\n",
            "Epoch 23/50\n",
            " - 1s - loss: 0.1150 - val_loss: 0.1230\n",
            "Epoch 24/50\n",
            " - 1s - loss: 0.1179 - val_loss: 0.1224\n",
            "Epoch 25/50\n",
            " - 1s - loss: 0.1165 - val_loss: 0.1229\n",
            "Epoch 26/50\n",
            " - 1s - loss: 0.1178 - val_loss: 0.1225\n",
            "Epoch 27/50\n",
            " - 1s - loss: 0.1162 - val_loss: 0.1221\n",
            "Epoch 28/50\n",
            " - 1s - loss: 0.1139 - val_loss: 0.1225\n",
            "Epoch 29/50\n",
            " - 1s - loss: 0.1145 - val_loss: 0.1224\n",
            "Epoch 30/50\n",
            " - 1s - loss: 0.1182 - val_loss: 0.1227\n",
            "Epoch 31/50\n",
            " - 1s - loss: 0.1166 - val_loss: 0.1217\n",
            "Epoch 32/50\n",
            " - 1s - loss: 0.1148 - val_loss: 0.1213\n",
            "Epoch 33/50\n",
            " - 1s - loss: 0.1156 - val_loss: 0.1210\n",
            "Epoch 34/50\n",
            " - 1s - loss: 0.1129 - val_loss: 0.1212\n",
            "Epoch 35/50\n",
            " - 1s - loss: 0.1150 - val_loss: 0.1211\n",
            "Epoch 36/50\n",
            " - 1s - loss: 0.1153 - val_loss: 0.1210\n",
            "Epoch 37/50\n",
            " - 1s - loss: 0.1151 - val_loss: 0.1205\n",
            "Epoch 38/50\n",
            " - 1s - loss: 0.1140 - val_loss: 0.1204\n",
            "Epoch 39/50\n",
            " - 1s - loss: 0.1161 - val_loss: 0.1194\n",
            "Epoch 40/50\n",
            " - 1s - loss: 0.1119 - val_loss: 0.1198\n",
            "Epoch 41/50\n",
            " - 1s - loss: 0.1144 - val_loss: 0.1204\n",
            "Epoch 42/50\n",
            " - 1s - loss: 0.1126 - val_loss: 0.1199\n",
            "Epoch 43/50\n",
            " - 1s - loss: 0.1141 - val_loss: 0.1187\n",
            "Epoch 44/50\n",
            " - 1s - loss: 0.1100 - val_loss: 0.1198\n",
            "Epoch 45/50\n",
            " - 1s - loss: 0.1120 - val_loss: 0.1188\n",
            "Epoch 46/50\n",
            " - 1s - loss: 0.1112 - val_loss: 0.1196\n",
            "Epoch 47/50\n",
            " - 1s - loss: 0.1136 - val_loss: 0.1174\n",
            "Epoch 48/50\n",
            " - 1s - loss: 0.1122 - val_loss: 0.1163\n",
            "Epoch 49/50\n",
            " - 1s - loss: 0.1090 - val_loss: 0.1162\n",
            "Epoch 50/50\n",
            " - 1s - loss: 0.1104 - val_loss: 0.1153\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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gJHGhpsNhbSQuJJbUyGTUquMbM1fZXsMXlV9RZa2hrqMBp+J0b1OhwhQSQ2ZU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\nEREXo3AWERFxMf8HKfIrI19/e3UAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 576x396 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "ktnooWCC7b6s",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 748
        },
        "outputId": "022e0c0f-19ba-46e2-d1f2-4c3825d0060a"
      },
      "cell_type": "code",
      "source": [
        "# make a prediction\n",
        "yhat = lstm_model3.predict(test_X)\n",
        "test_X = test_X.reshape((test_X.shape[0], test_X.shape[2]))\n",
        "# invert scaling for forecast to revert data into original form\n",
        "inv_yhat = np.concatenate((test_X[:,0].reshape(-1,1), yhat, test_X[:, 2:]), axis=1)\n",
        "inv_yhat = mm_scaler.inverse_transform(inv_yhat)\n",
        "# Actual Input\n",
        "inv_xp=np.delete(inv_yhat,[1], axis = 1)\n",
        "#predicted output\n",
        "inv_yhat = inv_yhat[:,1]\n",
        "# invert scaling for actual\n",
        "test_y = test_y.reshape((len(test_y), 1))\n",
        "inv_y = np.concatenate((test_X[:,0].reshape(-1,1),test_y, test_X[:, 2:]), axis=1)\n",
        "inv_y = mm_scaler.inverse_transform(inv_y)\n",
        "#Actual output\n",
        "inv_y = inv_y[:,1]\n",
        "print(\"Actial Input:\")\n",
        "print(inv_xp)\n",
        "print(\"Actual output:\")\n",
        "print(inv_y)\n",
        "#predicted output will be offset by 1\n",
        "print(\"Predicted output:\")\n",
        "print(inv_yhat)"
      ],
      "execution_count": 56,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Actial Input:\n",
            "[[ 90.     ]\n",
            " [ 85.     ]\n",
            " [109.99999]\n",
            " [104.     ]\n",
            " [ 80.     ]\n",
            " [136.     ]\n",
            " [ 86.     ]\n",
            " [ 72.     ]\n",
            " [ 83.     ]\n",
            " [ 91.     ]\n",
            " [ 57.     ]\n",
            " [ 83.     ]\n",
            " [ 47.     ]\n",
            " [ 37.     ]\n",
            " [ 59.     ]\n",
            " [118.     ]\n",
            " [120.99999]\n",
            " [120.     ]\n",
            " [128.     ]\n",
            " [ 95.     ]\n",
            " [153.     ]\n",
            " [ 94.     ]\n",
            " [118.     ]\n",
            " [135.     ]\n",
            " [ 89.     ]\n",
            " [ 75.     ]\n",
            " [118.     ]\n",
            " [106.     ]\n",
            " [119.     ]\n",
            " [135.     ]]\n",
            "Actual output:\n",
            "[ 85.      109.99999 104.       80.      136.       86.       72.\n",
            "  83.       91.       57.       83.       47.       37.       59.\n",
            " 118.      120.99999 120.      128.       95.      153.       94.\n",
            " 118.      135.       89.       75.      118.      106.      119.\n",
            " 135.      107.     ]\n",
            "Predicted output:\n",
            "[ 91.1671    89.445526  97.268555  95.56396   87.639786 103.54826\n",
            "  89.796486  84.568375  88.7335    91.50156   78.16832   88.7335\n",
            "  73.40622   68.22265   79.07203   99.38516  100.13488   99.88757\n",
            " 101.7954    92.80735  106.804955  92.485664  99.38516  103.33729\n",
            "  90.82938   85.74702   99.38516   96.14367   99.637665 103.33729 ]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "f5k4veMbANBn",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "2d96ffe9-6b32-4cfc-c9c0-52630833a133"
      },
      "cell_type": "code",
      "source": [
        "# calculate RMSE\n",
        "rmse = math.sqrt(mean_squared_error(inv_y, inv_yhat))\n",
        "print('Test RMSE: %.3f' % rmse)"
      ],
      "execution_count": 57,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Test RMSE: 26.033\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "jnEKIgh2ANIx",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 376
        },
        "outputId": "f05767c9-1e95-4ff6-a189-e7f0202f004b"
      },
      "cell_type": "code",
      "source": [
        "#visualise the results\n",
        "plt.plot(inv_y, color='red', label='real exits')\n",
        "plt.plot(inv_yhat, color='blue', label='predicted exits')\n",
        "plt.title('Store Exits Prediction')\n",
        "plt.xlabel('Time')\n",
        "plt.ylabel('Exits')\n",
        "plt.legend()\n",
        "plt.show()"
      ],
      "execution_count": 58,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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Ypuee+xcFBfmkpKSwf/9ej69TGcKRCwQCQSXIKcloMbFocU3M3wV1mxtuGEqvXn3K3We3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\n",
            "text/plain": [
              "<Figure size 576x396 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    }
  ]
}