Your_first_neural_network.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "source": [
    "# Your first neural network\n",
    "\n",
    "In this project, you'll build your first neural network and use it to predict daily bike rental ridership. We've provided some of the code, but left the implementation of the neural network up to you (for the most part). After you've submitted this project, feel free to explore the data and the model more.\n",
    "\n"
   ],
   "metadata": {
    "editable": true
   }
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "source": [
    "%matplotlib inline\r\n",
    "%load_ext autoreload\r\n",
    "%autoreload 2\r\n",
    "%config InlineBackend.figure_format = 'retina'\r\n",
    "\r\n",
    "import numpy as np\r\n",
    "import pandas as pd\r\n",
    "import matplotlib.pyplot as plt\r\n",
    "\r\n",
    "print(pd.__version__)"
   ],
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "1.0.4\n"
     ]
    }
   ],
   "metadata": {
    "collapsed": true,
    "editable": true,
    "jupyter": {
     "outputs_hidden": true
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "source": [
    "pd.set_option('display.max_rows', None)\r\n",
    "pd.set_option('display.max_columns', None)\r\n",
    "# pd.set_option('display.width', None)\r\n",
    "# pd.set_option('display.max_colwidth', None)"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "## Load and prepare the data\n",
    "\n",
    "A critical step in working with neural networks is preparing the data correctly. Variables on different scales make it difficult for the network to efficiently learn the correct weights. Below, we've written the code to load and prepare the data. You'll learn more about this soon!"
   ],
   "metadata": {
    "editable": true
   }
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "source": [
    "data_path = 'Bike-Sharing-Dataset/hour.csv'\r\n",
    "\r\n",
    "rides = pd.read_csv(data_path)"
   ],
   "outputs": [],
   "metadata": {
    "collapsed": true,
    "editable": true,
    "jupyter": {
     "outputs_hidden": true
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "source": [
    "rides.head()"
   ],
   "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>instant</th>\n",
       "      <th>dteday</th>\n",
       "      <th>season</th>\n",
       "      <th>yr</th>\n",
       "      <th>mnth</th>\n",
       "      <th>hr</th>\n",
       "      <th>holiday</th>\n",
       "      <th>weekday</th>\n",
       "      <th>workingday</th>\n",
       "      <th>weathersit</th>\n",
       "      <th>temp</th>\n",
       "      <th>atemp</th>\n",
       "      <th>hum</th>\n",
       "      <th>windspeed</th>\n",
       "      <th>casual</th>\n",
       "      <th>registered</th>\n",
       "      <th>cnt</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>2011-01-01</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.24</td>\n",
       "      <td>0.2879</td>\n",
       "      <td>0.81</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3</td>\n",
       "      <td>13</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>2011-01-01</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.22</td>\n",
       "      <td>0.2727</td>\n",
       "      <td>0.80</td>\n",
       "      <td>0.0</td>\n",
       "      <td>8</td>\n",
       "      <td>32</td>\n",
       "      <td>40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>2011-01-01</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.22</td>\n",
       "      <td>0.2727</td>\n",
       "      <td>0.80</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5</td>\n",
       "      <td>27</td>\n",
       "      <td>32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>2011-01-01</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.24</td>\n",
       "      <td>0.2879</td>\n",
       "      <td>0.75</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3</td>\n",
       "      <td>10</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>2011-01-01</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.24</td>\n",
       "      <td>0.2879</td>\n",
       "      <td>0.75</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   instant      dteday  season  yr  mnth  hr  holiday  weekday  workingday  \\\n",
       "0        1  2011-01-01       1   0     1   0        0        6           0   \n",
       "1        2  2011-01-01       1   0     1   1        0        6           0   \n",
       "2        3  2011-01-01       1   0     1   2        0        6           0   \n",
       "3        4  2011-01-01       1   0     1   3        0        6           0   \n",
       "4        5  2011-01-01       1   0     1   4        0        6           0   \n",
       "\n",
       "   weathersit  temp   atemp   hum  windspeed  casual  registered  cnt  \n",
       "0           1  0.24  0.2879  0.81        0.0       3          13   16  \n",
       "1           1  0.22  0.2727  0.80        0.0       8          32   40  \n",
       "2           1  0.22  0.2727  0.80        0.0       5          27   32  \n",
       "3           1  0.24  0.2879  0.75        0.0       3          10   13  \n",
       "4           1  0.24  0.2879  0.75        0.0       0           1    1  "
      ]
     },
     "metadata": {},
     "execution_count": 4
    }
   ],
   "metadata": {
    "collapsed": true,
    "editable": true,
    "jupyter": {
     "outputs_hidden": true
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "## Checking out the data\n",
    "\n",
    "This dataset has the number of riders for each hour of each day from January 1 2011 to December 31 2012. The number of riders is split between casual and registered, summed up in the `cnt` column. You can see the first few rows of the data above.\n",
    "\n",
    "Below is a plot showing the number of bike riders over the first 10 days or so in the data set. (Some days don't have exactly 24 entries in the data set, so it's not exactly 10 days.) You can see the hourly rentals here. This data is pretty complicated! The weekends have lower over all ridership and there are spikes when people are biking to and from work during the week. Looking at the data above, we also have information about temperature, humidity, and windspeed, all of these likely affecting the number of riders. You'll be trying to capture all this with your model."
   ],
   "metadata": {
    "editable": true
   }
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "source": [
    "display(rides[:24*10].plot(x='dteday', y='cnt'))"
   ],
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x23ff21b59d0>"
      ]
     },
     "metadata": {}
    },
    {
     "output_type": "display_data",
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "width": 375,
       "height": 261
      },
      "needs_background": "light"
     }
    }
   ],
   "metadata": {
    "collapsed": true,
    "editable": true,
    "jupyter": {
     "outputs_hidden": true
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "### Dummy variables\n",
    "Here we have some categorical variables like season, weather, month. To include these in our model, we'll need to make binary dummy variables. This is simple to do with Pandas thanks to `get_dummies()`."
   ],
   "metadata": {
    "editable": true
   }
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "source": [
    "dummy_fields = ['season', 'weathersit', 'mnth', 'hr', 'weekday']\r\n",
    "for each in dummy_fields:\r\n",
    "    dummies = pd.get_dummies(rides[each], prefix=each, drop_first=False)\r\n",
    "    rides = pd.concat([rides, dummies], axis=1)\r\n",
    "\r\n",
    "fields_to_drop = ['instant', 'dteday', 'season', 'weathersit', \r\n",
    "                  'weekday', 'atemp', 'mnth', 'workingday', 'hr']\r\n",
    "data = rides.drop(fields_to_drop, axis=1)\r\n",
    "data.head(20)"
   ],
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/html": [
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>yr</th>\n",
       "      <th>holiday</th>\n",
       "      <th>temp</th>\n",
       "      <th>hum</th>\n",
       "      <th>windspeed</th>\n",
       "      <th>casual</th>\n",
       "      <th>registered</th>\n",
       "      <th>cnt</th>\n",
       "      <th>season_1</th>\n",
       "      <th>season_2</th>\n",
       "      <th>season_3</th>\n",
       "      <th>season_4</th>\n",
       "      <th>weathersit_1</th>\n",
       "      <th>weathersit_2</th>\n",
       "      <th>weathersit_3</th>\n",
       "      <th>weathersit_4</th>\n",
       "      <th>mnth_1</th>\n",
       "      <th>mnth_2</th>\n",
       "      <th>mnth_3</th>\n",
       "      <th>mnth_4</th>\n",
       "      <th>mnth_5</th>\n",
       "      <th>mnth_6</th>\n",
       "      <th>mnth_7</th>\n",
       "      <th>mnth_8</th>\n",
       "      <th>mnth_9</th>\n",
       "      <th>mnth_10</th>\n",
       "      <th>mnth_11</th>\n",
       "      <th>mnth_12</th>\n",
       "      <th>hr_0</th>\n",
       "      <th>hr_1</th>\n",
       "      <th>hr_2</th>\n",
       "      <th>hr_3</th>\n",
       "      <th>hr_4</th>\n",
       "      <th>hr_5</th>\n",
       "      <th>hr_6</th>\n",
       "      <th>hr_7</th>\n",
       "      <th>hr_8</th>\n",
       "      <th>hr_9</th>\n",
       "      <th>hr_10</th>\n",
       "      <th>hr_11</th>\n",
       "      <th>hr_12</th>\n",
       "      <th>hr_13</th>\n",
       "      <th>hr_14</th>\n",
       "      <th>hr_15</th>\n",
       "      <th>hr_16</th>\n",
       "      <th>hr_17</th>\n",
       "      <th>hr_18</th>\n",
       "      <th>hr_19</th>\n",
       "      <th>hr_20</th>\n",
       "      <th>hr_21</th>\n",
       "      <th>hr_22</th>\n",
       "      <th>hr_23</th>\n",
       "      <th>weekday_0</th>\n",
       "      <th>weekday_1</th>\n",
       "      <th>weekday_2</th>\n",
       "      <th>weekday_3</th>\n",
       "      <th>weekday_4</th>\n",
       "      <th>weekday_5</th>\n",
       "      <th>weekday_6</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
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       "      <td>0.81</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>3</td>\n",
       "      <td>13</td>\n",
       "      <td>16</td>\n",
       "      <td>1</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
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       "      <td>0</td>\n",
       "      <td>0.22</td>\n",
       "      <td>0.80</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>8</td>\n",
       "      <td>32</td>\n",
       "      <td>40</td>\n",
       "      <td>1</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
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       "      <td>0</td>\n",
       "      <td>0.22</td>\n",
       "      <td>0.80</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>5</td>\n",
       "      <td>27</td>\n",
       "      <td>32</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.24</td>\n",
       "      <td>0.75</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>3</td>\n",
       "      <td>10</td>\n",
       "      <td>13</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.24</td>\n",
       "      <td>0.75</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.24</td>\n",
       "      <td>0.75</td>\n",
       "      <td>0.0896</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.22</td>\n",
       "      <td>0.80</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.20</td>\n",
       "      <td>0.86</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.24</td>\n",
       "      <td>0.75</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.32</td>\n",
       "      <td>0.76</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>8</td>\n",
       "      <td>6</td>\n",
       "      <td>14</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.38</td>\n",
       "      <td>0.76</td>\n",
       "      <td>0.2537</td>\n",
       "      <td>12</td>\n",
       "      <td>24</td>\n",
       "      <td>36</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.36</td>\n",
       "      <td>0.81</td>\n",
       "      <td>0.2836</td>\n",
       "      <td>26</td>\n",
       "      <td>30</td>\n",
       "      <td>56</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.42</td>\n",
       "      <td>0.77</td>\n",
       "      <td>0.2836</td>\n",
       "      <td>29</td>\n",
       "      <td>55</td>\n",
       "      <td>84</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.46</td>\n",
       "      <td>0.72</td>\n",
       "      <td>0.2985</td>\n",
       "      <td>47</td>\n",
       "      <td>47</td>\n",
       "      <td>94</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.46</td>\n",
       "      <td>0.72</td>\n",
       "      <td>0.2836</td>\n",
       "      <td>35</td>\n",
       "      <td>71</td>\n",
       "      <td>106</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.44</td>\n",
       "      <td>0.77</td>\n",
       "      <td>0.2985</td>\n",
       "      <td>40</td>\n",
       "      <td>70</td>\n",
       "      <td>110</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.42</td>\n",
       "      <td>0.82</td>\n",
       "      <td>0.2985</td>\n",
       "      <td>41</td>\n",
       "      <td>52</td>\n",
       "      <td>93</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.44</td>\n",
       "      <td>0.82</td>\n",
       "      <td>0.2836</td>\n",
       "      <td>15</td>\n",
       "      <td>52</td>\n",
       "      <td>67</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.42</td>\n",
       "      <td>0.88</td>\n",
       "      <td>0.2537</td>\n",
       "      <td>9</td>\n",
       "      <td>26</td>\n",
       "      <td>35</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.42</td>\n",
       "      <td>0.88</td>\n",
       "      <td>0.2537</td>\n",
       "      <td>6</td>\n",
       "      <td>31</td>\n",
       "      <td>37</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    yr  holiday  temp   hum  windspeed  casual  registered  cnt  season_1  \\\n",
       "0    0        0  0.24  0.81     0.0000       3          13   16         1   \n",
       "1    0        0  0.22  0.80     0.0000       8          32   40         1   \n",
       "2    0        0  0.22  0.80     0.0000       5          27   32         1   \n",
       "3    0        0  0.24  0.75     0.0000       3          10   13         1   \n",
       "4    0        0  0.24  0.75     0.0000       0           1    1         1   \n",
       "5    0        0  0.24  0.75     0.0896       0           1    1         1   \n",
       "6    0        0  0.22  0.80     0.0000       2           0    2         1   \n",
       "7    0        0  0.20  0.86     0.0000       1           2    3         1   \n",
       "8    0        0  0.24  0.75     0.0000       1           7    8         1   \n",
       "9    0        0  0.32  0.76     0.0000       8           6   14         1   \n",
       "10   0        0  0.38  0.76     0.2537      12          24   36         1   \n",
       "11   0        0  0.36  0.81     0.2836      26          30   56         1   \n",
       "12   0        0  0.42  0.77     0.2836      29          55   84         1   \n",
       "13   0        0  0.46  0.72     0.2985      47          47   94         1   \n",
       "14   0        0  0.46  0.72     0.2836      35          71  106         1   \n",
       "15   0        0  0.44  0.77     0.2985      40          70  110         1   \n",
       "16   0        0  0.42  0.82     0.2985      41          52   93         1   \n",
       "17   0        0  0.44  0.82     0.2836      15          52   67         1   \n",
       "18   0        0  0.42  0.88     0.2537       9          26   35         1   \n",
       "19   0        0  0.42  0.88     0.2537       6          31   37         1   \n",
       "\n",
       "    season_2  season_3  season_4  weathersit_1  weathersit_2  weathersit_3  \\\n",
       "0          0         0         0             1             0             0   \n",
       "1          0         0         0             1             0             0   \n",
       "2          0         0         0             1             0             0   \n",
       "3          0         0         0             1             0             0   \n",
       "4          0         0         0             1             0             0   \n",
       "5          0         0         0             0             1             0   \n",
       "6          0         0         0             1             0             0   \n",
       "7          0         0         0             1             0             0   \n",
       "8          0         0         0             1             0             0   \n",
       "9          0         0         0             1             0             0   \n",
       "10         0         0         0             1             0             0   \n",
       "11         0         0         0             1             0             0   \n",
       "12         0         0         0             1             0             0   \n",
       "13         0         0         0             0             1             0   \n",
       "14         0         0         0             0             1             0   \n",
       "15         0         0         0             0             1             0   \n",
       "16         0         0         0             0             1             0   \n",
       "17         0         0         0             0             1             0   \n",
       "18         0         0         0             0             0             1   \n",
       "19         0         0         0             0             0             1   \n",
       "\n",
       "    weathersit_4  mnth_1  mnth_2  mnth_3  mnth_4  mnth_5  mnth_6  mnth_7  \\\n",
       "0              0       1       0       0       0       0       0       0   \n",
       "1              0       1       0       0       0       0       0       0   \n",
       "2              0       1       0       0       0       0       0       0   \n",
       "3              0       1       0       0       0       0       0       0   \n",
       "4              0       1       0       0       0       0       0       0   \n",
       "5              0       1       0       0       0       0       0       0   \n",
       "6              0       1       0       0       0       0       0       0   \n",
       "7              0       1       0       0       0       0       0       0   \n",
       "8              0       1       0       0       0       0       0       0   \n",
       "9              0       1       0       0       0       0       0       0   \n",
       "10             0       1       0       0       0       0       0       0   \n",
       "11             0       1       0       0       0       0       0       0   \n",
       "12             0       1       0       0       0       0       0       0   \n",
       "13             0       1       0       0       0       0       0       0   \n",
       "14             0       1       0       0       0       0       0       0   \n",
       "15             0       1       0       0       0       0       0       0   \n",
       "16             0       1       0       0       0       0       0       0   \n",
       "17             0       1       0       0       0       0       0       0   \n",
       "18             0       1       0       0       0       0       0       0   \n",
       "19             0       1       0       0       0       0       0       0   \n",
       "\n",
       "    mnth_8  mnth_9  mnth_10  mnth_11  mnth_12  hr_0  hr_1  hr_2  hr_3  hr_4  \\\n",
       "0        0       0        0        0        0     1     0     0     0     0   \n",
       "1        0       0        0        0        0     0     1     0     0     0   \n",
       "2        0       0        0        0        0     0     0     1     0     0   \n",
       "3        0       0        0        0        0     0     0     0     1     0   \n",
       "4        0       0        0        0        0     0     0     0     0     1   \n",
       "5        0       0        0        0        0     0     0     0     0     0   \n",
       "6        0       0        0        0        0     0     0     0     0     0   \n",
       "7        0       0        0        0        0     0     0     0     0     0   \n",
       "8        0       0        0        0        0     0     0     0     0     0   \n",
       "9        0       0        0        0        0     0     0     0     0     0   \n",
       "10       0       0        0        0        0     0     0     0     0     0   \n",
       "11       0       0        0        0        0     0     0     0     0     0   \n",
       "12       0       0        0        0        0     0     0     0     0     0   \n",
       "13       0       0        0        0        0     0     0     0     0     0   \n",
       "14       0       0        0        0        0     0     0     0     0     0   \n",
       "15       0       0        0        0        0     0     0     0     0     0   \n",
       "16       0       0        0        0        0     0     0     0     0     0   \n",
       "17       0       0        0        0        0     0     0     0     0     0   \n",
       "18       0       0        0        0        0     0     0     0     0     0   \n",
       "19       0       0        0        0        0     0     0     0     0     0   \n",
       "\n",
       "    hr_5  hr_6  hr_7  hr_8  hr_9  hr_10  hr_11  hr_12  hr_13  hr_14  hr_15  \\\n",
       "0      0     0     0     0     0      0      0      0      0      0      0   \n",
       "1      0     0     0     0     0      0      0      0      0      0      0   \n",
       "2      0     0     0     0     0      0      0      0      0      0      0   \n",
       "3      0     0     0     0     0      0      0      0      0      0      0   \n",
       "4      0     0     0     0     0      0      0      0      0      0      0   \n",
       "5      1     0     0     0     0      0      0      0      0      0      0   \n",
       "6      0     1     0     0     0      0      0      0      0      0      0   \n",
       "7      0     0     1     0     0      0      0      0      0      0      0   \n",
       "8      0     0     0     1     0      0      0      0      0      0      0   \n",
       "9      0     0     0     0     1      0      0      0      0      0      0   \n",
       "10     0     0     0     0     0      1      0      0      0      0      0   \n",
       "11     0     0     0     0     0      0      1      0      0      0      0   \n",
       "12     0     0     0     0     0      0      0      1      0      0      0   \n",
       "13     0     0     0     0     0      0      0      0      1      0      0   \n",
       "14     0     0     0     0     0      0      0      0      0      1      0   \n",
       "15     0     0     0     0     0      0      0      0      0      0      1   \n",
       "16     0     0     0     0     0      0      0      0      0      0      0   \n",
       "17     0     0     0     0     0      0      0      0      0      0      0   \n",
       "18     0     0     0     0     0      0      0      0      0      0      0   \n",
       "19     0     0     0     0     0      0      0      0      0      0      0   \n",
       "\n",
       "    hr_16  hr_17  hr_18  hr_19  hr_20  hr_21  hr_22  hr_23  weekday_0  \\\n",
       "0       0      0      0      0      0      0      0      0          0   \n",
       "1       0      0      0      0      0      0      0      0          0   \n",
       "2       0      0      0      0      0      0      0      0          0   \n",
       "3       0      0      0      0      0      0      0      0          0   \n",
       "4       0      0      0      0      0      0      0      0          0   \n",
       "5       0      0      0      0      0      0      0      0          0   \n",
       "6       0      0      0      0      0      0      0      0          0   \n",
       "7       0      0      0      0      0      0      0      0          0   \n",
       "8       0      0      0      0      0      0      0      0          0   \n",
       "9       0      0      0      0      0      0      0      0          0   \n",
       "10      0      0      0      0      0      0      0      0          0   \n",
       "11      0      0      0      0      0      0      0      0          0   \n",
       "12      0      0      0      0      0      0      0      0          0   \n",
       "13      0      0      0      0      0      0      0      0          0   \n",
       "14      0      0      0      0      0      0      0      0          0   \n",
       "15      0      0      0      0      0      0      0      0          0   \n",
       "16      1      0      0      0      0      0      0      0          0   \n",
       "17      0      1      0      0      0      0      0      0          0   \n",
       "18      0      0      1      0      0      0      0      0          0   \n",
       "19      0      0      0      1      0      0      0      0          0   \n",
       "\n",
       "    weekday_1  weekday_2  weekday_3  weekday_4  weekday_5  weekday_6  \n",
       "0           0          0          0          0          0          1  \n",
       "1           0          0          0          0          0          1  \n",
       "2           0          0          0          0          0          1  \n",
       "3           0          0          0          0          0          1  \n",
       "4           0          0          0          0          0          1  \n",
       "5           0          0          0          0          0          1  \n",
       "6           0          0          0          0          0          1  \n",
       "7           0          0          0          0          0          1  \n",
       "8           0          0          0          0          0          1  \n",
       "9           0          0          0          0          0          1  \n",
       "10          0          0          0          0          0          1  \n",
       "11          0          0          0          0          0          1  \n",
       "12          0          0          0          0          0          1  \n",
       "13          0          0          0          0          0          1  \n",
       "14          0          0          0          0          0          1  \n",
       "15          0          0          0          0          0          1  \n",
       "16          0          0          0          0          0          1  \n",
       "17          0          0          0          0          0          1  \n",
       "18          0          0          0          0          0          1  \n",
       "19          0          0          0          0          0          1  "
      ]
     },
     "metadata": {},
     "execution_count": 6
    }
   ],
   "metadata": {
    "collapsed": true,
    "editable": true,
    "jupyter": {
     "outputs_hidden": true
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "### A. Splitting the data into training, testing, and validation sets\n",
    "We'll save the data for the last approximately 21 days to use as a test set after we've trained the network. We'll use this set to make predictions and compare them with the actual number of riders.\n",
    "\n",
    "### B. Scaling the target variables\n",
    "To make training the network easier, we'll standardize each of the continuous variables. That is, we'll shift and scale the variables such that they have zero mean and a standard deviation of 1.\n",
    "\n",
    "The scaling factors are saved so we can go backwards when we use the network for predictions.\n"
   ],
   "metadata": {
    "editable": true
   }
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "source": [
    "# Save data for approximately the last 21 days \r\n",
    "test_data = data[-21*24:]\r\n",
    "\r\n",
    "# Now remove the test data from the data set \r\n",
    "data = data[:-21*24]\r\n",
    "\r\n",
    "# scaling\r\n",
    "quant_features = ['casual', 'registered', 'cnt', 'temp', 'hum', 'windspeed']\r\n",
    "# Store scalings in a dictionary so we can convert back later\r\n",
    "scaled_features = {}\r\n",
    "for each in quant_features:\r\n",
    "    mean, std = data[each].mean(), data[each].std()\r\n",
    "    scaled_features[each] = [mean, std]\r\n",
    "    data.loc[:, each] = (data[each] - mean)/std\r\n",
    "\r\n",
    "# Separate the data into features and targets\r\n",
    "target_fields = ['cnt', 'casual', 'registered']\r\n",
    "features, targets = data.drop(target_fields, axis=1), data[target_fields]\r\n",
    "test_features, test_targets = test_data.drop(target_fields, axis=1), test_data[target_fields]\r\n",
    "\r\n",
    "\r\n",
    "display(features.head(20))"
   ],
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
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       "      <th>yr</th>\n",
       "      <th>holiday</th>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>-1.370528</td>\n",
       "      <td>0.636780</td>\n",
       "      <td>-1.558407</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>-1.370528</td>\n",
       "      <td>0.636780</td>\n",
       "      <td>-0.822562</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>-1.474766</td>\n",
       "      <td>0.894996</td>\n",
       "      <td>-1.558407</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>-1.579004</td>\n",
       "      <td>1.204855</td>\n",
       "      <td>-1.558407</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>-1.370528</td>\n",
       "      <td>0.636780</td>\n",
       "      <td>-1.558407</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>-0.953575</td>\n",
       "      <td>0.688423</td>\n",
       "      <td>-1.558407</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>-0.640860</td>\n",
       "      <td>0.688423</td>\n",
       "      <td>0.525120</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>-0.745098</td>\n",
       "      <td>0.946639</td>\n",
       "      <td>0.770676</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>-0.432383</td>\n",
       "      <td>0.740066</td>\n",
       "      <td>0.770676</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>-0.223907</td>\n",
       "      <td>0.481850</td>\n",
       "      <td>0.893043</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>-0.223907</td>\n",
       "      <td>0.481850</td>\n",
       "      <td>0.770676</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>-0.328145</td>\n",
       "      <td>0.740066</td>\n",
       "      <td>0.893043</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>-0.432383</td>\n",
       "      <td>0.998282</td>\n",
       "      <td>0.893043</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>-0.328145</td>\n",
       "      <td>0.998282</td>\n",
       "      <td>0.770676</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>-0.432383</td>\n",
       "      <td>1.308142</td>\n",
       "      <td>0.525120</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>-0.432383</td>\n",
       "      <td>1.308142</td>\n",
       "      <td>0.525120</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    yr  holiday      temp       hum  windspeed  season_1  season_2  season_3  \\\n",
       "0    0        0 -1.370528  0.946639  -1.558407         1         0         0   \n",
       "1    0        0 -1.474766  0.894996  -1.558407         1         0         0   \n",
       "2    0        0 -1.474766  0.894996  -1.558407         1         0         0   \n",
       "3    0        0 -1.370528  0.636780  -1.558407         1         0         0   \n",
       "4    0        0 -1.370528  0.636780  -1.558407         1         0         0   \n",
       "5    0        0 -1.370528  0.636780  -0.822562         1         0         0   \n",
       "6    0        0 -1.474766  0.894996  -1.558407         1         0         0   \n",
       "7    0        0 -1.579004  1.204855  -1.558407         1         0         0   \n",
       "8    0        0 -1.370528  0.636780  -1.558407         1         0         0   \n",
       "9    0        0 -0.953575  0.688423  -1.558407         1         0         0   \n",
       "10   0        0 -0.640860  0.688423   0.525120         1         0         0   \n",
       "11   0        0 -0.745098  0.946639   0.770676         1         0         0   \n",
       "12   0        0 -0.432383  0.740066   0.770676         1         0         0   \n",
       "13   0        0 -0.223907  0.481850   0.893043         1         0         0   \n",
       "14   0        0 -0.223907  0.481850   0.770676         1         0         0   \n",
       "15   0        0 -0.328145  0.740066   0.893043         1         0         0   \n",
       "16   0        0 -0.432383  0.998282   0.893043         1         0         0   \n",
       "17   0        0 -0.328145  0.998282   0.770676         1         0         0   \n",
       "18   0        0 -0.432383  1.308142   0.525120         1         0         0   \n",
       "19   0        0 -0.432383  1.308142   0.525120         1         0         0   \n",
       "\n",
       "    season_4  weathersit_1  weathersit_2  weathersit_3  weathersit_4  mnth_1  \\\n",
       "0          0             1             0             0             0       1   \n",
       "1          0             1             0             0             0       1   \n",
       "2          0             1             0             0             0       1   \n",
       "3          0             1             0             0             0       1   \n",
       "4          0             1             0             0             0       1   \n",
       "5          0             0             1             0             0       1   \n",
       "6          0             1             0             0             0       1   \n",
       "7          0             1             0             0             0       1   \n",
       "8          0             1             0             0             0       1   \n",
       "9          0             1             0             0             0       1   \n",
       "10         0             1             0             0             0       1   \n",
       "11         0             1             0             0             0       1   \n",
       "12         0             1             0             0             0       1   \n",
       "13         0             0             1             0             0       1   \n",
       "14         0             0             1             0             0       1   \n",
       "15         0             0             1             0             0       1   \n",
       "16         0             0             1             0             0       1   \n",
       "17         0             0             1             0             0       1   \n",
       "18         0             0             0             1             0       1   \n",
       "19         0             0             0             1             0       1   \n",
       "\n",
       "    mnth_2  mnth_3  mnth_4  mnth_5  mnth_6  mnth_7  mnth_8  mnth_9  mnth_10  \\\n",
       "0        0       0       0       0       0       0       0       0        0   \n",
       "1        0       0       0       0       0       0       0       0        0   \n",
       "2        0       0       0       0       0       0       0       0        0   \n",
       "3        0       0       0       0       0       0       0       0        0   \n",
       "4        0       0       0       0       0       0       0       0        0   \n",
       "5        0       0       0       0       0       0       0       0        0   \n",
       "6        0       0       0       0       0       0       0       0        0   \n",
       "7        0       0       0       0       0       0       0       0        0   \n",
       "8        0       0       0       0       0       0       0       0        0   \n",
       "9        0       0       0       0       0       0       0       0        0   \n",
       "10       0       0       0       0       0       0       0       0        0   \n",
       "11       0       0       0       0       0       0       0       0        0   \n",
       "12       0       0       0       0       0       0       0       0        0   \n",
       "13       0       0       0       0       0       0       0       0        0   \n",
       "14       0       0       0       0       0       0       0       0        0   \n",
       "15       0       0       0       0       0       0       0       0        0   \n",
       "16       0       0       0       0       0       0       0       0        0   \n",
       "17       0       0       0       0       0       0       0       0        0   \n",
       "18       0       0       0       0       0       0       0       0        0   \n",
       "19       0       0       0       0       0       0       0       0        0   \n",
       "\n",
       "    mnth_11  mnth_12  hr_0  hr_1  hr_2  hr_3  hr_4  hr_5  hr_6  hr_7  hr_8  \\\n",
       "0         0        0     1     0     0     0     0     0     0     0     0   \n",
       "1         0        0     0     1     0     0     0     0     0     0     0   \n",
       "2         0        0     0     0     1     0     0     0     0     0     0   \n",
       "3         0        0     0     0     0     1     0     0     0     0     0   \n",
       "4         0        0     0     0     0     0     1     0     0     0     0   \n",
       "5         0        0     0     0     0     0     0     1     0     0     0   \n",
       "6         0        0     0     0     0     0     0     0     1     0     0   \n",
       "7         0        0     0     0     0     0     0     0     0     1     0   \n",
       "8         0        0     0     0     0     0     0     0     0     0     1   \n",
       "9         0        0     0     0     0     0     0     0     0     0     0   \n",
       "10        0        0     0     0     0     0     0     0     0     0     0   \n",
       "11        0        0     0     0     0     0     0     0     0     0     0   \n",
       "12        0        0     0     0     0     0     0     0     0     0     0   \n",
       "13        0        0     0     0     0     0     0     0     0     0     0   \n",
       "14        0        0     0     0     0     0     0     0     0     0     0   \n",
       "15        0        0     0     0     0     0     0     0     0     0     0   \n",
       "16        0        0     0     0     0     0     0     0     0     0     0   \n",
       "17        0        0     0     0     0     0     0     0     0     0     0   \n",
       "18        0        0     0     0     0     0     0     0     0     0     0   \n",
       "19        0        0     0     0     0     0     0     0     0     0     0   \n",
       "\n",
       "    hr_9  hr_10  hr_11  hr_12  hr_13  hr_14  hr_15  hr_16  hr_17  hr_18  \\\n",
       "0      0      0      0      0      0      0      0      0      0      0   \n",
       "1      0      0      0      0      0      0      0      0      0      0   \n",
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       "3      0      0      0      0      0      0      0      0      0      0   \n",
       "4      0      0      0      0      0      0      0      0      0      0   \n",
       "5      0      0      0      0      0      0      0      0      0      0   \n",
       "6      0      0      0      0      0      0      0      0      0      0   \n",
       "7      0      0      0      0      0      0      0      0      0      0   \n",
       "8      0      0      0      0      0      0      0      0      0      0   \n",
       "9      1      0      0      0      0      0      0      0      0      0   \n",
       "10     0      1      0      0      0      0      0      0      0      0   \n",
       "11     0      0      1      0      0      0      0      0      0      0   \n",
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       "\n",
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       "0       0      0      0      0      0          0          0          0   \n",
       "1       0      0      0      0      0          0          0          0   \n",
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       "10      0      0      0      0      0          0          0          0   \n",
       "11      0      0      0      0      0          0          0          0   \n",
       "12      0      0      0      0      0          0          0          0   \n",
       "13      0      0      0      0      0          0          0          0   \n",
       "14      0      0      0      0      0          0          0          0   \n",
       "15      0      0      0      0      0          0          0          0   \n",
       "16      0      0      0      0      0          0          0          0   \n",
       "17      0      0      0      0      0          0          0          0   \n",
       "18      0      0      0      0      0          0          0          0   \n",
       "19      1      0      0      0      0          0          0          0   \n",
       "\n",
       "    weekday_3  weekday_4  weekday_5  weekday_6  \n",
       "0           0          0          0          1  \n",
       "1           0          0          0          1  \n",
       "2           0          0          0          1  \n",
       "3           0          0          0          1  \n",
       "4           0          0          0          1  \n",
       "5           0          0          0          1  \n",
       "6           0          0          0          1  \n",
       "7           0          0          0          1  \n",
       "8           0          0          0          1  \n",
       "9           0          0          0          1  \n",
       "10          0          0          0          1  \n",
       "11          0          0          0          1  \n",
       "12          0          0          0          1  \n",
       "13          0          0          0          1  \n",
       "14          0          0          0          1  \n",
       "15          0          0          0          1  \n",
       "16          0          0          0          1  \n",
       "17          0          0          0          1  \n",
       "18          0          0          0          1  \n",
       "19          0          0          0          1  "
      ]
     },
     "metadata": {}
    }
   ],
   "metadata": {
    "collapsed": true,
    "editable": true,
    "jupyter": {
     "outputs_hidden": true
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "We'll split the data into two sets, one for training and one for validating as the network is being trained. Since this is time series data, we'll train on historical data, then try to predict on future data (the validation set)."
   ],
   "metadata": {
    "editable": true
   }
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "source": [
    "# Hold out the last 60 days or so of the remaining data as a validation set\r\n",
    "train_features, train_targets = features[:-60*24], targets[:-60*24]\r\n",
    "val_features, val_targets = features[-60*24:], targets[-60*24:]\r\n",
    "\r\n",
    "display(train_features.head(20))\r\n",
    "train_targets[0:10]\r\n",
    "\r\n",
    "print(train_features.shape)"
   ],
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
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       "      <td>-1.474766</td>\n",
       "      <td>0.894996</td>\n",
       "      <td>-1.558407</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>-1.579004</td>\n",
       "      <td>1.204855</td>\n",
       "      <td>-1.558407</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>-1.370528</td>\n",
       "      <td>0.636780</td>\n",
       "      <td>-1.558407</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>-0.953575</td>\n",
       "      <td>0.688423</td>\n",
       "      <td>-1.558407</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>-0.640860</td>\n",
       "      <td>0.688423</td>\n",
       "      <td>0.525120</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>-0.745098</td>\n",
       "      <td>0.946639</td>\n",
       "      <td>0.770676</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>-0.432383</td>\n",
       "      <td>0.740066</td>\n",
       "      <td>0.770676</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>-0.223907</td>\n",
       "      <td>0.481850</td>\n",
       "      <td>0.893043</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>-0.223907</td>\n",
       "      <td>0.481850</td>\n",
       "      <td>0.770676</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>-0.328145</td>\n",
       "      <td>0.740066</td>\n",
       "      <td>0.893043</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>-0.432383</td>\n",
       "      <td>0.998282</td>\n",
       "      <td>0.893043</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>-0.328145</td>\n",
       "      <td>0.998282</td>\n",
       "      <td>0.770676</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>-0.432383</td>\n",
       "      <td>1.308142</td>\n",
       "      <td>0.525120</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>-0.432383</td>\n",
       "      <td>1.308142</td>\n",
       "      <td>0.525120</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    yr  holiday      temp       hum  windspeed  season_1  season_2  season_3  \\\n",
       "0    0        0 -1.370528  0.946639  -1.558407         1         0         0   \n",
       "1    0        0 -1.474766  0.894996  -1.558407         1         0         0   \n",
       "2    0        0 -1.474766  0.894996  -1.558407         1         0         0   \n",
       "3    0        0 -1.370528  0.636780  -1.558407         1         0         0   \n",
       "4    0        0 -1.370528  0.636780  -1.558407         1         0         0   \n",
       "5    0        0 -1.370528  0.636780  -0.822562         1         0         0   \n",
       "6    0        0 -1.474766  0.894996  -1.558407         1         0         0   \n",
       "7    0        0 -1.579004  1.204855  -1.558407         1         0         0   \n",
       "8    0        0 -1.370528  0.636780  -1.558407         1         0         0   \n",
       "9    0        0 -0.953575  0.688423  -1.558407         1         0         0   \n",
       "10   0        0 -0.640860  0.688423   0.525120         1         0         0   \n",
       "11   0        0 -0.745098  0.946639   0.770676         1         0         0   \n",
       "12   0        0 -0.432383  0.740066   0.770676         1         0         0   \n",
       "13   0        0 -0.223907  0.481850   0.893043         1         0         0   \n",
       "14   0        0 -0.223907  0.481850   0.770676         1         0         0   \n",
       "15   0        0 -0.328145  0.740066   0.893043         1         0         0   \n",
       "16   0        0 -0.432383  0.998282   0.893043         1         0         0   \n",
       "17   0        0 -0.328145  0.998282   0.770676         1         0         0   \n",
       "18   0        0 -0.432383  1.308142   0.525120         1         0         0   \n",
       "19   0        0 -0.432383  1.308142   0.525120         1         0         0   \n",
       "\n",
       "    season_4  weathersit_1  weathersit_2  weathersit_3  weathersit_4  mnth_1  \\\n",
       "0          0             1             0             0             0       1   \n",
       "1          0             1             0             0             0       1   \n",
       "2          0             1             0             0             0       1   \n",
       "3          0             1             0             0             0       1   \n",
       "4          0             1             0             0             0       1   \n",
       "5          0             0             1             0             0       1   \n",
       "6          0             1             0             0             0       1   \n",
       "7          0             1             0             0             0       1   \n",
       "8          0             1             0             0             0       1   \n",
       "9          0             1             0             0             0       1   \n",
       "10         0             1             0             0             0       1   \n",
       "11         0             1             0             0             0       1   \n",
       "12         0             1             0             0             0       1   \n",
       "13         0             0             1             0             0       1   \n",
       "14         0             0             1             0             0       1   \n",
       "15         0             0             1             0             0       1   \n",
       "16         0             0             1             0             0       1   \n",
       "17         0             0             1             0             0       1   \n",
       "18         0             0             0             1             0       1   \n",
       "19         0             0             0             1             0       1   \n",
       "\n",
       "    mnth_2  mnth_3  mnth_4  mnth_5  mnth_6  mnth_7  mnth_8  mnth_9  mnth_10  \\\n",
       "0        0       0       0       0       0       0       0       0        0   \n",
       "1        0       0       0       0       0       0       0       0        0   \n",
       "2        0       0       0       0       0       0       0       0        0   \n",
       "3        0       0       0       0       0       0       0       0        0   \n",
       "4        0       0       0       0       0       0       0       0        0   \n",
       "5        0       0       0       0       0       0       0       0        0   \n",
       "6        0       0       0       0       0       0       0       0        0   \n",
       "7        0       0       0       0       0       0       0       0        0   \n",
       "8        0       0       0       0       0       0       0       0        0   \n",
       "9        0       0       0       0       0       0       0       0        0   \n",
       "10       0       0       0       0       0       0       0       0        0   \n",
       "11       0       0       0       0       0       0       0       0        0   \n",
       "12       0       0       0       0       0       0       0       0        0   \n",
       "13       0       0       0       0       0       0       0       0        0   \n",
       "14       0       0       0       0       0       0       0       0        0   \n",
       "15       0       0       0       0       0       0       0       0        0   \n",
       "16       0       0       0       0       0       0       0       0        0   \n",
       "17       0       0       0       0       0       0       0       0        0   \n",
       "18       0       0       0       0       0       0       0       0        0   \n",
       "19       0       0       0       0       0       0       0       0        0   \n",
       "\n",
       "    mnth_11  mnth_12  hr_0  hr_1  hr_2  hr_3  hr_4  hr_5  hr_6  hr_7  hr_8  \\\n",
       "0         0        0     1     0     0     0     0     0     0     0     0   \n",
       "1         0        0     0     1     0     0     0     0     0     0     0   \n",
       "2         0        0     0     0     1     0     0     0     0     0     0   \n",
       "3         0        0     0     0     0     1     0     0     0     0     0   \n",
       "4         0        0     0     0     0     0     1     0     0     0     0   \n",
       "5         0        0     0     0     0     0     0     1     0     0     0   \n",
       "6         0        0     0     0     0     0     0     0     1     0     0   \n",
       "7         0        0     0     0     0     0     0     0     0     1     0   \n",
       "8         0        0     0     0     0     0     0     0     0     0     1   \n",
       "9         0        0     0     0     0     0     0     0     0     0     0   \n",
       "10        0        0     0     0     0     0     0     0     0     0     0   \n",
       "11        0        0     0     0     0     0     0     0     0     0     0   \n",
       "12        0        0     0     0     0     0     0     0     0     0     0   \n",
       "13        0        0     0     0     0     0     0     0     0     0     0   \n",
       "14        0        0     0     0     0     0     0     0     0     0     0   \n",
       "15        0        0     0     0     0     0     0     0     0     0     0   \n",
       "16        0        0     0     0     0     0     0     0     0     0     0   \n",
       "17        0        0     0     0     0     0     0     0     0     0     0   \n",
       "18        0        0     0     0     0     0     0     0     0     0     0   \n",
       "19        0        0     0     0     0     0     0     0     0     0     0   \n",
       "\n",
       "    hr_9  hr_10  hr_11  hr_12  hr_13  hr_14  hr_15  hr_16  hr_17  hr_18  \\\n",
       "0      0      0      0      0      0      0      0      0      0      0   \n",
       "1      0      0      0      0      0      0      0      0      0      0   \n",
       "2      0      0      0      0      0      0      0      0      0      0   \n",
       "3      0      0      0      0      0      0      0      0      0      0   \n",
       "4      0      0      0      0      0      0      0      0      0      0   \n",
       "5      0      0      0      0      0      0      0      0      0      0   \n",
       "6      0      0      0      0      0      0      0      0      0      0   \n",
       "7      0      0      0      0      0      0      0      0      0      0   \n",
       "8      0      0      0      0      0      0      0      0      0      0   \n",
       "9      1      0      0      0      0      0      0      0      0      0   \n",
       "10     0      1      0      0      0      0      0      0      0      0   \n",
       "11     0      0      1      0      0      0      0      0      0      0   \n",
       "12     0      0      0      1      0      0      0      0      0      0   \n",
       "13     0      0      0      0      1      0      0      0      0      0   \n",
       "14     0      0      0      0      0      1      0      0      0      0   \n",
       "15     0      0      0      0      0      0      1      0      0      0   \n",
       "16     0      0      0      0      0      0      0      1      0      0   \n",
       "17     0      0      0      0      0      0      0      0      1      0   \n",
       "18     0      0      0      0      0      0      0      0      0      1   \n",
       "19     0      0      0      0      0      0      0      0      0      0   \n",
       "\n",
       "    hr_19  hr_20  hr_21  hr_22  hr_23  weekday_0  weekday_1  weekday_2  \\\n",
       "0       0      0      0      0      0          0          0          0   \n",
       "1       0      0      0      0      0          0          0          0   \n",
       "2       0      0      0      0      0          0          0          0   \n",
       "3       0      0      0      0      0          0          0          0   \n",
       "4       0      0      0      0      0          0          0          0   \n",
       "5       0      0      0      0      0          0          0          0   \n",
       "6       0      0      0      0      0          0          0          0   \n",
       "7       0      0      0      0      0          0          0          0   \n",
       "8       0      0      0      0      0          0          0          0   \n",
       "9       0      0      0      0      0          0          0          0   \n",
       "10      0      0      0      0      0          0          0          0   \n",
       "11      0      0      0      0      0          0          0          0   \n",
       "12      0      0      0      0      0          0          0          0   \n",
       "13      0      0      0      0      0          0          0          0   \n",
       "14      0      0      0      0      0          0          0          0   \n",
       "15      0      0      0      0      0          0          0          0   \n",
       "16      0      0      0      0      0          0          0          0   \n",
       "17      0      0      0      0      0          0          0          0   \n",
       "18      0      0      0      0      0          0          0          0   \n",
       "19      1      0      0      0      0          0          0          0   \n",
       "\n",
       "    weekday_3  weekday_4  weekday_5  weekday_6  \n",
       "0           0          0          0          1  \n",
       "1           0          0          0          1  \n",
       "2           0          0          0          1  \n",
       "3           0          0          0          1  \n",
       "4           0          0          0          1  \n",
       "5           0          0          0          1  \n",
       "6           0          0          0          1  \n",
       "7           0          0          0          1  \n",
       "8           0          0          0          1  \n",
       "9           0          0          0          1  \n",
       "10          0          0          0          1  \n",
       "11          0          0          0          1  \n",
       "12          0          0          0          1  \n",
       "13          0          0          0          1  \n",
       "14          0          0          0          1  \n",
       "15          0          0          0          1  \n",
       "16          0          0          0          1  \n",
       "17          0          0          0          1  \n",
       "18          0          0          0          1  \n",
       "19          0          0          0          1  "
      ]
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     "name": "stdout",
     "text": [
      "(15435, 56)\n"
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   ],
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  {
   "cell_type": "markdown",
   "source": [
    "## Time to build the network\n",
    "\n",
    "Below you'll build your network. We've built out the structure. You'll implement both the forward pass and backwards pass through the network. You'll also set the hyperparameters: the learning rate, the number of hidden units, and the number of training passes.\n",
    "\n",
    "<img src=\"assets/neural_network.png\" width=300px>\n",
    "\n",
    "The network has two layers, a hidden layer and an output layer. The hidden layer will use the sigmoid function for activations. The output layer has only one node and is used for the regression, the output of the node is the same as the input of the node. That is, the activation function is $f(x)=x$. A function that takes the input signal and generates an output signal, but takes into account the threshold, is called an activation function. We work through each layer of our network calculating the outputs for each neuron. All of the outputs from one layer become inputs to the neurons on the next layer. This process is called *forward propagation*.\n",
    "\n",
    "We use the weights to propagate signals forward from the input to the output layers in a neural network. We use the weights to also propagate error backwards from the output back into the network to update our weights. This is called *backpropagation*.\n",
    "\n",
    "> **Hint:** You'll need the derivative of the output activation function ($f(x) = x$) for the backpropagation implementation. If you aren't familiar with calculus, this function is equivalent to the equation $y = x$. What is the slope of that equation? That is the derivative of $f(x)$.\n",
    "\n",
    "Below, you have these tasks:\n",
    "1. Implement the sigmoid function to use as the activation function. Set `self.activation_function` in `__init__` to your sigmoid function.\n",
    "2. Implement the forward pass in the `train` method.\n",
    "3. Implement the backpropagation algorithm in the `train` method, including calculating the output error.\n",
    "4. Implement the forward pass in the `run` method.\n",
    "  "
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    "editable": true
   }
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "source": [
    "#############\r\n",
    "# In the my_answers.py file, fill out the TODO sections as specified\r\n",
    "#############\r\n",
    "\r\n",
    "from my_answers import NeuralNetwork"
   ],
   "outputs": [],
   "metadata": {
    "collapsed": true,
    "editable": true,
    "jupyter": {
     "outputs_hidden": true
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "source": [
    "def MSE(y, Y):\r\n",
    "    return np.mean((y-Y)**2)"
   ],
   "outputs": [],
   "metadata": {
    "collapsed": true,
    "editable": true,
    "jupyter": {
     "outputs_hidden": true
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "## Unit tests\n",
    "\n",
    "Run these unit tests to check the correctness of your network implementation. This will help you be sure your network was implemented correctly befor you starting trying to train it. These tests must all be successful to pass the project."
   ],
   "metadata": {
    "editable": true
   }
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "source": [
    "from my_answers import NeuralNetwork\r\n",
    "import unittest\r\n",
    "\r\n",
    "inputs = np.array([[0.5, -0.2, 0.1]])\r\n",
    "targets = np.array([[0.4]])\r\n",
    "test_w_i_h = np.array([[0.1, -0.2],\r\n",
    "                       [0.4, 0.5],\r\n",
    "                       [-0.3, 0.2]])\r\n",
    "test_w_h_o = np.array([[0.3],\r\n",
    "                       [-0.1]])\r\n",
    "\r\n",
    "class TestMethods(unittest.TestCase):\r\n",
    "    \r\n",
    "    ##########\r\n",
    "    # Unit tests for data loading\r\n",
    "    ##########\r\n",
    "    \r\n",
    "    def test_data_path(self):\r\n",
    "        # Test that file path to dataset has been unaltered\r\n",
    "        self.assertTrue(data_path.lower() == 'bike-sharing-dataset/hour.csv')\r\n",
    "        \r\n",
    "    def test_data_loaded(self):\r\n",
    "        # Test that data frame loaded\r\n",
    "        self.assertTrue(isinstance(rides, pd.DataFrame))\r\n",
    "    \r\n",
    "    ##########\r\n",
    "    # Unit tests for network functionality\r\n",
    "    ##########\r\n",
    "\r\n",
    "    def test_activation(self):\r\n",
    "        network = NeuralNetwork(3, 2, 1, 0.5)\r\n",
    "        # Test that the activation function is a sigmoid\r\n",
    "        self.assertTrue(np.all(network.activation_function(0.5) == 1/(1+np.exp(-0.5))))\r\n",
    "\r\n",
    "    def test_train(self):\r\n",
    "        # Test that weights are updated correctly on training\r\n",
    "        network = NeuralNetwork(3, 2, 1, 0.5)\r\n",
    "        network.weights_input_to_hidden = test_w_i_h.copy()\r\n",
    "        network.weights_hidden_to_output = test_w_h_o.copy()\r\n",
    "        \r\n",
    "        network.train(inputs, targets)\r\n",
    "\r\n",
    "        # print(network.weights_input_to_hidden)\r\n",
    "        self.assertTrue(np.allclose(network.weights_hidden_to_output, \r\n",
    "                                    np.array([[ 0.37275328], \r\n",
    "                                              [-0.03172939]])))\r\n",
    "\r\n",
    "        self.assertTrue(np.allclose(network.weights_input_to_hidden,\r\n",
    "                                    np.array([[ 0.10562014, -0.20185996], \r\n",
    "                                              [0.39775194, 0.50074398], \r\n",
    "                                              [-0.29887597, 0.19962801]])))\r\n",
    "\r\n",
    "    # print(network.weights_hidden_to_output.shape)\r\n",
    "    # print(np.array([[ 0.37275328], \r\n",
    "    #                 [-0.03172939]]))\r\n",
    "\r\n",
    "    # print(network.weights_input_to_hidden)\r\n",
    "    # print(np.array([[ 0.10562014, -0.20185996], \r\n",
    "    #                 [0.39775194, 0.50074398], \r\n",
    "    #                 [-0.29887597, 0.19962801]]))\r\n",
    "\r\n",
    "    def test_run(self):\r\n",
    "        # Test correctness of run method\r\n",
    "        network = NeuralNetwork(3, 2, 1, 0.5)\r\n",
    "        network.weights_input_to_hidden = test_w_i_h.copy()\r\n",
    "        network.weights_hidden_to_output = test_w_h_o.copy()\r\n",
    "\r\n",
    "        self.assertTrue(np.allclose(network.run(inputs), 0.09998924))\r\n",
    "\r\n",
    "suite = unittest.TestLoader().loadTestsFromModule(TestMethods())\r\n",
    "unittest.TextTestRunner().run(suite)"
   ],
   "outputs": [
    {
     "output_type": "stream",
     "name": "stderr",
     "text": [
      "....."
     ]
    },
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "(20, 1)\n",
      "[[ 0.37275328]\n",
      " [-0.03172939]]\n",
      "[[ 0.10562014 -0.20185996]\n",
      " [ 0.39775194  0.50074398]\n",
      " [-0.29887597  0.19962801]]\n"
     ]
    },
    {
     "output_type": "stream",
     "name": "stderr",
     "text": [
      "\n",
      "----------------------------------------------------------------------\n",
      "Ran 5 tests in 0.005s\n",
      "\n",
      "OK\n"
     ]
    },
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "<unittest.runner.TextTestResult run=5 errors=0 failures=0>"
      ]
     },
     "metadata": {},
     "execution_count": 23
    }
   ],
   "metadata": {
    "collapsed": true,
    "editable": true,
    "jupyter": {
     "outputs_hidden": true
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "## Training the network\n",
    "\n",
    "Here you'll set the hyperparameters for the network. The strategy here is to find hyperparameters such that the error on the training set is low, but you're not overfitting to the data. If you train the network too long or have too many hidden nodes, it can become overly specific to the training set and will fail to generalize to the validation set. That is, the loss on the validation set will start increasing as the training set loss drops.\n",
    "\n",
    "You'll also be using a method know as Stochastic Gradient Descent (SGD) to train the network. The idea is that for each training pass, you grab a random sample of the data instead of using the whole data set. You use many more training passes than with normal gradient descent, but each pass is much faster. This ends up training the network more efficiently. You'll learn more about SGD later.\n",
    "\n",
    "### Choose the number of iterations\n",
    "This is the number of batches of samples from the training data we'll use to train the network. The more iterations you use, the better the model will fit the data. However, this process can have sharply diminishing returns and can waste computational resources if you use too many iterations.  You want to find a number here where the network has a low training loss, and the validation loss is at a minimum. The ideal number of iterations would be a level that stops shortly after the validation loss is no longer decreasing.\n",
    "\n",
    "### Choose the learning rate\n",
    "This scales the size of weight updates. If this is too big, the weights tend to explode and the network fails to fit the data. Normally a good choice to start at is 0.1; however, if you effectively divide the learning rate by n_records, try starting out with a learning rate of 1. In either case, if the network has problems fitting the data, try reducing the learning rate. Note that the lower the learning rate, the smaller the steps are in the weight updates and the longer it takes for the neural network to converge.\n",
    "\n",
    "### Choose the number of hidden nodes\n",
    "In a model where all the weights are optimized, the more hidden nodes you have, the more accurate the predictions of the model will be.  (A fully optimized model could have weights of zero, after all.) However, the more hidden nodes you have, the harder it will be to optimize the weights of the model, and the more likely it will be that suboptimal weights will lead to overfitting. With overfitting, the model will memorize the training data instead of learning the true pattern, and won't generalize well to unseen data.  \n",
    "\n",
    "Try a few different numbers and see how it affects the performance. You can look at the losses dictionary for a metric of the network performance. If the number of hidden units is too low, then the model won't have enough space to learn and if it is too high there are too many options for the direction that the learning can take. The trick here is to find the right balance in number of hidden units you choose.  You'll generally find that the best number of hidden nodes to use ends up being between the number of input and output nodes."
   ],
   "metadata": {
    "editable": true
   }
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "source": [
    "import sys\r\n",
    "\r\n",
    "####################\r\n",
    "### Set the hyperparameters in you myanswers.py file ###\r\n",
    "####################\r\n",
    "from my_answers import NeuralNetwork\r\n",
    "from my_answers import iterations, learning_rate, hidden_nodes, output_nodes\r\n",
    "\r\n",
    "# from my_answers_gh0 import NeuralNetwork\r\n",
    "# from my_answers_gh0 import iterations, learning_rate, hidden_nodes, output_nodes\r\n",
    "\r\n",
    "print(\r\n",
    "    'iterations:', iterations,'  \\n', \r\n",
    "    'learning_rate:', learning_rate,'  \\n', \r\n",
    "    'hidden_nodes:',hidden_nodes,'  \\n', \r\n",
    "    'output_nodes:', output_nodes,'  \\n'\r\n",
    "    )\r\n",
    "\r\n",
    "N_i = train_features.shape[1]\r\n",
    "network = NeuralNetwork(N_i, hidden_nodes, output_nodes, learning_rate)\r\n",
    "\r\n",
    "losses = {'train':[], 'validation':[]}\r\n",
    "for ii in range(iterations):\r\n",
    "    # Go through a random batch of 128 records from the training data set\r\n",
    "    batch = np.random.choice(train_features.index, size=128)\r\n",
    "    # X, y = train_features.ix[batch].values, train_targets.ix[batch]['cnt']\r\n",
    "    X, y = train_features.loc[batch].values, train_targets.loc[batch]['cnt']\r\n",
    "\r\n",
    "    # print('X','y')\r\n",
    "    # print(type(X),type(y))\r\n",
    "    network.train(X, y.to_numpy())\r\n",
    "    \r\n",
    "    # Printing out the training progress\r\n",
    "    train_loss = MSE(network.run(train_features).T, train_targets['cnt'].values)\r\n",
    "    val_loss = MSE(network.run(val_features).T, val_targets['cnt'].values)\r\n",
    "    # sys.stdout.write(\"\\iterations {:2.1f} out of {:2.1f}\".format(ii,float(iterations))) \r\n",
    "    sys.stdout.write(\"\\rProgress: {:2.1f}\".format(100 * ii/float(iterations)) \\\r\n",
    "                     + \"% ...  Training loss: \" + str(train_loss)[:5] \\\r\n",
    "                     + \" ...  Validation loss: \" + str(val_loss)[:5] \\\r\n",
    "                     + \" ...  iterations {:2.1f} out of {:2.1f}\".format(ii,float(iterations))  )\r\n",
    "    sys.stdout.flush()\r\n",
    "    \r\n",
    "    losses['train'].append(train_loss)\r\n",
    "    losses['validation'].append(val_loss)"
   ],
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "iterations: 5000   \n",
      " learning_rate: 0.75   \n",
      " hidden_nodes: 20   \n",
      " output_nodes: 1   \n",
      "\n",
      "Progress: 100.0% ...  Training loss: 0.073 ...  Validation loss: 0.184 ...  iterations 4999.0 out of 5000.0"
     ]
    }
   ],
   "metadata": {
    "collapsed": true,
    "editable": true,
    "jupyter": {
     "outputs_hidden": true
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "### runs and scores\r\n"
   ],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "\r\n",
    "\r\n",
    "iterations: 5000   \r\n",
    " learning_rate: 0.75   \r\n",
    " hidden_nodes: 24   \r\n",
    " output_nodes: 1   \r\n",
    "\r\n",
    "Progress: 100.0% ...  \r\n",
    "Training loss: 0.064 ... \r\n",
    " Validation loss: 0.129 ...  \r\n",
    " iterations 4999.0 out of 5000.0\r\n",
    "\r\n",
    "\r\n",
    "------------------------------------------------------------------\r\n",
    "\r\n",
    "iterations: 2500   \r\n",
    " learning_rate: 0.75   \r\n",
    " hidden_nodes: 24   \r\n",
    " output_nodes: 1   \r\n",
    "\r\n",
    "Progress: 100.0% ...   \r\n",
    "Training loss: 0.107 ...  \r\n",
    "Validation loss: 0.220 ...  \r\n",
    "iterations 2499.0 out of 2500.0\r\n",
    "\r\n",
    "------------------------------------------------------------------\r\n",
    "\r\n",
    "iterations: 2500   \r\n",
    " learning_rate: 0.75   \r\n",
    " hidden_nodes: 12   \r\n",
    " output_nodes: 1   \r\n",
    "\r\n",
    "Progress: 100.0% ...   \r\n",
    "Training loss: 0.073 ...   \r\n",
    "Validation loss: 0.185 ...   \r\n",
    "iterations 2499.0 out of 2500.0\r\n",
    "\r\n",
    "------------------------------------------------------------------\r\n",
    "\r\n",
    "iterations: 5000  \r\n",
    " learning_rate: 0.75  \r\n",
    " hidden_nodes: 12  \r\n",
    " output_nodes: 1  \r\n",
    "\r\n",
    "Progress: 100.0% ...  \r\n",
    "Training loss: 0.056 ...  \r\n",
    "Validation loss: 0.135 ...  \r\n",
    "iterations 4999.0 out of 5000.0\r\n",
    "\r\n",
    "------------------------------------------------------------------\r\n",
    "\r\n",
    "iterations: 5000  \r\n",
    " learning_rate: 0.2   \r\n",
    " hidden_nodes: 100   \r\n",
    " output_nodes: 1   \r\n",
    "\r\n",
    "Progress: 100.0% ...  \r\n",
    "Training loss: 0.219 ...  \r\n",
    "Validation loss: 0.381 ...  \r\n",
    "iterations 4999.0 out of 5000.0\r\n",
    "\r\n",
    "------------------------------------------------------------------\r\n",
    "\r\n",
    "iterations: 5000  \r\n",
    " learning_rate: 0.75  \r\n",
    " hidden_nodes: 20  \r\n",
    " output_nodes: 1  \r\n",
    "\r\n",
    "Progress: 100.0% ...  \r\n",
    "Training loss: 0.068 ...  \r\n",
    "Validation loss: 0.174 ...  \r\n",
    "iterations 4999.0 out of 5000.0\r\n",
    "\r\n",
    "------------------------------------------------------------------\r\n",
    "\r\n",
    "iterations: 5000  \r\n",
    " learning_rate: 1.0  \r\n",
    " hidden_nodes: 20  \r\n",
    " output_nodes: 1  \r\n",
    "\r\n",
    "Progress: 100.0% ...  \r\n",
    "Training loss: 0.075 ...  \r\n",
    "Validation loss: 0.171 ...  \r\n",
    "iterations 4999.0 out of 5000.0\r\n",
    "\r\n",
    "\r\n",
    "------------------------------------------------------------------\r\n",
    "\r\n",
    "iterations: 5000  \r\n",
    " learning_rate: 1.1  \r\n",
    " hidden_nodes: 20  \r\n",
    " output_nodes: 1  \r\n",
    "\r\n",
    "Progress: 100.0% ...  \r\n",
    "Training loss: 0.073 ...  \r\n",
    "Validation loss: 0.151 ...  \r\n",
    "iterations 4999.0 out of 5000.0\r\n",
    "\r\n",
    "------------------------------------------------------------------\r\n",
    "\r\n",
    "iterations: 5000  \r\n",
    " learning_rate: 0.5  \r\n",
    " hidden_nodes: 20  \r\n",
    " output_nodes: 1  \r\n",
    "\r\n",
    "Progress: 100.0% ...  \r\n",
    "Training loss: 0.058 ...  \r\n",
    "Validation loss: 0.155 ...  \r\n",
    "iterations 4999.0 out of 5000.0\r\n",
    "\r\n",
    "------------------------------------------------------------------\r\n",
    "\r\n",
    "iterations: 5000  \r\n",
    " learning_rate: 0.05  \r\n",
    " hidden_nodes: 20  \r\n",
    " output_nodes: 1  \r\n",
    "\r\n",
    "Progress: 100.0% ...  \r\n",
    "Training loss: 0.267 ...  \r\n",
    "Validation loss: 0.440 ...  \r\n",
    "iterations 4999.0 out of 5000.0\r\n",
    "\r\n",
    "------------------------------------------------------------------"
   ],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "### "
   ],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "### output code"
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "source": [
    "# type(train_features.loc[batch].values)\r\n",
    "\r\n",
    "batch = np.random.choice(train_features.index, size=512)\r\n",
    "# X, y = train_features.ix[batch].values, train_targets.ix[batch]['cnt']\r\n",
    "X, y = train_features.loc[batch].values, train_targets.loc[batch]['cnt']\r\n",
    "\r\n",
    "y = network.run(X)\r\n",
    "\r\n",
    "xy = np.concatenate([X,y],axis=1)\r\n",
    "\r\n",
    "df = pd.DataFrame(xy)\r\n",
    "df = df.sort_values(56)\r\n",
    "\r\n",
    "df = df.rename(columns = \r\n",
    "{0:'yr', 1:'holiday', 2:'temp', 3:'hum', 4:'windspeed', 5:'season_1', 6:'season_2',7:'season_3', 8:'season_4', 9:'weathersit_1', 10:'weathersit_2', 11:'weathersit_3',12:'weathersit_4', 13:'mnth_1', 14:'mnth_2', 15:'mnth_3', 16:'mnth_4', 17:'mnth_5',18:'mnth_6', 19:'mnth_7', 20:'mnth_8', 21:'mnth_9', 22:'mnth_10', 23:'mnth_11', 24:'mnth_12',25:'hr_0', 26:'hr_1', 27:'hr_2', 28:'hr_3', 29:'hr_4', 30:'hr_5', 31:'hr_6', 32:'hr_7', 33:'hr_8',34:'hr_9', 35:'hr_10', 36:'hr_11', 37:'hr_12', 38:'hr_13', 39:'hr_14', 40:'hr_15', 41:'hr_16',42:'hr_17', 43:'hr_18', 44:'hr_19', 45:'hr_20', 46:'hr_21', 47:'hr_22', 48:'hr_23',49:'weekday_0', 50:'weekday_1', 51:'weekday_2', 52:'weekday_3', 53:'weekday_4',54:'weekday_5', 55:'weekday_6',56:'y_hat'}\r\n",
    ")\r\n",
    "\r\n",
    "# print(features.columns)\r\n",
    "# print(data.columns)\r\n",
    "df.head(20)\r\n",
    "\r\n",
    "df.to_csv(r'C:\\Users\\JGarza\\GitHub\\UdacityDeepLearning_NanoDegree\\bike_project\\xy_data.csv')\r\n",
    "\r\n"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "### graph the trainning"
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "source": [
    "plt.plot(losses['train'], label='Training loss')\r\n",
    "plt.plot(losses['validation'], label='Validation loss')\r\n",
    "plt.legend()\r\n",
    "_ = plt.ylim()"
   ],
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
      "image/png": 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uuIDp06ezePFiNtlkE771rW8xceJENtpoo8a+hx56KO+88w6zZs3itttu4/3332fgwIEccsghjB8/vnHHnA022IAf/ehHzJgxg1mzZvHOO+/Qp08ftttuO84880yOPfbYtRpz3oQLI+tFxOxBgwYNau4xwO1p5ao6tj3rXz/Uf/3BgfTs6u9UkqS2M3fuXAB22mmnKo9Enc23v/1tfvazn/HQQw/x6U9/utrDya3W/owNHjyYOXPmzEkpDV7dz7DGPQeyvzr5u5QkSepo5faaf/LJJ/nFL37BJptswtChQ6swKjXltG4OGNQlSVK17bTTTgwaNIidd96Z7t2789xzzzWW+VxxxRWNe8mrevwvkAPWtEuSpGr7xje+wZ133skNN9zA+++/z/rrr8/o0aM544wzGDZsWLWHJwzuuWSMlyRJHe3888/n/PPPr/Yw1AJr3HPAUhlJkiRVYnDPIXf6kSRJUpbBXZIkSeoEDO454AS7JEmSKjG450B2VxlzvCRJkrIM7pIkSVInYHDPgWypjKUzkiRJyjK454A5XZIkSZUY3CVJkqROwOCeAyX7tjsFL0lSp/biiy8SEXz1q18tav/KV75CRPDaa6+1+l6f+MQn2Hbbbdt6iEWaG281/f73vyciOO+886o9lNxok+AeEWMi4rKImBUR70VEiojrV+P6awvXpIho9n+ZEXFsRDwREe9HxKKImBkRo9viO1STOV2SpPZ31FFHERFceeWVFfvuv//+RATTpk3rgJG1v5UrVxIR7LffftUeitZCW824TwTGAbsBr6/OhRFxCPD/Ae9X6HcxMAUYCFwNXA/sCvwmIsat/pDzK7s9pCRJWnsnnXQSAFdffXWL/ebNm8d9993HwIEDGT26becHL7roIubOncuAAQPa9L5ra4sttmDu3LnObudcWwX304DtgT7Aya29KCI2pD6E3wjMbqHfMGA88BLwqZTSaSmlbwKDgYXAxRGx5ZoOvtrcRUaSpPY3cuRItt9+e55++mnmzJnTbL9rrrmGlBLHH388tbW1bTqGgQMHsuOOO7b5fdfWOuusw4477pi7XyhUrE2Ce0ppRkrphVRSrF3RLwrHb1bo9/XC8YcppXebfO484AqgG3D8an52fhjcJUnqECeeeCLQ/Kz7qlWrmDJlSkm99+uvv873v/99hg0bxoABA+jatSubbropRx99NM8++2yrP7+5GveUEj/72c/45Cc/Sbdu3dh000055ZRTeO+998re55///CcXXngh++yzD5tuuildu3Zlo4024vDDD+eJJ54o6nvNNdewzjrrAHDfffcREY2vhhn2lmrc33jjDU4++WS22GILunXrxkYbbcQRRxzB008/XdL3mmuuISK4/vrrue+++xgxYgTrrrsuffv25ZBDDuG5555r9d9VS5577jmOOeYYNtlkE7p27comm2zCsccey0svvVTS97333uP73/8+u+yyC71796Z3795su+22fPnLXy75DtOmTWPUqFEMGDCg8b/DyJEjueqqq9pk3GuraotTI+I44HDg6ymlf1ToPqpwvLvMubsyfTo9Z+AlSWofxx57LF27duX//u//WLp0acn5O++8k9dff5399tuPrbbaqrF9xowZXHjhhfTr148jjjiCU089lSFDhnDTTTcxZMgQnnnmmbUa17hx4/j2t7/NokWL+NrXvsaXvvQlpk+fzgEHHMCKFStK+j/zzDNMnDiR2tpaDjnkEE4//XT23Xdffve737H33nvz+9//vrHvoEGDmDRpEgBbbbUVZ599duNr+PDhLY7rpZdeYvDgwVx11VVsv/32nH766ey///785je/Ya+99uKuu+4qe920adP47Gc/y3rrrcfJJ5/MsGHDmD59OiNGjGDhwoVr8TcFjz32GHvuuSc33HADQ4cOZfz48QwdOpRf/epX7LHHHkX/mpJS4oADDuCcc86hb9++nHjiiXz9619nzz33ZMaMGTz++OONff/nf/6Hz3/+8zz77LMceuihjB8/noMOOoglS5Zw3XXXrdWY20xKqU1fwEjq55Cvb6HPFsAi4FdN2mYWrts207dXoX1xM/fqXzj/VivHN7uZ15JBgwalanh3yfK0xZnTG18L319elXFIkj66/vrXv6a//vWv1R5GLnzxi19MQJo8eXLJuUMPPTQB6eabby5qnz9/flq8eHFJ/zlz5qSePXum0aNHF7W/8MILCUgnnHBCUfvRRx+dgPTqq682tj3wwAMJSNttt11auHBhY/vSpUvTnnvumYC0zTbbFN3n3XffTQsWLCgZz7x589LGG2+cdtlll6L2FStWJCDtu+++Jde0NN5Ro0YlIF1wwQVF7Q8++GCqqalJ/fv3T0uWLGlsv/rqqxOQamtr04wZM4qumTBhQgLSJZdcUnYMWb/73e8SkM4999zGtlWrVqXtttsuAWnq1KlF/a+//voEpJ133jnV1dWllOr/+wBpzJgxJfdfuXJl0d/3pz71qdS9e/f0zjvvlPQt15bV2p+xQYMGJWB2WoOc3eEFVhFRA1xH/WLUU1pxSd/CcVEz5xva11vLoVWNu0FKkqrunL6V++TFOc1FgtY56aSTuOmmm7jmmms47rjjGtvffPNN7rzzTjbeeGMOO+ywoms23njjsvfafffdGTFiBPfddx+rVq2iS5cuqz2eyZMnAzBp0iTWX3/9xvYePXrwox/9iP3337/kmvXWKx97tthiC77whS9w5ZVX8sYbb7DJJpus9ngazJs3j/vvv5+tttqK8ePHF537zGc+wxe/+EWmTp3KtGnTOOqoo4rOH3300YwcObKo7aSTTuLiiy8uKeVZHbNmzeKFF17gM5/5DF/60pdKPvPyyy/nscce49FHH2XYsGGN53r06FFyry5duhT9fUN9rX9DWVFT/fv3X+Mxt6VqlMqcBowATkxN6tXbQKvybkppcLkX0PoCtTZmUJckqeOMGjWKbbbZhocffpi5c+c2tk+ePJmVK1dy3HHHlQ1vd9xxB5/73OcYMGAA66yzTmOd+F133cUHH3ywxiUgDaUdI0aMKDk3fPhwamrKx7VZs2Zx5JFHstlmm9GtW7fG8TRsd/n666u10V+Jhvrv4cOHl11MO2rUqKJ+Te2xxx4lbZttthkA77675vGv4e+q4bMrjWnXXXdl11135Ve/+hWf+cxnuOiii3j00UfLlh8dffTRLF68mE9+8pOcfvrp3H777SxYsGCNx9oeOnTGPSK2A34ITE4p3dnKyxp+rW5uKqDSjLwkSVKjhkWY3/3ud7nmmmu45JJLSClx7bXXNrtA88c//jHjx4+nX79+7LfffmyxxRb06NGDiOC2227jz3/+M8uXL1+j8SxaVB9hys3qd+3atWRWGODmm29m7Nix9OjRg/3335+tt96aXr16UVNTw/3338+sWbPWeDzZcQ0cOLDs+Yb2f/7znyXnyv2LQEP4X7VqVYeNqba2lpkzZ/L973+fW2+9lTPOOAOAPn36cNxxx/GjH/2IXr16AXDGGWew0UYbceWVV3LppZfyk5/8hIhgn3324aKLLmLQoEFrPO620tGlMjtT2AEmIprbBeaFiAD4fEppWkppSUS8DmwaEQNTSm9m+m9XOD7fPkNufylTK5N9L0lSu1vL8pPO5vjjj+d73/sev/zlLzn//POZNWsWL7/8MqNGjSp5SumKFSs455xz2GSTTZgzZ05JwJ41a9ZajaVv3/o5yLfeeovNN9+86NyHH37Iu+++WxKEJ02aRPfu3Zk9ezY77LBD0blXX311rcfUdFzz588ve/7NN98s6tcR1mRM/fr146c//Sk//elPeeGFF5g5cyY///nP+dnPfsZ7773XWKoEcNxxx3Hcccfx7rvv8sgjj3DbbbcxefJkDjzwQJ599lk22GCDdvx2lXV0qcw84NpmXg3/BW4uvJ/X5Lr7C8fPlrnnQZk+nY4xXZKkjrXxxhtz6KGHsmDBAqZNm9a4PWTDQ5qaeuutt1i8eDF77713SWh/7733ypaKrI6GmdwHHnig5NyDDz5IXV1dSftLL73ELrvsUhLaV61axcMPP1zSv6HcZnVmu3fffXeg/heTctfNmDGjaPwdoWFMM2fOLHu+ob25MW233XaceOKJPPDAA/To0aPZJ+Ouv/76fO5zn+Paa6/lmGOOYcGCBTz00ENrPf611aHBPaX0h5TSV8u9gIaNPf+r0PaHJpc2bJ55VkQ0/ntR4aFL3wSWA5P5iDDIS5LU/hr2dL/kkkuYNm0a/fv35/Of/3xJv4EDB9K9e3eefPJJlixZ0tj+4Ycf8q1vfWutarahfvYf4Nxzzy0qO/nggw/4r//6r7LXbLHFFjz33HNFM88pJb73ve+V3Su9pqaG9ddfn7///e+tHteWW27JPvvsw0svvcRll11WdO7hhx/mxhtvZIMNNihZyNuehg8fzrbbbsvMmTNLQvfUqVN55JFH2Gmnndhrr72A+l9wmq5jaPDuu++yYsUKevbs2dh29913s3LlyqJ+KSXefvttgKK+1dImpTIRcTj1e7IDNDxya6+ImFL484KU0oQ1vX9K6ZGI+DFwOvCniLgF6Ap8CegHfCvVP4ypU7IyRpKkjnfAAQew1VZbNe5yMm7cOLp27VrSr0uXLowbN46LL76YXXfdlUMPPZTly5dz//33s2jRIkaMGFF2try1hg8fzsknn8yVV17JzjvvzJgxY6itrWXatGlsuOGGbLTRRiXXnHbaaYwbN47ddtuNI444gtraWmbNmsXzzz/P6NGjmT59esk1++67L7fccguHHXYYu+++O7W1tYwcOZK999672bH9/Oc/Z++99+a0007jrrvuYvDgwfz973/n5ptvpra2lilTpjTWiHeEmpoarrvuOg444ACOOOIIDj/8cHbYYQeeffZZbr/9dvr06cMvf/lLCmXXPP300xx55JHsscce7LLLLgwcOJC3336b22+/nZUrV3LmmWc23nvMmDH07t2bvffemy233JJVq1Yxa9YsnnrqKYYMGcI+++zTYd+zOW01474bcGzhdWChbesmbWPW9gNSSuOB46gvqTkJ+A/gL8AhKaXL1/b+1ZScY5ckqcNFBCeccELj+4YZ+HLOP/98LrzwQrp168bPf/5zpk2bxtChQ3nyySf5xCc+sdZjufzyy7n00kvp06cPV111FVOnTuXggw/m3nvvLbvDzTe/+U2uvfZaNt54YyZPnswNN9zAlltuyeOPP86//du/lf2Myy67jLFjx/Loo49y7rnnMmnSpGZLThpst912zJ49m6997WvMnTuXiy++mLvvvpvPfe5zPPzww4wePXqtv/vqGjZsGE8++SRjx47lkUceadwp5qijjuKpp54q2tFm6NCh/Od//ifrrLMOd911F5dccgn33HMPQ4YM4e677+aUU/61M/mFF17I0KFDmT17NldccQVTpkxh1apVXHjhhdx3331ld9bpaOFCyHoRMXvQoEGDZs+e3eGf/fbiZQz54X2N7588az827N2tw8chSfroaigX2Gmnnao8EumjqbU/Y4MHD2bOnDlzCtuRr5Zq7OOuLH93kiRJUgUG9xzI5nZLZyRJkpRlcJckSZI6AYN7DrjMQJIkSZUY3HOgpDTGIC9JkqQMg7skSZLUCRjcc8BSGUmSpM6ro7ZXN7jnQOmuMpIkta2GJ0nW1dVVeSTSR09DcG/4OWsvBvcc8CFYkqT21q1b/YP9lixZUuWRSB89DT9XDT9n7cXgnkPmeElSW+vduzcA8+fPZ/HixdTV1TlxJK2FlBJ1dXUsXryY+fPnA//6OWsvte16d7WK/78pSWpv/fr1Y8mSJSxdupTXXnut2sORPnJ69uxJv3792vUzDO6SJH0M1NTUsNlmm7Fw4UIWL17M8uXLnXGX1lJE0K1bN3r37k2/fv2oqWnfYhaDew6V7OsuSVIbqKmpoX///vTv37/aQ5G0BqxxzwEnPCRJklSJwT0HsjPsBnlJkiRlGdwlSZKkTsDgngPOsEuSJKkSg3sO+ORUSZIkVWJwlyRJkjoBg3sOZPfRdV9dSZIkZRncc8CYLkmSpEoM7pIkSVInYHDPgWxljJUykiRJyjK454JJXZIkSS0zuEuSJEmdgHGdphQAACAASURBVME9ByyNkSRJUiUG9xwwt0uSJKkSg3sOuDhVkiRJlRjcJUmSpE7A4J4DKVMsk30vSZIkGdxzwNIYSZIkVWJwlyRJkjoBg3sOuDhVkiRJlRjcc8CadkmSJFVicM8hY7wkSZKyDO45YGmMJEmSKjG4S5IkSZ1AmwT3iBgTEZdFxKyIeC8iUkRc30zf7SLizIi4PyJejYgPI+KtiLg9Ivap8DnHRsQTEfF+RCyKiJkRMbotvkOeJKfgJUmSlNFWM+4TgXHAbsDrFfqeC1wAbAzcCVwCPAx8Drg/Ik4pd1FEXAxMAQYCVwPXA7sCv4mIcWv/FarHnC5JkqRKatvoPqcBrwEvAiOAGS30vRv475TS000bI2IE8Dvgooi4OaX0ZpNzw4DxwEvAnimldwvtFwGzgYsjYnpKaV4bfZ8O1bCrTHeWs4xuLk6VJElSiTaZcU8pzUgpvZBaUeORUpqSDe2F9geAmUBXYFjm9NcLxx82hPbCNfOAK4BuwPFrNvp8OKN2Ks90O4Gza6+r9lAkSZKUQ3lbnLqicFyZaR9VON5d5pq7Mn06nZTgG7V3UBt1HF97D7FyWbWHJEmSpJxpq1KZtRYRWwD7AkuBB5u09wI2Bd5vWj7TxAuF4/at/JzZzZzasfWjbVvZf6ZIdXVVGYckSZLyKxfBPSK6ATdQX/JyRtNyGKBv4biomcsb2tdrp+G1u9IKo6jKOCRJkpRfVQ/uEdEF+BXwaeBG4OI1vFWr1nSmlAY3M47ZwKA1/Ow25vJUSZIkFatqjXshtF8PHAncBHylzALXhhn1vpRXaUY+90piuhPukiRJyqhacI+IWuD/AWOB/wOOSillF6WSUlpC/d7w60bEwDK32q5wfL69xtre3MddkiRJlVQluEdEV+AW6mfafwkck1Ja1cIl9xeOny1z7qBMn84nk9wN8pIkScrq8OBeWIj6a+Aw4Frg+JRSpW1Uriocz4qI9Zvca0vgm8ByYHKbD7bDuIuMJEmSWtYmi1Mj4nDg8MLbAYXjXhExpfDnBSmlCYU/XwUcDCygvgTmexElRd0zU0ozG96klB6JiB8DpwN/iohbqH9Q05eAfsC3OutTUwFSXWaK3Rl3SZIkZbTVrjK7Acdm2rYuvAD+BjQE960Kx/7A91q458ymb1JK4yPiT8A44CTqp6nnABellKav8chzIbXwTpIkSWqj4J5SOgc4p5V9R67F51wHXLem1+dXySOYqjIKSZIk5VdVt4NUvWypjItTJUmSlGVwzwVn3CVJktQyg3sOlD5zSpIkSSpmcM+BkuBecXdMSZIkfdwY3HMgLI2RJElSBQb3HChdnGqQlyRJUjGDew4kn5wqSZKkCgzuuZCtcXfGXZIkScUM7jkQ2VKZKo1DkiRJ+WVwz4GSoO6MuyRJkjIM7jlQshukc+6SJEnKMLjngk9OlSRJUssM7nngA5ckSZJUgcE9B0r2bTfHS5IkKcPgnguWxkiSJKllBvccKH1SqkFekiRJxQzueWCNuyRJkiowuOeSM+6SJEkqZnDPgWypTKozuEuSJKmYwT0HwielSpIkqQKDey5kZtzdD1KSJEkZBvccMKhLkiSpEoN7HmRq2i2dkSRJUpbBPYeM7ZIkScoyuOdAyQOYnHGXJElShsE9D7LbQVZpGJIkScovg3sOpJInpxrdJUmSVMzgngsGdUmSJLXM4J4LmVIZa9wlSZKUYXDPgZJKGUmSJCnD4J4L7iojSZKklhnccyB8cqokSZIqMLjnQKrLzrA74y5JkqRiBvdcyC5OrdIwJEmSlFsG91ywxl2SJEktM7jnQGlON7hLkiSpWJsE94gYExGXRcSsiHgvIlJEXF/hmmERcWdELIyIpRHxp4g4NSK6tHDNsRHxRES8HxGLImJmRIxui+9QTVGS3A3ukiRJKtZWM+4TgXHAbsDrlTpHxGHAg8Bw4NfAFUBX4CfA1GauuRiYAgwErgauB3YFfhMR49b6G1RRclcZSZIkVdBWwf00YHugD3BySx0jog/1wXsVMDKldEJK6TvUh/5HgTERMTZzzTBgPPAS8KmU0mkppW8Cg4GFwMURsWUbfZcOV/KkVGvcJUmSlNEmwT2lNCOl9EIqSaBljQE2BKamlJ5qco9l1M/cQ2n4/3rh+MOU0rtNrplH/Wx9N+D4NRx+1YWlMZIkSaqgGotTRxWOd5c59yCwFBgWEd1aec1dmT6djhPukiRJqqS2Cp+5Q+H4fPZESmllRLwC7AxsDcyNiF7ApsD7KaU3y9zvhcJx+9Z8eETMbubUjq25vl0ka9wlSZLUsmrMuPctHBc1c76hfb017N8JuauMJEmSWlaNGfdKonBc3fTaqv4ppcFlP7R+Jn7Qan5m20g+OVWSJEktq8aMe8MMed9mzvfJ9KvUv9KMfKfjYlVJkiRlVSO4P1c4ltSkR0QtsBWwEngZIKW0hPq94deNiIFl7rdd4VhSM99ZtG4zHkmSJH2cVSO43184frbMueFAT+CRlNLyVl5zUKZPJ5QtlXGxqiRJkopVI7jfAiwAxkbEHg2NEdEdOK/w9srMNVcVjmdFxPpNrtkS+CawHJjcTuNtf864S5IkqYI2WZwaEYcDhxfeDigc94qIKYU/L0gpTQBIKb0XESdSH+BnRsRU6p9+eij1W0XeAtzY9P4ppUci4sfA6cCfIuIWoCvwJaAf8K3Cw5g6pWypjKUzkiRJymqrXWV2A47NtG1deAH8DZjQcCKlNC0iRgBnAUcA3YEXqQ/mPyv3BNaU0viI+BMwDjgJqAPmABellKa30feoChejSpIkqZI2Ce4ppXOAc1bzmoeBg1fzmuuA61bnms4g+3uKQV6SJElZ1ahxV5b7uEuSJKkCg3su+ORUSZIktczgnguphXeSJEmSwT0f6jJR3VoZSZIkZRjcc8GgLkmSpJYZ3HMhs6uMM+6SJEnKMLjngUFdkiRJFRjccyG7ONUgL0mSpGIG9xwo86BYSZIkqYjBPQ9KtnE3yEuSJKmYwT0X6oreGdslSZKUZXDPBXeVkSRJUssM7nmQXJwqSZKklhncc8AJdkmSJFVicM+BcIZdkiRJFRjc8yBlFqc6BS9JkqQMg3suGNQlSZLUMoN7HriPuyRJkiowuOeBQV2SJEkVGNxzIGVq3C2dkSRJUpbBPQeyu8q4OFWSJElZBvccyMZ0t4eUJElSlsE9D0qenCpJkiQVM7jnQiaqWyojSZKkDIN7HhjUJUmSVIHBPReyM+7VGYUkSZLyy+CeB864S5IkqQKDey5l93WXJEnSx53BPQ+ccZckSVIFBvdcyAZ3g7wkSZKKGdxzIfvk1CoNQ5IkSbllcM+DbFI3uUuSJCnD4J4LBnVJkiS1zOCeB864S5IkqQKDex6Y0yVJklSBwT0HUmbfdnO8JEmSsgzuORBGdUmSJFVQ1eAeEZ+LiHsj4rWI+CAiXo6ImyNir2b6D4uIOyNiYUQsjYg/RcSpEdGlo8fepjI17QZ5SZIkZVUtuEfEfwPTgUHA3cBPgTnAYcDDEfGVTP/DgAeB4cCvgSuArsBPgKkdN/L2l1ycKkmSpIzaanxoRAwAJgBvAZ9KKb3d5Nw+wP3AD4DrC219gKuBVcDIlNJThfZJhb5jImJsSqmTBnifnCpJkqSWVWvGfYvCZz/eNLQDpJRmAIuBDZs0jym8n9oQ2gt9lwETC29PbtcRt6fk4lRJkiS1rFrB/QXgQ2BIRPRveiIihgO9gd83aR5VON5d5l4PAkuBYRHRrR3G2v4yST0slZEkSVJGVUplUkoLI+JM4MfAXyNiGvAPYBvgUOB3wNeaXLJD4fh8mXutjIhXgJ2BrYG5LX12RMxu5tSOq/Ul2pRBXZIkSS2rSnAHSCldGhHzgP8FTmxy6kVgSqaEpm/huKiZ2zW0r9emg+womRl2F6dKkiQpq5q7ypwB3AJMoX6mvRcwGHgZuCEiLlyd2xWOFRNvSmlwuRfw7Gp9gbZUEtQN7pIkSSpWleAeESOB/wbuSCmdnlJ6OaW0NKU0B/g88DowPiK2LlzSMKPet/RuAPTJ9OtkDOqSJElqWbVm3EcXjjOyJ1JKS4EnqB/b7oXm5wrH7bP9I6IW2ApYSf1sfSeUeQCTpTKSJEnKqFZwb9j9ZcNmzje0f1g43l84frZM3+FAT+CRlNLythleBzOoS5IkqYJqBfdZheNJEbFp0xMRcRDwaWAZ8Eih+RZgATA2IvZo0rc7cF7h7ZXtOuJ25eJUSZIktaxau8rcQv0+7fsBcyPi18B8YCfqy2gC+M+U0j8AUkrvRcSJhetmRsRUYCH1W0fuUGi/scO/hSRJktRBqrWPe11EHAx8ExhL/YLUntSH8TuBn6WU7s1cMy0iRgBnAUcA3anfOvL0Qv9OO00dmSenulhVkiRJWdXcx30FcGnh1dprHgYObrdBVY1BXZIkSS2r2j7uaqIktxvkJUmSVMzgngvZxalVGoYkSZJyy+CeB5mkHs64S5IkKcPgnguphXeSJEmSwT0fsrUx1spIkiQpw+CeCwZ1SZIktczgngMlNe3OuEuSJCnD4J4H5nRJkiRVYHDPgURd5r1JXpIkScUM7jng9o+SJEmqxOCeB5ncbpCXJElSlsE9h1ybKkmSpCyDex6kumxDVYYhSZKk/DK4S5IkSZ2AwT0PfHKqJEmSKjC454JBXZIkSS0zuOeAT06VJElSJQb3PDCoS5IkqQKDey5kg7tBXpIkScUM7rlgUJckSVLLDO55UJLbDfKSJEkqZnDPgeziVEveJUmSlGVwz4XipF6yy4wkSZI+9gzueZCccZckSVLLDO654K4ykiRJapnBPQcsjZEkSVIlBvc8yNbGWCsjSZKkDIN7LhjUJUmS1DKDex6UzLAb5CVJklTM4J5DVspIkiQpy+CeB8l93CVJktQyg3supBbeSZIkSQb3fLDGXZIkSRUY3HPBoC5JkqSWGdxzIFvTHq5OlSRJUobBPQ8M6pIkSaqg6sE9Ij4TEbdGxJsRsbxwvDciDi7Td1hE3BkRCyNiaUT8KSJOjYgu1Rh728ksTjXIS5IkKaO2mh8eEROBc4EFwHTgTaA/sDswErizSd/DgFuBZcCNwELgEOAnwKeBIztw6G3LnC5JkqQKqhbcI+JI6kP774EvpJQWZ86v0+TPfYCrgVXAyJTSU4X2ScD9wJiIGJtSmtpR429b7uMuSZKkllWlVCYiaoD/BpYCR2VDO0BKaUWTt2OADYGpDaG90GcZMLHw9uT2G3H7yi5GtVJGkiRJWdWacR8GbAXcArwbEZ8DdqG+DOaJlNKjmf6jCse7y9zrQep/ARgWEd1SSsvbacztyH3cJUmS1LJqBfc9C8e3gDnArk1PRsSDwJiU0juFph0Kx+ezN0oprYyIV4Cdga2Bue0y4nZlUJckSVLLqhXcNyocvw68AuwHPA5sAVwCHAjcTP0CVYC+heOiZu7X0L5epQ+OiNnNnNqx0rXtxienSpIkqYJqbQfZsH1jUD+zfl9K6f2U0l+AzwOvASMiYq9W3i8Kx06aeDvpsCVJktRhqjXj/m7h+HJK6Y9NT6SUPoiIe4ATgCHAo/xrRr0v5fUpHJubkW96/8Hl2gsz8YMqXd8hXJ0qSZKkjGrNuD9XOP6zmfMNwb5Hpv/22Y4RUUv9QteVwMttNcAOZVCXJElSBdUK7g9SH7S3i4iuZc7vUjjOKxzvLxw/W6bvcKAn8Ejn3FEG3FVGkiRJlVQluKeUFlD/9NO+wPeanouI/alfnLqIf23/eAv1T1cdGxF7NOnbHTiv8PbKdh52u4nKXSRJkvQxV7UnpwKnA0OBsyJiOPAE9bvKfJ76J6SemFL6J0BK6b2IOJH6AD8zIqYCC4FDqd8q8hbqfxHonFJd5r0z7pIkSSpWrVIZUkpvUx/cfwJsBpxC/YOWfgt8JqV0c6b/NGAE9WU2RwDfAlZQ/wvA2JQ6c9pNLbyTJEmSqjvjTkppIfXB+/RW9n8YOLhdB1UFkfmdI/tekiRJqtqMu5pyxl2SJEktM7jngrvKSJIkqWUG9xywNEaSJEmVGNxzwRp3SZIktczgngNhaYwkSZIqMLjnQcouTjXIS5IkqZjBXZIkSeoEDO45kC2VsXRGkiRJWQb3PEh1xW/N7ZIkScowuOeAM+ySJEmqxOCeR065S5IkKcPgngclQd3gLkmSpGIG9xywVEaSJEmVGNxzIKjLtBjkJUmSVMzgngsGdUmSJLXM4J4HJSXuBnlJkiQVM7jngDXukiRJqsTgngupxbeSJEmSwT0HIrk4VZIkSS0zuOdAVHsAkiRJyj2Dey5kS2WyM/CSJEn6uDO454CLUyVJklSJwT0P3P5RkiRJFRjcJUmSpE7A4J4DgbvKSJIkqWUG9xwIS2UkSZJUgcE9F7K7yhjkJUmSVMzgngPu4y5JkqRKDO65kJ1hd8ZdkiRJxQzuOVC6OFWSJEkqZnDPgSiZcHfGXZIkScUM7rlgUJckSVLLDO45ENa4S5IkqQKDey4Y1CVJktQyg3sOlDyAyRp3SZIkZRjcc6C0VEaSJEkqZnDPBWfcJUmS1LLcBPeIOCYiUuH11Wb6DIuIOyNiYUQsjYg/RcSpEdGlo8fblpxxlyRJUiW5CO4RsRlwGfB+C30OAx4EhgO/Bq4AugI/AaZ2wDDbT0luN8hLkiSpWNWDe0QEMBn4B3BVM336AFcDq4CRKaUTUkrfAXYDHgXGRMTYDhpym3PGXZIkSZVUPbgDpwCjgOOBJc30GQNsCExNKT3V0JhSWgZMLLw9uT0H2Z6CukyLQV6SJEnFqhrcI2In4ALgpymlB1voOqpwvLvMuQeBpcCwiOjWxkPsEJFtMLdLkiQpo7ZaHxwRtcCvgL8D/1Wh+w6F4/PZEymllRHxCrAzsDUwt8Lnzm7m1I4VxtBufHKqJEmSKqlacAe+B+wO7J1S+qBC376F46Jmzje0r9cWA+t4BnVJkiS1rCrBPSKGUD/LfklK6dG2uGXhWDEBp5QGNzOm2cCgNhjLanNxqiRJkirp8Br3JiUyzwOTWnlZw4x632bO98n061TCBy5JkiSpgmosTl0X2B7YCVjW5KFLCTi70OfqQtulhffPFY7bZ29W+EVgK2Al8HL7Dr19lMy4p+wuM5IkSfq4q0apzHLg2mbODaK+7v0h6sN6QxnN/cDRwGeB/5e5ZjjQE3gwpbS8zUfbIZxxlyRJUss6PLgXFqJ+tdy5iDiH+uB+XUrpmianbgH+GxgbEZc17OUeEd2B8wp9rmy3Qbez0u0gDfKSJEkqVs1dZVotpfReRJxIfYCfGRFTgYXAodRvFXkLcGMVh7iWDOqSJElqWR6enNoqKaVpwAjqH7h0BPAtYAVwOjA2pc47TV2TCe4lM/CSJEn62MvVjHtK6RzgnBbOPwwc3FHj6TiphXeSJElSJ5px/yjzyamSJEmqxOCeA2FulyRJUgUG91wwuUuSJKllBvccqMEHLkmSJKllBvdccsZdkiRJxQzuOVC6OFWSJEkqZnDPgZLg3nm3pJckSVI7MbjngA9ckiRJUiUG91zIPjnVGXdJkiQVM7jnQHZXGWO7JEmSsgzuuWR0lyRJUjGDew6ULk6tzjgkSZKUXwb3HMgGd2vcJUmSlGVwzwF3lZEkSVIlBvcciFSXaXHGXZIkScUM7jngjLskSZIqMbjngE9OlSRJUiUG9xxwMaokSZIqMbhLkiRJnYDBPQeyT051caokSZKyDO45ULI41Rp3SZIkZRjcc8GgLkmSpJYZ3HPAxamSJEmqxOCeA+7jLkmSpEoM7jngPu6SJEmqxOCeAzWWykiSJKkCg3suFAd3a94lSZKUZXDPgWxQN7ZLkiQpy+CeA9nFqc64S5IkKcvgngPOuEuSJKkSg3uVpZRKFqeGu8pIkiQpw+BeZSlBTRjUJUmS1DKDe5WVj+wGeUmSJBUzuFdZXV1dtYcgSZKkTsDgXmWpXD27Ne6SJEnKMLhXWUqrqj0ESZIkdQIG9ypLdc6uS5IkqbKqBPeI2CAivhoRv46IFyPig4hYFBEPRcQJEVF2XBExLCLujIiFEbE0Iv4UEadGRJeO/g5tp1xwN8xLkiSpWG2VPvdI4ErgTWAG8HdgY+ALwDXAQRFxZGpSAB4RhwG3AsuAG4GFwCHAT4BPF+7Z6ZSdcbfGXZIkSRnVCu7PA4cCv00pNW6rEhH/BTwBHEF9iL+10N4HuBpYBYxMKT1VaJ8E3A+MiYixKaWpHfot2kDCXWUkSZJUWVVKZVJK96eUftM0tBfa5wNXFd6ObHJqDLAhMLUhtBf6LwMmFt6e3H4jbj/WuEuSJKk18rg4dUXhuLJJ26jC8e4y/R8ElgLDIqJbew6sPWR+d5EkSZLKqlapTFkRUQv8R+Ft05C+Q+H4fPaalNLKiHgF2BnYGphb4TNmN3Nqx9UbbdsoF9zDxamSJEnKyNuM+wXALsCdKaV7mrT3LRwXNXNdQ/t67TWw9lL2AUySJElSRm5m3CPiFGA88CxwzOpeXjhWTMEppcHNfP5sYNBqfu5aK5vbDfOSJEnKyMWMe0R8E/gp8Fdgn5TSwkyXhhn1vpTXJ9Ov8yif3Dt8GJIkScq3qgf3iDgVuBx4hvrQPr9Mt+cKx+3LXF8LbEX9YtaX22uc7SatqvYIJEmS1AlUNbhHxJnUP0DpD9SH9reb6Xp/4fjZMueGAz2BR1JKy9t+lO2r/HaQzrhLkiSpWNWCe+HhSRcAs4F9U0oLWuh+C7AAGBsRezS5R3fgvMLbK9trrO0pGdIlSZLUClVZnBoRxwI/oP5JqLOAUyIi221eSmkKQErpvYg4kfoAPzMipgILqX/66g6F9hs7ZvRtK9WV2cfdxamSJEnKqNauMlsVjl2AU5vp8wAwpeFNSmlaRIwAzgKOALoDLwKnAz9LnXRfRWfcJUmS1BpVCe4ppXOAc9bguoeBg9t6PNVUbsbdBzBJkiQpq+q7ysiQLkmSpMoM7tVWdlcZSZIkqZjBvcrK1rh3znJ9SZIktSODe5WVX1NrcJckSVIxg3uVpVRmO0hJkiQpw+BebeX2cZckSZIyDO5V5j7ukiRJag2De5WVrXF3caokSZIyDO5VltwOUpIkSa1gcK+60uDuk1MlSZKUZXCvtjKLU43tkiRJyjK4V1miNLg74y5JkqQsg3uVWeMuSZKk1jC4V527ykiSJKkyg3uVld0OUpIkScowuFdZSuWenGqYlyRJUjGDe7U54y5JkqRWMLhXW5kZ96jCMCRJkpRvBvcqK1fj7iS8JEmSsgzuVZZ8cqokSZJaweBebeXWpkqSJEkZBvcqS6wq2ypJkiQ1ZXCvNp+cKkmSpFYwuFdZqisz4+7qVEmSJGUY3KssVi2r9hAkSZLUCRjcqyxWfFDaZo27JEmSMgzuVVazsjS4S5IkSVkG9yqr+3BpmVZn3CVJklTM4F5lK5YtqfYQJEmS1AkY3Kts5fIywd1dZSRJkpRhcK+yuuXlSmUkSZKkYgb3KltVtsZdkiRJKmZwr7K6D91VRpIkSZUZ3KttRemMu/u4S5IkKcvgXm1lHsAkSZIkZRncq6zsA5jcVUaSJEkZnSq4R8QnIuJ/I+KNiFgeEfMi4tKIWL/aY1tTm3zwXLWHIEmSpE6gttoDaK2I2AZ4BNgIuB14FhgCfBv4bER8OqX0jyoOcfUtW0T/FW+WNG/xwV9g+ulVGFAzIqo9goK8jIMmfyfRfFvR31u5tra6VzPXNXuPcudauG9Ek2sKx6jJtNX867psW9QUv8qeK9Ov6aumC0SXJn/OnqupP1/TpE9Nbf2rob2m9l/3aTx2qrkLSdLHXKcJ7sD/UB/aT0kpXdbQGBE/Bk4Dfgh8vUpjWyMrpk9gnTLtG334Kjx1bYePR/pYKgr9DcG/6S8ZTX9h+f/bu/cgycqzjuPfX/fMwLphISCIiZGbC2zkDxECBEwCRDcmRiElSayUSFESRdGIwQrlNVBeQqIo5FKVwly2lFipgBWjFgoKJIRguASjJiHhsqwSA8EstyUsy0734x/ve3pO9/RlZneme96Z36fq1Olz3ve8551+erqft8/pc5rQnM7TDDSm8/o+g5v6wGfeQGQqtVENLprTqa1GM58qF2n7+gCkMQXNPBCpD4465T11Ry5X65qpn1P7pml6XfrbqmlqZu5v9UDHzGyiikjcJR0JbAa2AR/qKX438EvAuZIuiYg+tyJdgSL46CMv4dzYh/XaNenemK1d0U5Te/eke7LihZpEYzoNIBrTRCPN03L1OC2rkQYmygMdNWfmBjz1AUE11Y+koDRIqAYM1WBJzbnODDuiVC132htSvzkz1+68ARjdy9GCdjsP7Jo9++hzJKnTxjBDKgw92jmi4T3dduQRVvd3Yfvdg+2G/b5tIUdml3XdMCvoaPie2ncDTO0z6V4sSBGJO3Bmnt8UEe16QUTskPQFUmJ/CnDzuDu3RySOfcNFvG7L4ZzVuIMnDzqee7fPcCJfmXTPOlbSZSlXVl+qedTWxcAyesq66w8rG93WoD50Hqtev/8+RQzcZ7VdqjNXt9G1Tfe6er36+oaq5TaCPE/ljVrdan2zNm9q7nGjM9WX5+pP0aKptH6KNk1aqQ3aTNGiUT1W11uJLYCihVotaE26J2ZmS+fh117DEa9666S7sSClJO7H5Pn9A8ofICXuRzMicZf0pQFFx+5Z1/bc6cccwhknv4KNh23mrB95CU8+t5t7tj1BOyAiCKAdMbccabk+D1J5Z7lWv1pPVztV3aqdvMzcPiInZOkxXevorJtfJxXH3OOqTk8b3dvU1g2sk9f37rtWr9pf1WQssF2I2rrB7c5tO7zdefse0i6d571fuyP23dMuPdsMapdRdQY8N/O2Wey+AdqdVrvarV67kxGdpL7RmUdncDA3CKEz6JhWi2lm89RCXfXm6lZ6BzBNpQHEFKmducctGnkwEaizXzhA/QAADElJREFUTVU+RYsmLaZo5xp0BjXV+manXjtPLabUvb57nspFMMMs+/IC+7KLabWYYZYZdjNFixl2MyNn62a2Oj21s5wjrqUk7vvn+dMDyqv1B4yhL0vqD88+rvP4wPUzbP7hQyfYG7PJqA9M6wPRtDx/MNrurd9eZP08eK22XVT9Wvut9vDydrvf3zOkfu/fX9t+NoIXRtXvbb89v36rq+9zdVpdz0cqjyCvD9qtNooWjZil0d5NI1ooZmnGbtRO65sxSyNmUbRodpZbTOUBygy7mWaWGc0y0xn8pMfVEZN0ZCYdLakPbqaZpVE7ygNzR/HnBkpVefeAqTGvPM2rgdHc0aLuI0bQPSBr5SFd/aiPgEYe/PQeQRp1pHBY+fCy4fZ029FHNpen3eXq754eqZ1Ef0XkV9T8dnqP0NbLq6Oqg/Yz/Khun/a6+jj6+VsFJ8kA0G70+8XhylRK4j5K9doZ+SqLiBP6NpC+if/RpeyUmS2MJCRorJqPAaurBmadQUC7NsjqGjjUltu1o3fUjjQyd9Sn39GdrnnP+u4+9a/TlbbUjyx1tTO/3ephKyA6F25Kr+t5/Y0Y+mE17AjUwC2HbrPE+xq53d6JAY0PbHfgU5KOXXUs9O1lxB8wPHq5zqg2FvAkjRxGjWhkIXEY3Y9I789D2o2oD6LnKK+sJ2jVFxZ7/yrZM73/59XyCYeVc1XxUhL36hv1/QeUb+ipZ2ZmK4QHZmZmS6OUa3tVdyk6ekD5xjwfdA68mZmZmVnRSkncb83zzZK6+ixpP+A0YCfwxXF3zMzMzMxsHIpI3CPiIeAm4HDgop7iy4H1wF8Vcw13MzMzM7NFKuUcd4BfBe4A3i/ptcB9wMnAGaRTZH53gn0zMzMzM1tWRXzjDp1v3U8EtpAS9kuAo4D3A6+MiO2T652ZmZmZ2fIq6Rt3IuIR4PxJ98PMzMzMbNyK+cbdzMzMzGwtc+JuZmZmZlYAJ+5mZmZmZgVw4m5mZmZmVgAn7mZmZmZmBXDibmZmZmZWACfuZmZmZmYFcOJuZmZmZlYAJ+5mZmZmZgVw4m5mZmZmVgBFxKT7sCJI2r5u3boDN23aNOmumJmZmdkqdd9997Fz584nIuKgxW7rxD2T9DCwAdg25l0fm+dfH/N+bbwc57XBcV4bHOe1wXFe/SYV48OBZyLiiMVu6MR9wiR9CSAiTph0X2z5OM5rg+O8NjjOa4PjvPqVGGOf425mZmZmVgAn7mZmZmZmBXDibmZmZmZWACfuZmZmZmYFcOJuZmZmZlYAX1XGzMzMzKwA/sbdzMzMzKwATtzNzMzMzArgxN3MzMzMrABO3M3MzMzMCuDE3czMzMysAE7czczMzMwK4MTdzMzMzKwATtwnRNIPSPqYpG9J2iVpm6SrJL140n2zbpIOknSBpE9LelDSTklPS7pd0i9K6vt/JOlUSTdIekLSc5L+U9LFkppD9nWepLskPZv38VlJb1y+v85GkXSupMjTBQPqONYFkvQqSX8r6dH8PvyopJskvaFPXce4QJJ+Ksf0m/m9e6uk6yS9ckB9x3kFknSOpA9I+rykZ/L78bUjtln2WEpaJ+lySd+Q9LykxyV9StKmvfl7h4oIT2OegKOAbwMB/B1wBXBLXv46cNCk++ipK14X5th8C/gE8B7gY8BTef315JuZ1bY5C5gFngU+Cvxpjm0A1w3Yz5/l8keAvwA+BGzP635t0s/DWpyAl+U478hxuKBPHce6wAn4vfx8/x/wceBPgGuAu4H3OcblT8B78/P9HeAj+bP2euAFoA38vONcxgR8OT+nO4D78uNrh9Rf9lgC+wC35/K78+vtb4DdwHeBk5fluZh0MNbiBNyYA/3rPev/PK//8KT76KkrLmcCPw00etYfCvxPjtnP1tZvAB4HdgEn1tbvC9yR6/9cT1un5vUPAi+urT88v3E8Dxw+6ediLU2AgH8FHspv+vMSd8e6zAl4c47BvwD79SmfdozLnvL7cwt4DDikp+yMHJ+tjnMZU47Zxvy+fDpDEvdxxRL47bzNddTyA9KgIYCv0pM3LMXkU2XGTNKRwGZgG2k0V/du0ijtXEnrx9w1GyAibomIf4iIds/6x4AP58XTa0XnAAcDn4yIe2r1nyd9ywfwKz27uTDP/zginqxts430OtkHOH/v/hJbpHeQBm3nk/4v+3GsC5NPbXsv8BzwtojY0VsnInbXFh3jMh1GOh34zoh4vF4QEbeSvrk9uLbacV7BIuLWiHggcmY8wrLHUpJq27yrnh9ExGeAzwMvB16zgP4uihP38Tszz2/qkwjuAL4AfA9wyrg7Znuk+oCfra2rYvzPferfRkoYTpW0zwK3+aeeOrbM8vmJVwBXR8RtQ6o61uU5FTgCuAF4Mp8Dfamk3xhw3rNjXKYHSKfEnCTpe+sFkl4N7Ec6olZxnFePccTyKOAHgfsj4uEFbrMknLiP3zF5fv+A8gfy/Ogx9MX2gqQp4BfyYv2ffWCMI2IWeBiYAo7M7awHXgo8GxGP9tmVXxNjlOP616TToH5nRHXHujyvyPNvA/cC/0gapF0F3CHpc5Lq38Q6xgWKiCeAS4HvA74m6RpJ75H0KeAm0mlSv1zbxHFePcYRy4nlclNL3aCNtH+ePz2gvFp/wBj6YnvnCuA44IaIuLG2frEx9mtiZfkD4HjgxyJi54i6jnV5DsnzC0kf4D8O3Ek6teJK4HWkc1ZPz/Uc40JFxFWStpEuJvD2WtGDwJaeU2gc59VjHLGcWPz9jfvKozxfyHlcNiGS3gFcQvqV+rmL3TzPFxtjvyaWmaSTSN+yXxkR/7YUTea5Y71yVJeCE3BORNwcEc9GxFeBNwHfBF4z6HKBfTjGK5Skd5GuIrOFdGrDeuAEYCvwCUnvW0xzee44l28csVy2XM6J+/hVo7D9B5Rv6KlnK4yki4Crga8BZ+RDsnWLjfGo+qNG9rYEaqfI3A/8/gI3c6zLU/3wbGtE/Ee9IB9hqY6enZTnjnGBJJ1O+hHy30fEOyNia0Q8FxH3kgZo/wtcki8YAY7zajKOWE4sl3PiPn7fyPNB5z1tzPNB503ZBEm6GPgg8BVS0v5Yn2oDY5yTwyNIP2bdChAR3yV9iLxI0vf3ac+vifF4ESlmm4DnazddCtIVnwD+Mq+7Ki871uWpYvbUgPIqsV/XU98xLkt105xbewsi4jngLlIOdHxe7TivHuOI5cRyOSfu41e9iWxWzx03Je0HnAbsBL447o7ZcJIuJd2U4cukpP3xAVVvyfOf7FP2atJVg+6IiF0L3Ob1PXVseewi3aij3/Tvuc7tebk6jcaxLs9tpA/tjZJm+pQfl+fb8twxLlN1xZCDB5RX61/Ic8d59RhHLB8iXcDgaElHLHCbpbHUF4b3tKAbCfgGTIVNpFMnArgHOHBE3Q2kuzH6Rh6rZAIuY/ANmBzrwibg2hyDP+pZ/xOkO2o+BRzgGJc7AW/JMXgMeGlP2etznHeS71TuOJczsbAbMC17LJnQDZiUd2JjJOko0ovnEOAzpNv3nky6M9j9wKkRsX1yPbQ6SeeRftzUAj5A/3PWtkXElto2Z5N+FPU88EngCeBnSJeQuh54S/T880m6Engn6cdx1wMzwFuBg0iDvA8u5d9lCyfpMtLpMm+PiI/0lDnWhZF0COmeGT9EulHKXaSryryJ9IH7toi4rlbfMS5MPqJ9I+mqQTuAT5OS+E2k02gEXBwRV9e2cZxXqBybs/PioaSrP20l/f8CfCcifqun/rLGMl8H/hZS0n8PcDPp2u5vJh3JOTMi7lyCP7/bpEdOa3UCXgZ8HHg0B/i/ST94HPptrqeJxOoy0of5sOmzfbY7jXyTF9I3O/8F/CbQHLKv84C7SXfq3AF8DnjjpJ+DtT4x4Bt3x7rcCTiQdJTz4fwevJ30RcopjvHqmIBp4GLSqafPkE6Repx07f7NjnM50wI+h7dNIpak38JcTrpu+y7SN/3XAS9frufC37ibmZmZmRXAP041MzMzMyuAE3czMzMzswI4cTczMzMzK4ATdzMzMzOzAjhxNzMzMzMrgBN3MzMzM7MCOHE3MzMzMyuAE3czMzMzswI4cTczMzMzK4ATdzMzMzOzAjhxNzMzMzMrgBN3MzMzM7MCOHE3MzMzMyuAE3czMzMzswI4cTczMzMzK4ATdzMzMzOzAjhxNzMzMzMrwP8DBLbBOB/i0WYAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "width": 375,
       "height": 250
      },
      "needs_background": "light"
     }
    }
   ],
   "metadata": {
    "collapsed": true,
    "editable": true,
    "jupyter": {
     "outputs_hidden": true
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "## Check out your predictions\n",
    "\n",
    "Here, use the test data to view how well your network is modeling the data. If something is completely wrong here, make sure each step in your network is implemented correctly."
   ],
   "metadata": {
    "editable": true
   }
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "source": [
    "figure, axis = plt.subplots(2,figsize=(8,8))\r\n",
    "\r\n",
    "mean, std = scaled_features['cnt']\r\n",
    "predictions = network.run(test_features).T*std + mean\r\n",
    "axis[0].plot(predictions[0], label='Prediction', color='red')\r\n",
    "axis[0].set_title(\"Prediction\")\r\n",
    "  \r\n",
    "# For Cosine Function\r\n",
    "# axis[1].plot((test_targets['cnt']*std + mean).values, label='Data', color='cyan')\r\n",
    "axis[1].plot((test_targets['cnt']).values, label='Data', color='cyan')\r\n",
    "axis[1].set_title(\"cnt\")\r\n",
    "\r\n",
    "dates = pd.to_datetime(rides.iloc[test_data.index]['dteday'])\r\n",
    "dates = dates.apply(lambda d: d.strftime('%b %d'))\r\n",
    "\r\n",
    "axis[0].set_xticks(np.arange(len(dates))[12::24])\r\n",
    "_ = axis[0].set_xticklabels(dates[12::24], rotation=45)\r\n",
    "\r\n",
    "axis[1].set_xticks(np.arange(len(dates))[12::24])\r\n",
    "_ = axis[1].set_xticklabels(dates[12::24], rotation=45)"
   ],
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 576x576 with 2 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "width": 486,
       "height": 503
      },
      "needs_background": "light"
     }
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "source": [
    "fig, ax = plt.subplots(figsize=(8,4))\r\n",
    "\r\n",
    "mean, std = scaled_features['cnt']\r\n",
    "predictions = network.run(test_features).T*std + mean\r\n",
    "ax.plot(predictions[0], label='Prediction')\r\n",
    "# ax.plot((test_targets['cnt']*std + mean).values, label='Data')\r\n",
    "ax.plot((test_targets['cnt']).values, label='Data')\r\n",
    "ax.set_xlim(right=len(predictions))\r\n",
    "ax.legend()\r\n",
    "\r\n",
    "dates = pd.to_datetime(rides.loc[test_data.index]['dteday'])\r\n",
    "dates = dates.apply(lambda d: d.strftime('%b %d'))\r\n",
    "ax.set_xticks(np.arange(len(dates))[12::24])\r\n",
    "_ = ax.set_xticklabels(dates[12::24], rotation=45)"
   ],
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 576x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "width": 502,
       "height": 272
      },
      "needs_background": "light"
     }
    }
   ],
   "metadata": {
    "collapsed": true,
    "editable": true,
    "jupyter": {
     "outputs_hidden": true
    }
   }
  },
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   "cell_type": "markdown",
   "source": [
    "## OPTIONAL: Thinking about your results(this question will not be evaluated in the rubric).\n",
    " \n",
    "Answer these questions about your results. How well does the model predict the data? Where does it fail? Why does it fail where it does?\n",
    "\n",
    "> **Note:** You can edit the text in this cell by double clicking on it. When you want to render the text, press control + enter\n",
    "\n",
    "#### Your answer below"
   ],
   "metadata": {
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  },
  {
   "cell_type": "markdown",
   "source": [
    "## Submitting:\n",
    "Open up the 'jwt' file in the first-neural-network directory (which also contains this notebook) for submission instructions"
   ],
   "metadata": {
    "editable": true
   }
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "source": [],
   "outputs": [],
   "metadata": {
    "editable": true
   }
  }
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