Lesson8-Bike Sharing - Solutions.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "\n",
    "from keras.models import Sequential\n",
    "from keras.layers import Dense, Activation, Embedding, Flatten, LeakyReLU, Merge, BatchNormalization\n",
    "\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Description of the data:\n",
    "\n",
    "- instant: record index\n",
    "- dteday : date\n",
    "- season : season (1:springer, 2:summer, 3:fall, 4:winter)\n",
    "- yr : year (0: 2011, 1:2012)\n",
    "- mnth : month ( 1 to 12)\n",
    "- hr : hour (0 to 23)\n",
    "- holiday : weather day is holiday or not (extracted from [Web Link])\n",
    "- weekday : day of the week\n",
    "- workingday : if day is neither weekend nor holiday is 1, otherwise is 0.\n",
    "+ weathersit : \n",
    "  - 1: Clear, Few clouds, Partly cloudy, Partly cloudy\n",
    "  - 2: Mist + Cloudy, Mist + Broken clouds, Mist + Few clouds, Mist\n",
    "  - 3: Light Snow, Light Rain + Thunderstorm + Scattered clouds, Light Rain + Scattered clouds\n",
    "  - 4: Heavy Rain + Ice Pallets + Thunderstorm + Mist, Snow + Fog\n",
    "- temp : Normalized temperature in Celsius. The values are derived via (t-t_min)/(t_max-t_min), t_min=-8, t_max=+39 (only in hourly scale)\n",
    "- atemp: Normalized feeling temperature in Celsius. The values are derived via (t-t_min)/(t_max-t_min), t_min=-16, t_max=+50 (only in hourly scale)\n",
    "- hum: Normalized humidity. The values are divided to 100 (max)\n",
    "- windspeed: Normalized wind speed. The values are divided to 67 (max)\n",
    "- casual: count of casual users\n",
    "- registered: count of registered users\n",
    "- cnt: count of total rental bikes including both casual and registered"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/sachin/anaconda/lib/python3.5/site-packages/pandas/core/ops.py:533: PerformanceWarning: Adding/subtracting array of DateOffsets to Series not vectorized\n",
      "  \"Series not vectorized\", PerformanceWarning)\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>season</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>hum</th>\n",
       "      <th>windspeed</th>\n",
       "      <th>cnt</th>\n",
       "      <th>time</th>\n",
       "      <th>t</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</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>0.81</td>\n",
       "      <td>0.0</td>\n",
       "      <td>16</td>\n",
       "      <td>2011-01-01 00:00:00</td>\n",
       "      <td>1.293840e+18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</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>0.80</td>\n",
       "      <td>0.0</td>\n",
       "      <td>40</td>\n",
       "      <td>2011-01-01 01:00:00</td>\n",
       "      <td>1.293844e+18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</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>0.80</td>\n",
       "      <td>0.0</td>\n",
       "      <td>32</td>\n",
       "      <td>2011-01-01 02:00:00</td>\n",
       "      <td>1.293847e+18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</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>0.75</td>\n",
       "      <td>0.0</td>\n",
       "      <td>13</td>\n",
       "      <td>2011-01-01 03:00:00</td>\n",
       "      <td>1.293851e+18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</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>0.75</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>2011-01-01 04:00:00</td>\n",
       "      <td>1.293854e+18</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   season  mnth  hr  holiday  weekday  workingday  weathersit  temp   hum  \\\n",
       "0       1     1   0        0        6           0           1  0.24  0.81   \n",
       "1       1     1   1        0        6           0           1  0.22  0.80   \n",
       "2       1     1   2        0        6           0           1  0.22  0.80   \n",
       "3       1     1   3        0        6           0           1  0.24  0.75   \n",
       "4       1     1   4        0        6           0           1  0.24  0.75   \n",
       "\n",
       "   windspeed  cnt                time             t  \n",
       "0        0.0   16 2011-01-01 00:00:00  1.293840e+18  \n",
       "1        0.0   40 2011-01-01 01:00:00  1.293844e+18  \n",
       "2        0.0   32 2011-01-01 02:00:00  1.293847e+18  \n",
       "3        0.0   13 2011-01-01 03:00:00  1.293851e+18  \n",
       "4        0.0    1 2011-01-01 04:00:00  1.293854e+18  "
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rides = pd.read_csv('hour.csv')\n",
    "rides['time'] = pd.to_datetime(rides['dteday'])+pd.Series([pd.DateOffset(hours = a ) for a in rides.hr.tolist()])\n",
    "rides.drop(['instant','yr','dteday','casual','registered', 'atemp'],1,inplace=True)\n",
    "rides['t'] = rides.time.values.astype(float)\n",
    "rides.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "7"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rides.weekday.nunique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
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PEkKOEUIeIYTc0MsnnwVmFmsAgH3To1istVb13E5eP5fR7JmtL81xNKdr0Ei8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\nnAgDY6yRzD7j4/7EUAFLtZa0rCLD7Ic4Zv+0OxL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      "text/plain": [
       "<matplotlib.figure.Figure at 0x120161320>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(rides.time[:240],rides.cnt[:240])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/sachin/anaconda/lib/python3.5/site-packages/sklearn/utils/validation.py:429: DataConversionWarning: Data with input dtype int64 was converted to float64 by StandardScaler.\n",
      "  warnings.warn(msg, _DataConversionWarning)\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>season</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>hum</th>\n",
       "      <th>windspeed</th>\n",
       "      <th>cnt</th>\n",
       "      <th>time</th>\n",
       "      <th>t</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</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>-1.334648</td>\n",
       "      <td>0.947372</td>\n",
       "      <td>-1.553889</td>\n",
       "      <td>-0.956339</td>\n",
       "      <td>2011-01-01 00:00:00</td>\n",
       "      <td>-1.745435</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</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>-1.438516</td>\n",
       "      <td>0.895539</td>\n",
       "      <td>-1.553889</td>\n",
       "      <td>-0.824022</td>\n",
       "      <td>2011-01-01 01:00:00</td>\n",
       "      <td>-1.745236</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</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>-1.438516</td>\n",
       "      <td>0.895539</td>\n",
       "      <td>-1.553889</td>\n",
       "      <td>-0.868128</td>\n",
       "      <td>2011-01-01 02:00:00</td>\n",
       "      <td>-1.745038</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</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>-1.334648</td>\n",
       "      <td>0.636370</td>\n",
       "      <td>-1.553889</td>\n",
       "      <td>-0.972879</td>\n",
       "      <td>2011-01-01 03:00:00</td>\n",
       "      <td>-1.744840</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</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>-1.334648</td>\n",
       "      <td>0.636370</td>\n",
       "      <td>-1.553889</td>\n",
       "      <td>-1.039037</td>\n",
       "      <td>2011-01-01 04:00:00</td>\n",
       "      <td>-1.744641</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   season  mnth  hr  holiday  weekday  workingday  weathersit      temp  \\\n",
       "0       1     1   0        0        6           0           1 -1.334648   \n",
       "1       1     1   1        0        6           0           1 -1.438516   \n",
       "2       1     1   2        0        6           0           1 -1.438516   \n",
       "3       1     1   3        0        6           0           1 -1.334648   \n",
       "4       1     1   4        0        6           0           1 -1.334648   \n",
       "\n",
       "        hum  windspeed       cnt                time         t  \n",
       "0  0.947372  -1.553889 -0.956339 2011-01-01 00:00:00 -1.745435  \n",
       "1  0.895539  -1.553889 -0.824022 2011-01-01 01:00:00 -1.745236  \n",
       "2  0.895539  -1.553889 -0.868128 2011-01-01 02:00:00 -1.745038  \n",
       "3  0.636370  -1.553889 -0.972879 2011-01-01 03:00:00 -1.744840  \n",
       "4  0.636370  -1.553889 -1.039037 2011-01-01 04:00:00 -1.744641  "
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "quant_features = ['temp', 'hum', 'windspeed','t']\n",
    "x_scaler = StandardScaler()\n",
    "rides[quant_features] = x_scaler.fit_transform(rides[quant_features].values)\n",
    "\n",
    "y_scaler = StandardScaler()\n",
    "rides['cnt'] = y_scaler.fit_transform(rides['cnt'].values[:,None])\n",
    "\n",
    "rides.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(17379,)"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rides['cnt'].values.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(17379, 1)"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rides['cnt'].values[:,None].shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1, 17379)"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rides['cnt'].values[None,:].shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/sachin/anaconda/lib/python3.5/site-packages/ipykernel/__main__.py:17: UserWarning: The `Merge` layer is deprecated and will be removed after 08/2017. Use instead layers from `keras.layers.merge`, e.g. `add`, `concatenate`, etc.\n"
     ]
    }
   ],
   "source": [
    "cat_features = ['season', 'hr', 'mnth', 'holiday', 'weekday', 'workingday', 'weathersit']\n",
    "\n",
    "# Preprocess data so that it starts from 0\n",
    "for feature in cat_features:\n",
    "    if rides[feature].min() > 0:\n",
    "        rides[feature] -= 1\n",
    "\n",
    "# Embed the models\n",
    "cat_models = []\n",
    "for feature in cat_features:\n",
    "    model_cat = Sequential()\n",
    "    model_cat.add(Embedding(rides[feature].nunique(),10,input_length=1))\n",
    "    model_cat.add(Flatten())\n",
    "    cat_models.append(model_cat)\n",
    "    \n",
    "cat_model = Sequential()\n",
    "cat_model.add(Merge(cat_models))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "merge_7 (Merge)              (None, 10)                0         \n",
      "=================================================================\n",
      "Total params: 550.0\n",
      "Trainable params: 550.0\n",
      "Non-trainable params: 0.0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "cat_model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'weathersit'"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "feature"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rides[feature].nunique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "embedding_14 (Embedding)     (None, 1, 10)             40        \n",
      "_________________________________________________________________\n",
      "flatten_14 (Flatten)         (None, 10)                0         \n",
      "=================================================================\n",
      "Total params: 40.0\n",
      "Trainable params: 40.0\n",
      "Non-trainable params: 0.0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "model_cat.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>temp</th>\n",
       "      <th>hum</th>\n",
       "      <th>windspeed</th>\n",
       "      <th>t</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
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       "      <td>-1.553889</td>\n",
       "      <td>-1.745435</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-1.438516</td>\n",
       "      <td>0.895539</td>\n",
       "      <td>-1.553889</td>\n",
       "      <td>-1.745236</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-1.438516</td>\n",
       "      <td>0.895539</td>\n",
       "      <td>-1.553889</td>\n",
       "      <td>-1.745038</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-1.334648</td>\n",
       "      <td>0.636370</td>\n",
       "      <td>-1.553889</td>\n",
       "      <td>-1.744840</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-1.334648</td>\n",
       "      <td>0.636370</td>\n",
       "      <td>-1.553889</td>\n",
       "      <td>-1.744641</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       temp       hum  windspeed         t\n",
       "0 -1.334648  0.947372  -1.553889 -1.745435\n",
       "1 -1.438516  0.895539  -1.553889 -1.745236\n",
       "2 -1.438516  0.895539  -1.553889 -1.745038\n",
       "3 -1.334648  0.636370  -1.553889 -1.744840\n",
       "4 -1.334648  0.636370  -1.553889 -1.744641"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rides[quant_features].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>season</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>hum</th>\n",
       "      <th>windspeed</th>\n",
       "      <th>cnt</th>\n",
       "      <th>time</th>\n",
       "      <th>t</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
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       "      <td>0.947372</td>\n",
       "      <td>-1.553889</td>\n",
       "      <td>-0.956339</td>\n",
       "      <td>2011-01-01 00:00:00</td>\n",
       "      <td>-1.745435</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>-1.438516</td>\n",
       "      <td>0.895539</td>\n",
       "      <td>-1.553889</td>\n",
       "      <td>-0.824022</td>\n",
       "      <td>2011-01-01 01:00:00</td>\n",
       "      <td>-1.745236</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>-1.438516</td>\n",
       "      <td>0.895539</td>\n",
       "      <td>-1.553889</td>\n",
       "      <td>-0.868128</td>\n",
       "      <td>2011-01-01 02:00:00</td>\n",
       "      <td>-1.745038</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>-1.334648</td>\n",
       "      <td>0.636370</td>\n",
       "      <td>-1.553889</td>\n",
       "      <td>-0.972879</td>\n",
       "      <td>2011-01-01 03:00:00</td>\n",
       "      <td>-1.744840</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>-1.334648</td>\n",
       "      <td>0.636370</td>\n",
       "      <td>-1.553889</td>\n",
       "      <td>-1.039037</td>\n",
       "      <td>2011-01-01 04:00:00</td>\n",
       "      <td>-1.744641</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   season  mnth  hr  holiday  weekday  workingday  weathersit      temp  \\\n",
       "0       0     0   0        0        6           0           0 -1.334648   \n",
       "1       0     0   1        0        6           0           0 -1.438516   \n",
       "2       0     0   2        0        6           0           0 -1.438516   \n",
       "3       0     0   3        0        6           0           0 -1.334648   \n",
       "4       0     0   4        0        6           0           0 -1.334648   \n",
       "\n",
       "        hum  windspeed       cnt                time         t  \n",
       "0  0.947372  -1.553889 -0.956339 2011-01-01 00:00:00 -1.745435  \n",
       "1  0.895539  -1.553889 -0.824022 2011-01-01 01:00:00 -1.745236  \n",
       "2  0.895539  -1.553889 -0.868128 2011-01-01 02:00:00 -1.745038  \n",
       "3  0.636370  -1.553889 -0.972879 2011-01-01 03:00:00 -1.744840  \n",
       "4  0.636370  -1.553889 -1.039037 2011-01-01 04:00:00 -1.744641  "
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rides.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "quant_model = Sequential()\n",
    "quant_model.add(Dense(len(quant_features),input_dim=len(quant_features)))\n",
    "quant_model.add(LeakyReLU())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# model = Sequential()\n",
    "# model.add(Merge([cat_model, quant_model, quant_model], mode='concat'))\n",
    "model = quant_model\n",
    "model.add(Dense(15))\n",
    "model.add(LeakyReLU())\n",
    "model.add(Dense(1))\n",
    "model.compile(optimizer='adadelta', loss='mse')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "dense_13 (Dense)             (None, 4)                 20        \n",
      "_________________________________________________________________\n",
      "leaky_re_lu_9 (LeakyReLU)    (None, 4)                 0         \n",
      "_________________________________________________________________\n",
      "dense_16 (Dense)             (None, 15)                75        \n",
      "_________________________________________________________________\n",
      "leaky_re_lu_11 (LeakyReLU)   (None, 15)                0         \n",
      "_________________________________________________________________\n",
      "dense_17 (Dense)             (None, 1)                 16        \n",
      "=================================================================\n",
      "Total params: 111.0\n",
      "Trainable params: 111.0\n",
      "Non-trainable params: 0.0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Save the last 21 days \n",
    "test_data = rides[-21*24:]\n",
    "train_data = rides[:-21*24]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/10\n"
     ]
    },
    {
     "ename": "IndexError",
     "evalue": "indices are out-of-bounds",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mIndexError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-40-67fcf73fd4e8>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0;31m# Train the model\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 5\u001b[0;31m \u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtrain_data\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mquant_features\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtrain_data\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'cnt'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbatch_size\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m256\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;32m/Users/sachin/anaconda/lib/python3.5/site-packages/keras/models.py\u001b[0m in \u001b[0;36mfit\u001b[0;34m(self, x, y, batch_size, epochs, verbose, callbacks, validation_split, validation_data, shuffle, class_weight, sample_weight, initial_epoch, **kwargs)\u001b[0m\n\u001b[1;32m    843\u001b[0m                               \u001b[0mclass_weight\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mclass_weight\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    844\u001b[0m                               \u001b[0msample_weight\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0msample_weight\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 845\u001b[0;31m                               initial_epoch=initial_epoch)\n\u001b[0m\u001b[1;32m    846\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    847\u001b[0m     def evaluate(self, x, y, batch_size=32, verbose=1,\n",
      "\u001b[0;32m/Users/sachin/anaconda/lib/python3.5/site-packages/keras/engine/training.py\u001b[0m in \u001b[0;36mfit\u001b[0;34m(self, x, y, batch_size, epochs, verbose, callbacks, validation_split, validation_data, shuffle, class_weight, sample_weight, initial_epoch, **kwargs)\u001b[0m\n\u001b[1;32m   1483\u001b[0m                               \u001b[0mval_f\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mval_f\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mval_ins\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mval_ins\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mshuffle\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mshuffle\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1484\u001b[0m                               \u001b[0mcallback_metrics\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcallback_metrics\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1485\u001b[0;31m                               initial_epoch=initial_epoch)\n\u001b[0m\u001b[1;32m   1486\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1487\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mevaluate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbatch_size\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m32\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mverbose\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msample_weight\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/Users/sachin/anaconda/lib/python3.5/site-packages/keras/engine/training.py\u001b[0m in \u001b[0;36m_fit_loop\u001b[0;34m(self, f, ins, out_labels, batch_size, epochs, verbose, callbacks, val_f, val_ins, shuffle, callback_metrics, initial_epoch)\u001b[0m\n\u001b[1;32m   1129\u001b[0m                         \u001b[0mins_batch\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_slice_arrays\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mins\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbatch_ids\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mins\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1130\u001b[0m                     \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1131\u001b[0;31m                         \u001b[0mins_batch\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_slice_arrays\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mins\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbatch_ids\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1132\u001b[0m                 \u001b[0;32mexcept\u001b[0m \u001b[0mTypeError\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1133\u001b[0m                     raise TypeError('TypeError while preparing batch. '\n",
      "\u001b[0;32m/Users/sachin/anaconda/lib/python3.5/site-packages/keras/engine/training.py\u001b[0m in \u001b[0;36m_slice_arrays\u001b[0;34m(arrays, start, stop)\u001b[0m\n\u001b[1;32m    386\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mhasattr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mstart\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'shape'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    387\u001b[0m                 \u001b[0mstart\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mstart\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtolist\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 388\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mstart\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mx\u001b[0m \u001b[0;32min\u001b[0m \u001b[0marrays\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    389\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    390\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mstart\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0mstop\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mx\u001b[0m \u001b[0;32min\u001b[0m \u001b[0marrays\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/Users/sachin/anaconda/lib/python3.5/site-packages/keras/engine/training.py\u001b[0m in \u001b[0;36m<listcomp>\u001b[0;34m(.0)\u001b[0m\n\u001b[1;32m    386\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mhasattr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mstart\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'shape'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    387\u001b[0m                 \u001b[0mstart\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mstart\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtolist\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 388\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mstart\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mx\u001b[0m \u001b[0;32min\u001b[0m \u001b[0marrays\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    389\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    390\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mstart\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0mstop\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mx\u001b[0m \u001b[0;32min\u001b[0m \u001b[0marrays\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/Users/sachin/anaconda/lib/python3.5/site-packages/pandas/core/frame.py\u001b[0m in \u001b[0;36m__getitem__\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m   2051\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mSeries\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mndarray\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mIndex\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlist\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2052\u001b[0m             \u001b[0;31m# either boolean or fancy integer index\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2053\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_getitem_array\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2054\u001b[0m         \u001b[0;32melif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mDataFrame\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2055\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_getitem_frame\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/Users/sachin/anaconda/lib/python3.5/site-packages/pandas/core/frame.py\u001b[0m in \u001b[0;36m_getitem_array\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m   2096\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2097\u001b[0m             \u001b[0mindexer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mix\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_convert_to_indexer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2098\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtake\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mindexer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mconvert\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2099\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2100\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m_getitem_multilevel\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/Users/sachin/anaconda/lib/python3.5/site-packages/pandas/core/generic.py\u001b[0m in \u001b[0;36mtake\u001b[0;34m(self, indices, axis, convert, is_copy, **kwargs)\u001b[0m\n\u001b[1;32m   1667\u001b[0m         new_data = self._data.take(indices,\n\u001b[1;32m   1668\u001b[0m                                    \u001b[0maxis\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_get_block_manager_axis\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0maxis\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1669\u001b[0;31m                                    convert=True, verify=True)\n\u001b[0m\u001b[1;32m   1670\u001b[0m         \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_constructor\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnew_data\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__finalize__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1671\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/Users/sachin/anaconda/lib/python3.5/site-packages/pandas/core/internals.py\u001b[0m in \u001b[0;36mtake\u001b[0;34m(self, indexer, axis, verify, convert)\u001b[0m\n\u001b[1;32m   3953\u001b[0m         \u001b[0mn\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0maxis\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3954\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mconvert\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3955\u001b[0;31m             \u001b[0mindexer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmaybe_convert_indices\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mindexer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mn\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   3956\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3957\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mverify\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/Users/sachin/anaconda/lib/python3.5/site-packages/pandas/core/indexing.py\u001b[0m in \u001b[0;36mmaybe_convert_indices\u001b[0;34m(indices, n)\u001b[0m\n\u001b[1;32m   1871\u001b[0m     \u001b[0mmask\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mindices\u001b[0m \u001b[0;34m>=\u001b[0m \u001b[0mn\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m|\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mindices\u001b[0m \u001b[0;34m<\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1872\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mmask\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0many\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1873\u001b[0;31m         \u001b[0;32mraise\u001b[0m \u001b[0mIndexError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"indices are out-of-bounds\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1874\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0mindices\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1875\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mIndexError\u001b[0m: indices are out-of-bounds"
     ]
    }
   ],
   "source": [
    "train_x = [train_data[col].values for col in cat_features+[quant_features]]\n",
    "test_x = [test_data[col].values for col in cat_features+[quant_features]]\n",
    "\n",
    "# Train the model\n",
    "model.fit(train_x, train_data['cnt'], batch_size=256)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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rOtGjl6krZqJrVOcwdZiLxowSgS0zqllGndbfZWtTm/91285Ep3GOtSeiq7T9\nzrZRt922ozn+4uV9xIidDgCAJXadNkcbIZUU7gC24Tgkt8xhVzxZIAiCINYeJKIbggsJ3zNOdLGg\n9VwndaLtS9yVd4WTMRFt60RzIRG45mQhf9mIC5w1EwAAeg1NLMwTJOMTNtvLRK/dOIdxTMuY9s86\nfi8eCa/Ded6StctfJcfPhYQvEww8VSuaJT2rMat8TgAggDopciGwITpcsPTKVIk9AcDt3z6I/+2z\nD1iNRRAEQUwHEtENIWS2Okf+ssaJ9lwHnsPsnegKrcabKnHHxTC2UqZj4eaudqLr1IkW2duTxzTu\nqIlz1ClPVok1HOcwqYMyud2dB29Bl0V4qfdIrf1npdsrkQiBgCWIvA0AAIf3rcYcFe75y0op4WF4\nwteJ563GHImQlBDu33j8KG6//6DVWFn+6aFncN3H7639PARBEEQxJKIbQlXnKBfnMNU5AKDju804\n0YWungBjSLv52VYE4TIT5yjKRHOB0HcwE7i1MtFl3cvxyArFOYpJnegy3QMD1Vj0uezISOa3CpVy\n/FzCR4JIO9EOt4tzjDYlKuF+IwFnnh7TTrhnt0+ZyYk/dfDD+LqsP8f62/tP4qvfPVIqtkIQBEHU\ng0R0Q6iIho5zlKhl/Fb+GeDAfQg9p4agHd4uk8P2HJYK4HpOdLl62GZi4UzgNZeJznmdMZc4D8fx\nO9/8YbzS2d2iiDZxjjVY4q5CJvqEp5oenc+OIk7sRBqvMMkv4RIBEsS+EdF2gtaM4zqs1OfExzBC\n4tYcEyjnRL/i+KewmS0hGdhFVsbHpcmMBEEQqw+J6IbIOtFl8sm/lvwV8BevqOlEDx3twjiHmcxo\nRLSlwMxmoktV5/AcdHzHOrIyPk5+JlrguUxlWH/Hu7X9THTvRPEbcZqRtv0usd5G/56Pw9YnKNVa\nuAsEiMH9WQgwuEm9OIfnsMLXyYUW7jpC4go797tqRRDD4Oj3rcZLxzUiuq0oE0EQxBkMieiG4Nrp\nBYon3IlMt8DQd9C3daIruMIJl3iVczc6A9W62bqsXmbMokvGERd4YfQANrjcejygfFm0hEtwvUu/\n0HnS2i2tjIlzSA7059oZsyGSCh0LIdR++mwcsT5BERlXuNTEQiQQToAIgb2g1eMErlPqKlE2QtKI\nE11i2x53zwEAxEf2WI03PlZco9MiQRAEUQ4S0Q3BhYTjMDisxCStTLaz47kYWJe4wzBCUiBonWQZ\n72d/gnMLb2jqAAAgAElEQVT/7nUA6oloEwkp0lFnxYfwW0++C9dHH7Z+jWbMlW6PE3OBkGVbU9uP\nOYlv7jmKa/7ka/jmnqOZFcyUQVtjkY5Kran1ycKzxGHrbWucYN8tFtExVxML4QaIWAjPUkSbcXzP\nKXGVSMBnHImvnGjPModdtTrHMVdFZcSxek40JyeaIAiiNUhEN4SQEi5jpXKX2QlSM56o1Wyl7GRG\noWvseieeANBOdQ6ZKHG5K76vnhNdMgKQCIkQQxE9u7zPesyV2HN4AW/52D149JkFfO3xCSK6d6LR\nMVebNM5RyolWk0O76COM7Rx3I+4C1ym8YmNcYbg+IhbAq1mdQznR+csa99vksG3d76pOdI911Y3j\nT1iNZxi2GicnmiAIYrUhEd0QXGeOGSvOXTIxFF3ns2O12n6X7ZLIeDzyt7WIrlCdQ+oxN8l5DCxz\n38CoCMlz2NREtOHrPHf+IesxV+LxQ4vpdt5/fBlRItR7l922a6xCR5WJhSbOAQBnDw5YjZcKWs8p\nV+IOCaQbInbqO9GeW3yCG+tMNNdOtC9tnehqHQtd/Z3gzu21Gs/A9bjkRBMEQaw+JKIbgkuAMcBl\nrHBuGcs4l9txyHpioZDDLomFImik85q0rwjCy+ews6/TNvcNjFYhyTtBMaLLsHXpe9ZjrsRcT4nl\n52+bxf4Ty3jvPzyEN//FPesizlFGRDMxPFnYZNmEJI1WuGWiFUrQwg0Qs9Ba0Jp4VeAWC3eu3e9E\nO9HWcY7MTlsmKuNJtW2DmiKaJhYSBEG0B4nohpBSOdFlJkxl4xzPkYdqOdFlXeGs0DvHWarnRJeI\nkEgp4WQc90GNEndlJxbGY060bUm0Scz31XNfev5m7D++jHu+fxwPH5xXsRV/Vi20xhqupCK6TCZa\nDE9QPG5Xik3IoYgunOSXCB3nCJA4IXxbJzo7ZsHLjIUaU/ozEHBqONFyxduTMCI6XHoK4DVqqtPE\nQoIgiNYgEd0QXKhMNGMlJvllBO2z+DM1q3OUm1iYFdEv8A5bi+hElMthJ0LCl0NB203sxaWJypjb\nE8c0E9E0ts05JjHXi+E6DBc/ayNOLMd44sgiejFHHA2A7llqoWi50TFXGyPwynQsdGRGRAvbqhXq\nt+8Wx57iJILDJJinRXRNQVsmzsGTBB4TkG6AmAW1IyRAuW3r6s+KI2Jgyc7lB8iJJgiCaBMS0Q1h\nqnO4Dis8aDqZA/O5yUHrvPBIl8SCp8hmoi90D1tP9BMlq3NEiYCfEbTnRE9bjQeUd7+zEwt76DQu\noud7CTZ1PDz37Bm9Lur+Xr8H+F2AuYBlLePKHHkM+PCVtTPYopITzRFBtXG3neRnssKB50LK/DKJ\nItYnfl6oRXQ0cdk8zGv0y8Q5zJg6hx00koku/qxlTziR2O+3aYm7tmqkEwRBnMGQiG4IU53DYazw\nkrGbiTlsEiet4xyJkPBKNj7J5lkvdA4140TnvNAoGc0nn8vtRbTIOO55DlvMRRrn6DkzI3GSJpjr\nxdjU9XHBWTMj9w8GfcANAK/Tnog+9CBw5JHGqjmUufrPZIKeo167rRNtxglKVHgRsRKTzPXBnRCh\npaA1YwRecYSE6/ePub7OYdvtQ+mYJYQ7oOIcPRnoB8f5C+dg3s8qDV4IgiAIO0hEN4SJHDglqnM4\nRtCGm+DLqFbjCiNGihqfZOMcz8UhqzHTCVpecYQkyghaQMVWbFEVQdzCMU2baABYZrMjJytNMN+P\nsbnr4wLtROuECaJoALg+4Lcooo3QiuvFR4Ztoov3B0ckSFiAhPkIhF0m2nw2ypz8ce3IMi9E4nUR\n2DrR2RJ3RaUgtRNtIiT2TrQaJyxRhQQAfMRYREf9we33W1Hh/SQIgiDqQSK6IYQENosT2IzFQrfL\nNTGDzhYEoo/YMr/IM9U5io7TWVd2E1u2cqKTzGVxM/4kokQgzDjR2+SRYqE/gezJQp72T4REwGJI\nMAycmcZF9FwvxqaOj7NmfMwGLn5o+xZ4DkMSDdp3os0+FNuJ2fRpUueyeFkmOTjzEDkdBHUbn7jF\n7epTJ9oLIZwQAWxdYfXbK5HDFomJcwS14hxcf6ZDv7yIXpK6VnQNEZ2kcY7T1Im+/5PA7r+c9loQ\nBEE0gjftFVgvcCHxPx18L14utuBO+V9yl00FbXcz/P4xcCEhdKa6CkKoLmxm/DyGmWiGLiKrEndZ\nR0+NP3nMgamsAECCoYNIVc/wqr1GQDeV0a8zz2FLhECIGNINkDgB3BpiZCXmezGes7kLxhhe/5Lt\nuOi8jTiyMEASDwB3I+CFtfKslTCvrSEnukwZNiY5BHMRsw4Cy9c5jFa4I3+vhBHRjheAux2EttGK\nkYogBcumwj0Ad0Jr9zvbarxM3jyQCZZSJ9o+znHadyz87K+p3y95m6oJShAEsYYhJ7ohuJDYwE9i\nC+YLXT3PHJg7W9KyXTYlqbiQ8J1ycY40QtLZjBCR1cTC1IkuIdyjRCDQLbgH3kaEiO1rUwuUm1jI\npRbRIbgTDLdzQ8z1EmzqqvPO977mUrzlyufhOVs6Suy5PuB1azvDpTGOaVzP+U4Ex48534bgxe+N\nI2JI5iJxOgikZZwjFZf6ykJemURuohUhhNdBFw3UiS5sEKTGdLwAidupIaLV5yv03WJBKyVCFmMR\n2okWdTLRatxS3ydC1BLstTj8yHTGJQiCaBAS0Q0hpYSHRHVYKzhQpzGDzua0hJaNc5Rt+13oRKdj\nbkLIYqs4R3aylBp/8rJqkp9yohN/AzqI7JvKZCYzFk8sVHWFhRNYTwqbxHxfTSzM8uzNXVUn2g1W\nxYnef3wZv/JXu9Ebr7OdxjnsnWgpJS7FE7g5uBE7ZbGoccAhmI/Y7VpP8svWiQbyr2aYVvWuH0C4\nHQQsgbSooZxkIiSFmegkWxGkg9A6QlI+h22E+7IM9YPrZKLV71LfJze/Cvgv51iPZUW4Sf3e+/V2\nxyUIglgFSEQ3BJcSnojhgRe6XalD2t0CV1c5sClJJYSEV7Ltd3YyYyjtnOhslQMg3/3OTiyM/U3o\nWEZIgPKtxtMxvQ64G6YNLJqgH3NEicCmzqiIfs6WLiBiSNdflUz07n3H8U8PHcKew4uj/0gnFto7\n31xIbGDq8Z0SEwUdHedQ0Qr7OMd5OI53Pv2/YgOWC+IcxhXuQHpKYCZR9debLXFXtqye4wbgbojQ\n0v1OMpnoomYrPFL7zJJxomtV51Cfa17Gid73DfXbcq6CFRvOVb+//7X2xiTWD/054H/fDNz736e9\nJgQBgER0Y3ABuIjhIylR4s5EK7bAlQlccKtqGWXrJwNjIhqDWk60X6I8WZTJRHN/IzrMTrgDZmJh\ncW3qfiwQshjwAgjXPs+6EvO65ffmMSf6nA0BPJkghqed6GZFtKkhbrolpiT1JxZyOaxm4qJYuDky\ngXRcJG4HHUSlmoicMqaQ2OnswaVL/4IXsIP5J5z6NTp+AOkpgRkPqjvvaSa6zPwBPhxTuCE6TTjR\nRZ0ZtXBflPWrcwzrRFd4b/onrcerjNlv932zvTGJ9UPvhPr99f9ruuuxHlk+DvzDb7cXSVwnNCai\nGWMuY+xbjLF/1H9fyBi7hzG2hzH2N4yxQN8f6r/36P/vaGodpomQEp7UIrrgoJk6pN0tAIAOIrs4\nR8nugUBWuG9CYFlWj2ccPaBEdQ7dbEUEGxAitm9vLiV8zwj3yevdjzkCJGBeCNlwnMOI2PE4Rzdw\nESABZ75quNKwiDbbzIj4lAYmFnIh0xMdVxbHJBzJIZkH4XYxg4F1jt9UbVGflcnLDvPJIaSnBGbS\nr/4Ff2oMKS9CMsxE8xonC2mJuxJOdKzd9eUGStylHQurvDcn91uPVxnz+eidaM8B750ABgvtjEWs\nLlLv10tHp7se65F93wTu+zhw8P5pr8maokkn+l0AssHKGwF8QEr5AgAnALxd3/92ACf0/R/Qy615\nuJBwZaKd6PyDQ9q+uKNEdBeRVZyDZ+IcRQ9nRlDq2tQ20QojPtJoRUF1jgAxhBMA/oyOc9g50XzE\niZ48phLRsRLRXjjS7KUucxOc6K7vwmcJEuatSibabLNTnOhURNu7BonIONElRLSrRTT3uuhgYFVG\njUuZTjgNWJw/sTDNRIdgRkRbtFUf5rCLy+rJRK0b80IIV2Wii0TwSpiTvVJO9EAJy8UGq3OUem+Y\nqpCCuRZFdDoRVgKiuc9nLp9+K/C5/9jOWMTqYj4bNSbfEhPInuASpWlERDPGtgP4WQB/of9mAF4B\n4Fa9yM0AXqtvv0b/Df3/q/XyaxohJFwRw0NSOMnPkzES41wC6DB7EV3aiTYiqaMbvNjEOfiYE53z\nFIOEp5P84HUQMnsneqTVeM7L7MccXScB8zqQrqorbFubepz5ntp+4yK647vwkSCBtyqZ6FRE94aC\n4+DJHp48oi/B13CiRcaJ9kpcQXGg4hzC7aLLIsQW+5DICPeiqzZSHzBdP4T0VYMbbiGizefRK3Ei\nZtxvzw9TJ9qmccmw2YpbKMITXVaviTrRlUrcmXxy2060oyurtlUOcuEQcPSxdsYiVpe29pkzEbNt\n24x3rQOacqL/BMD1AMzRZiuAk1Km9tYBAOfr2+cD2A8A+v9zevkRGGPvYIztZoztPnLkSEOruXoI\nyeGCl8pE+zIaXv4HEOoaytXHHLbDLmzwMtYlMY6ru0CpE11CuJs60dINwIJOPSc607EwP84h0NGZ\naHghQsTW3SDHMU70ps5oafWuEdHMV050zZJz45grBnOZOMdf3b0P9+7RbdRrOtE+M4K2eEKsKzmk\n40P4Xcygb3Xip9xv9VrCohNO40QHIZivXFo+sI9zlIkhyWTYsVC6IQLGkSQWn5VMnKPoBHc4sbC5\nTHSpiYXBBvW7LSdacOUgmgodbQmipK+ENLH2mUZJxvmn1WTGRz/X/thtQk60FbVFNGPsVQAOSynv\na2B9UqSUH5VS7pJS7tq2bVuTT70qmIl7niwT54iROKGqKwygi0EDTnT+smlZvXAjAIDx6gcwc2B2\nHQbGCiplJKZSRgDH76o60ZYl7rjITmacvFw/5iqH7YaAF8JjAlHUTC7axCnGnejQdxAgQSQ99X42\nHedYYWLhyeVomKuvmYnOusK5lTKEhAcO6biQXhcd2OXqhVS1vNMx8z4rWky6XpiecNZxooMyJ5zp\nmAGEZ4R79THNSXHolajOoU+8uD9r7qg8nqFSx0Ij1k8+aT1eJcxno7NZj9+WiB4AS4dR2GmHOP3J\nnmC2NQHu+BPq910faGe8aWE+nySiK9GEE/1SAK9mjO0F8CmoGMcHAWxhjBnbbjuAp/TtpwBcAAD6\n/5sBHGtgPaaKETVFmWgppRLaTibOYZGJllJCyOEl6jJxjoR5gL4sbiP2zCq6DoPLWK7oGiQCAUvA\n3BBO0K1V4k7IbCY6x4k2kxm9YYZ2YDERbSUW+kpsbpjgRMdwV6c6RxrnGAqr+V6SxjDqlrjzs9GK\nnH0oFgIuBOD4gD+DkCWI4+pij2ec6ABxgaDVJ6ZBB47+rAgbET1emzrvo2JEtB+mFUFscthcCGzC\nEn5w+Z7CeJfpkij8DSPrYEMa5ygjGM13wNwB6/EqYT4bRkS36USLBOgdb2c8YvXInngtPN3OmPoq\nGOZbGm9akBNtRW0RLaV8j5Ryu5RyB4A3AbhDSvlmAHcCeL1e7FoAt+nbt+u/of9/h2wquDpFTObY\nLXD0uJAIWQTuBKmI7rLqcQ4zhMuKXWG1frGKkOh6u54YVK46kHZh44sInPzL/yoTrZxoN+jCZxzR\nwL5cWJqJztEGvYiryg9emL7O2OLy/0r0Yw6HDaMsho4LuEzqEncd9SXfoONlTjzm+8NIwXw/Hk6a\nrCmig9QV5rn7bcKVEw3HhTSu8GDJbkwTIWH5+5C5WuL6IVgqouvViTbrMJG0rN6wIojNmImQ+Dn3\n63jrE9ejI/K3k3GinVCJ6LThiwWVJhYaQdJWnCN1oluOc5iTkoVn2hlvWtx/y/rvBJm9StOWqDWf\nx4WD7Yw3LabhRD/yDyoq09aJ/CqwmnWifxfA7zDG9kBlnj+m7/8YgK36/t8BcMMqrkNrOHqmeVGc\nw1RE4E6oRBfsnGhzsNz5zKfxUuehQhHtyWQkh91h1S/HG234I196Ld7ufC6/Okcs0nJzbqBr/Fo4\nempcCc8pzrMq4R4BbgjH1805Bs04w4NEIPRcjM+BnXGVyB1Id+hYNHiZ2jjR2Uz0fD8ZiuikZiYa\nav2Lys0ZES2d4dWMpF9dRAs56kTn12zWr9n14YT2Itrs5n6Jhj1m1r/rB7VENBcSs2wABglP5Iti\noZ1oI6J53ECJuzIi2hw0l460I2iN02Uy0W3EOaQcjru4zkX05/4jcM+fT3stVpdkCk602U/lOo8D\n8SmI6Ie1t/rEV9sbs2G84kXKI6X8KoCv6ttPAPjhFZbpA3hDk+NOGyGGZbuKxIiqkxvrOIcSI12L\nOtFGCLzkyY/jhHsJHhWvzl3elXrMjHAfxAId3y09ZupE9w7hWewEnshZ5UEi0GFKRHuhrqxg6Qpz\nKeE6KkZSpk40vABMd39LGsrN9WOOjn/qOWfHUeujMtFaRCf99GSlLmkmOiOiF3oNOtGpK5zkRgBi\nIeAxjsTxgcC8n9VPirJl9YKCz4pjvtTdEK7entLi9XIpcT6O4KqH/jNcvKaUcPf84UmutHSiZ/S2\ndQvKcZkuiWGni0i6SOIB/NxHTOYcfhgvcR5DIl5QYiUHStAO5oHBYnr1ZtVInegt+u/m6rhPRCRD\n8bN4ePXHmxZSqhPquaeKl13LZKNO8y05w9n9VKircauO4MC3b1Gfy0t/HtjQwryw1IlusTrHWTvU\n7xP72huzYahjYQNkO785EBA59U9NdQLhhqlzGVq4wmnZLhnDBy8s5eYjHnWiEWHAq2WUhZRgEHBE\njJDlu4iDxEzyC+BqEZ3Yimgh4aQ57MnL9WMBH/GIEx03lIkexMqJHqfrqG3Yl95QhNRx9Q49PBIH\n6adxjqwTHacnbU01WymqzjGMc3hw9MmfTT5ZiOHEwqBoYqERn24AR+9DNiJaCImXuw/iBftvxXZ2\nJNeJZpkSdya2ImyEu5AImHrvWEENbqGd0qDTQQwvzUjb8Fr5Zfyp/6Hi2tY8ASRPGz4hrn5VoTLm\nqkka52h2/sDKY2bGWM9xDnOysFYvi+/9RjlRnBXRbTvRADDf0knKU/8G3PZO4Iu/C3zrE+2MOY1M\ntDmhPrG3vTEbhkR0A2TFCJDpSDhh2ZANm5AAdnEOc5B0RAyfFdemds1kRi30QsSVa0UnfHiyEBZM\nRDNONNwArj5ZsBEjgBLvLmNwHZY7Zj/m8GUMeCGcwDTnaEhEJxzhCk60cXIHwh060bbu8MkngY9c\nBez58nDc1IlW40gpG51YGKDcFZSYC7jgYI4LFhgRXS8TXRTncHikJmw6Dlw9pk0JwURIdJ1yXRIZ\nH3YsNJNTbfbbhMu0Y2dRPWwT53B9JaLrVOcIZKSy5knBcxhh0D1L/W6j0oE5uWwzzpF1ERfXcZk7\nI4Dm9rfXCbJJ/ubNwL98uHg5I6KZCyy3VI8gK9zbEnvR4vD2YHHyck0yjUy02bYkos9sxkU0yzkI\nJkIMnWh9kO5iUD3OoQ/KrojUZfG8K9R6/bjjp2X1Oqx63WYuM85lgXCPEqGcOG84KUxauqZcSFUR\nxGG526kfJ+oExhte/k8aqtusMtGnflyMUBrIjIi2daLNl1fm4GAmFvZijigRGCQCEReNxTmyzVby\nXOGYC3gQYK6fEdE2lTIwFO4s/0SMiVg1sYGq0AEA0iIDLuRQuIcFr5OJGEIyMHd41cYuEy0QsmHe\nvExnRk+LaFkj5mBO4EWRiE7GRLTFCVFlTqnO0UKc40xxos37GS0C/bnprosN0XK577J0v93SXom7\nZAoiehql/MxnpT+n4iRtQCKaAEZzngDg5Fy+VZlo1YRkGK2o3hREHZQlXBnDZ7xgMqNufOL4aYSk\ng+pdC7N1hcs40QGLdcdCU+O3uqA1pfxm4xMICkRXFMVwIFSGNgitx1wJlYk+Nc7BdHSnL5zRTLQN\n5uQr8wWaPdGZ78dpNnroRC9bO09mvzDPl+eWJrpONFwPbqjqGUvL0m+jmeh8QRvrdLDvuejJAMwy\nWtEpWQ8bIlZuMGNpgxebg1gsRp3o3C6JWhj4YQcRvFol7kyVIFn0HOMiukYsqDTjdaLbjnOs50x0\n9nW2FTloCinVVYky+735juxsbmefBUavmLQl9rLvZ1uvMzV/ZHsnYunk5sNr8+QPJKIbIXuJGgCc\nnNn4KhKhnWjHhXQDdFn1ZisiU1khKBC0I5MZvYxwrxrnOKXG7+RlB7EuN+cGw6ywTZ5VAi44rvvW\n6/Hz7M7cyW9GjMALUifaxkVciUlOtPni7wk3k4m2FdHR6G+MiehenGajQ5OJlsJadCmHVu9DLL/E\nnXKiOeD4cEy0wkpEYyRCkufQuiJSghaqPF0fgdW25UIicIYRkrz5Aw6P0zFRZzIjz8ZWSorooINY\n1hPRnhHRRS7vVOIcxok2cY42nGj9Oh1/fVfnyF79Wmu5aDOHKGcuUYrZZzpTcqIHC+2P2bqIRnut\nv7PfAUcfb2fMhiER3QBZRw8o4USzWDnRAKSnWmJXjXMkY40ycmv8asE9kom2KnGXbROdL4BU228V\nrRiKETsBFCJGJ1nAuTiRO7FQmoO014HfMZPCmsldmhJ3p66gFtHcre9Emy+xERHNsWVGubHz/QRz\nveH2T7H8kk34aJwjbzJawiVccDgZJ9pm3GyJu7DgRIyJWDUIgupY2UcAZrFthcw40awozhEh1mM6\nNfZbdcKpTlA88PyJfvqA6YddFeewPSnKfD5bjXPs+TJw538tXm48E91GWT1zsjC7DejPr/5402JE\nRLdU9xsAnv52/czuCuZB4bLdLe070eGmFgWtKQe5uf04B9BeLjr7np9cmxU6SEQ3gBCjosYpqM4R\nIoZ0tWvpd63rRI9MCss7RnN1cJWZBi82bbhHypOx/Evxg4SrbeIGtcSlyOSwQ8YnjsmFHGbR3QCe\nvhTPG8tE8wlOtBqzJzJ1om3FgVn/zOMHscC2DWpfyTrRAfREUcD6S/bUfShPRHMEjIO5/jDOYV21\notyJmMMjJEy9xsB10Jc+HItMdJKJVhS5wtkctsnyM4sxuRAj5QPzOzOq9zsIwloTC7OfT1ZQm7rR\nOMfDtwP3frTECk6hY2G2wUsbzve0yH63tuVE8wT4f34U+OQb6z1Pah6U2O95BDAHCDe2KC4zMaS2\nxuTZ7HdN4V72imEyAHxtkLQlorPfAW1t24YhEd0A4060m1OdI0rUxEIYEe11rRqfjAvavEvUZv1E\nRtB2ECGqWuJupDxZXNmJtnERs68zZJPd0n7METJ9kPRC+KG9i6gGjoBPvBZ46j79/BNqausD83Li\n1K/OwU89mAwSgXM3qX1lrjfMRAeIEfubao2XnSgaFHQsjPUER+Z48Dvqi9YmnzwSCSoQl67oI2Hq\nio2n4xxWTrS++gMoEZ0XIXcyItrRTYJsK4JkT1DynegBBtJDJ/AQwbUWe9mJopIXXBpvUkQng3KT\nBOPxOEfLDV7a6pA4DUac6JYy0Wbb7rur3vOkc0FKiOhkoIwZf6ZFJzqbw25ZuNedQLl0FLhxB/D9\nr5cYsw9sPE/dbqtWNI/TiCmJ6DOY7IQ7QB2IJ9GLVbc5U8eYaSe6cpyDi1FHr6DVeIBE5QIzdaJt\nMtFD0ZU/5iAWqtxcNhNt5ehlRVc80S1NG60Ayok2Itr2wLl0BHjiTuDAbgB5TrQW0SMTC22daHNZ\nc/j4fsxx3kb1vCeXo7T9t48Esb9RLWQb58jst15RJCgxpd98eFpEO4ltnehy+23Al9F3VP7adxkG\nCIYNWCow6kQXlNUTySlxDtv91s+433kuv+QxIvjo+i4SeLnVffIYOZkv2k6p03W2+m3ZTTR9rjLC\nP3uJGpiCEz1Ym+XfymC2LXPba0LSlLNvnqegKZFaVhtQfrfePlsFPlDHzmC2/Xxy96x6Yy4eVutf\nJuKTDIANWkS3lokeZGrVt7RtG4ZEdAOMV+fIc6KXBwlCxHC1yGN+F11mVye6SpwjQKyqczgeJHOs\nStyJTFMZH3Hu8SjiWRGtXquNAMrmPMMcR68X89Qlh9dBEBoBVHOSn/4yG8RixTrRxqVYShqozpGM\njimlxCAReNZm9bxHFyPM92IwqPKBUepE243H+Vjb75w3VMTqdTLXg++56EvfKuZwiqDNGTMUPUSu\nrqXuu+gjgGM7sRAlIyQyQqIrgng6QmLzfo62VM/PRDs8QgQP3cBREwvLiIkVyMbKip1o/ZqaOIAl\nA7XOeQF3sxygLsVn/15NxsesUYP7tGZEdLVQrhBoXkSXykQPAFd3+63jXEoJ3PF/AE9/p3jZJBpe\nUW09QrKl/gkuUP4k1zQ/aSgGWTzmYBjvIif6zCXrOgHDMlMr0RsM4DCZZnahRbRNx8LspLD8Endq\nWekGAGOQXkdlom2arWTam+fWiY5juOD6y0e9VmYholXkwFQhiSde/u/HmdrJXoigUzPOMeYKF00s\nXE4aqBOdjqm2sdknZkMPZ834OLY0wHw/TrfHwKvnRHOZeT9ZvtBL9EQ1x/Xhuw56CO2zwpmOhXlx\njo5YTkW07zqIEIBxO0EbZl5nfoQkBtdOtFdjMiMfc/mLMtERfHQ8VzWXqeFEp1WCip7DnLD5XeXs\n1RLRevsUltXrqzyr66sxW4lzTKHByzQYydC2ldttqGpF+r1XsjpHNs5he2WBx8DX/hvw6D+WWHYa\nERI9ZrCh3vtZJSrDo3bLTwI6zhHq7yAS0Wcs481WcjPRPbWjmOYR8LuYsazOkbprsqjEnYDPeFoR\nBF7Xrk50ttmKzHcRRay/GDM5bNdCAImx7PekEnf9rBOdmVho7ZaMCdp+nB/nWOKsVmxldMyhcAeA\n0L3UE/AAACAASURBVHNw9myA40sR5nsJOloQDlwjou0nFpatEy30dmCeEdEBXIsDSlbQBgWVMjqy\nh9idTf+OnI7VPjRamzrOvWrjiDidzOg59iI65npOAMx+W+BESw+B5yCCl3ZNrMrI91AZQQuoz2Yw\nU8/tSko6iUlfjcf0Z6XNZitpq/F1Orkw61y2JqIzx7nj36/xPBXiHEkEeGaCvKxhWOjHlXl8MlD7\nq9dpt6yeia3UvUoElHeiw40AWIsiepB5nSSiz1iqZKL7A/WBMBPf4HXQsYhz8LFGGcUl7nSlDDOm\nZbOVsKToSrPIbgA4LhLmwbV0orPO5aTXOUh46qrC64C5HmLpgtl+yWaiFSZWEa44sVCNuZg0mYnW\nIllXTwl9F1s3hCrO0Y9x7gwDAPTrOtFVyiRmnGjXYeihA4fXy7gXjdmVPSTezPCxTghXWGSiebl9\nCAAC0UPE1MmQ6zjoywCOlXDPbtv8SZuqrJ4Pz3UQwwOzjHNk5ywUPgfPnOT6NbOepZ3owfAz4oXt\nHKjHc9jr1YnOxnPaEkDZ77kTNUR02ZMws4xxhQH7/baqQ5tGSNratv1mIiRVojLJQF019jot7kPZ\nqAxlos9YTslE55S4i/qnOtEdxIgrTywcOnpeQcWBVES4uiSa37Fr+z0igPKdaGkOVtqdTVho6SKO\nTmactM4qzmFEtDpZiJhvdflfDTz88jGxijwnejFmgOsBjtdcDltXxAg9B1u1E31yOcJ5s0rM95wN\navkaInoYOeD5Vxa0iHZdFXXoI7B6P2Mux1zhCWNKiRn0wL2hE83dDjzLfWikS2Ke+y2W0HPUmHWc\n6Cp13B0eIYYPz2G1RDTP5LALJydmnei23C7jRAM6ztGCK2zGaDOHPQ2m4kRn3r8mnOjScY5hxSfr\n/TbdZ0vsD7whV7gKXLvf/oy6slk032Di85Q8WZBy+Pn0wvY+J+kJCjnRZzR8vNkKJu+w8UAdvExm\nV4no6h0LT5ksVeBEBxknmvkqE23jRGfjHJOGlFIOO6bpMRMnhFtUu3YFhMCwygGLJ26n0eocSrhH\nCGrEOYaX+/pxCRGdaJfa69i7FWOOTDbOsXVDgGOLAzx5fBn/7iwlZOf9c9TyS0fthhs7Qcn7nhZ8\nWJ0DAAYshGflRA9jDsqhnbRyfXgQ4P5QRAs3hCerv5/jk3Bz3W+xhGUtogPPQR++9WRGX5Yb09FO\ntMO0iG4gzsFyImUAhvuaF9aPc5S9NG4uiwPqRLeViYVT6JI4DbJO9DREdB0numqzFVdXygBqlBMd\nNSxymdbEQnfY18E+IljyBFckqvtt268zjXPMtOd+NwyJ6AbgGaEHqMlJk4gH2ok2mV3PTkRnBZAH\nDikm13w2Ip8ZEe110WFx5TrR2UYZHhKICescZzrhmYMmd0P4cpBbz3rFMeWoizhJ+I9W51BjxvCt\nKoKogc0ZfDR0hHPiHPMxU6+tzmXqsYNJGufwXJw9G+LEcoyDJ/v4d2ep93HR3awuU1uWtBJjjU/y\nWqoniVpuKKItXWGewIMaJ2+iqOmCJv0Nw/X1OghEzasZOWUSAWBGLGHgqjE7votBDSfaKxnncESE\nBB48lyGSXm4crGjMtNlKaSc6bDDOUTRmLxPn6LQ7sTDQ+1FbIvqW/wDc9YF2xgJGq3OIuJyrW5fs\ntlyo0VK9Uom7jKAFasQ5RqNz+ctmJhbWcYWrYKJPtU8Whsex/PH0Z9gN23Wisxl3inOcuZzS9jsn\nzsFNi1TzpW5ZJzobrQDynacoSuAymcYc4Hcxw6LKHQuzYzqQ6ux1pfF4JlqhhTt3Q4Swq0KSFdGT\nYi/9WKCTNltRB+qY1RDRmUvUZjt1VnKi9XIRPOUce536k11SJ9qIdwfnbNDbUUhs36TE/EB6wKbn\nAPN2zRVGTsQYzy9xl8Y5lIiOLEV01mXNm1gY99VsfxEMRbR0O2q/qngQSzIVQXJL+QmhJjPqrPlM\n4KIv7SqCcCHgyZJl9fRkRreJOIc5mc/5DlILmzkLTU5equBEuy060W6Yma/Qkoh+6j7gybvbGQvQ\n25JlWqq34CRmhVkdF7GKoE0yl/+B+k50meOD2W/9mmVTq5AMMhMosfonC+az6HVUSdo29h9gmHFv\nc9Jmw5CIboBTJxZO/qKWqYieTX/7SMArfrmr2f/lhHsUqQ+965tLqSG6LK4uaOVoFZJJrYUHMU8n\nIBoRLVw1mbEfVa9NPcyWTo6g9GOOzdD1UXXt25gFue9FLpnLfWmsYiUnWn+59RGgH3Mtomu6Bnpf\nyMZIzp4N0sXO36g+tpF0a4noUyenTl5WGhGtnejYCeFZuMLZA2+QU/c7Xp5X4wbDOIftQSwbrQjy\nXme0AAcSsRbuvutgwCwbvHAJzwh3loDnCH9XREicAJ7DkMC1dqKzjnve1TC1giZu5TdQncMiE92W\n25VEWhjoz08d9/vz1wOf/fVyy/JBPXe2KtmJaEA7E+DMPhRsrPdeVnKFo+HlfwCILGtiV201PjKZ\nsY0TlMFwvkKdMUt/NjNzmFrPRIftlg9sGBLRDWAyl5KpzZlXJ5pFWkSHxolWH8yq3d+y0QogfzZ+\nOpkxFdH1q3OoOyaI6EScEueQrsph95PqEZJsDjsvE72JLamGMnqbJsy3ymGrgYdf7P14OMHvFKIl\nxG4XEo4SvXWaAIzHOfS26vguts6G6WLP3jDuRNvFOUZz9fmRA6FP0lwtRmLHLlrh6AOvZC6CnJOi\naHkOAMDMhDCoxkQAKoto5biXqM7RV2NyfzhmnQmx3kgmevKyvuhjwEK4DlMl7opc5Jwx04mFhZno\nPhInxO/f9pCOc9Ro0GEOuEVGwEgmuqWJhUZcuqb8ZA1xcPhh4NBDJceNgMVD9mNVZdwtbUOQmPev\ns6meO1urOkcbmeixfHIb2zbb3rzOmFXjHOlE4xonCv154G+vKzdXJ+u4t1X5pGFIRDeA6fwmdCUB\nJuLJ2V9z5mwuUwfqQ+JWdC8TIYcZYEx2hYGhE52KaL+LkFVvtqKEe+bgPMHVGSSnxjmkLuVnBGm1\nMU0Oe/I6DxKhnOjOZlWHFmoyo2ediR5e7stO8DuFaAlcl2HrxVxdYTAnSlVJhmMCoxMLTZxjJnCx\nJVD7Vl96wKbzVWtXi8vUCRcjk1NzW1OnTrSa1Bg7XSsRne4z4Qb4ORn3pKfiHCwcOtHMzCOo+AU/\nPslv4mezr9xvEWwaroflPpQImcY5goK8eSh6iJyZNM7hFAngiWMOT14L3WweYSA93Pndw/UOmlKW\nn7w0Xp2jtcvi4bC8Zx3hzqPyj+cD9bnMmavSKGllhZpdU6uQVj7ZVE8ApZnostU5/OZEdKnqHPHY\nCUoLTnQy5kTbXiniJR33Jp3oQw8CD30GOLC7xPrFmUY2FOc4Y+FSZRGFvvSc53Yx4/iYy9S6+oCN\nEz3S4CXnC2igK4L4oSmrN4MZ9Cs70cnYmGyCcFM1m8ecaC9UcY6KOWwh853ob+w5iuNLkXail4dt\njKEEkG8j9ICMuzYYcYRPIVqCMCI64uoKw8BSRI/XiU6yEwuVCHju2TPpVYe+0HEOSGCx+qVjzhM4\nTO2nfk60Ahg2W3F0JjpxOghk9S/a9GQv3DgyUfTEUoRv7hk6F0lPCVqnMxS0jv7MyIpfttloRcBi\nxBMnM+oIScb9TtwOPIva1ILHcPQESg88d85DIPuqkYzDEEsPjuRW4mukOkeRIEn6GMDH0iDRJ36W\nB2kzqx8od8nYz8Y5WnSiTZyjbuygzOOF0NuFA8vH7cerQupE13Quq5AtH9hIJrpkLeMmHPdk9Ls2\nf/0acoWrcMr72czVzcnjZZzoupnoEmP+L39zP/7TbQ9mtm2H4hxnMuayuNRiygOfOAHOS8ZEdA0n\numyDlyRSO3NamzrciBnZq+xECyERshJxjmzNZi26mK6H3bNxok3OU47muKNE4Nq/vBcf+NJj6EUc\nZznLYKZtKYDEtXRLgbHqHDlOdLyciuh+Ypxoy0vjY6XCBpkYyZaZAA4Dnrd1Jv1/X/rA5vPVYywi\nHaYMoXBDBIxPrLYCAELP9mdGRLsdVX2iYotqR+8zLNwEn3FE2uH+f+/eh7d87B4s9NXfoq9ORJxw\nOLHQ1Q2Kor7FCWfGiY4n7Peip+IcMhzuQ0JXlalKtjqGjyS3+k4oeoidLjxHNVtRK13djeZJAo+p\ncQrd7CRCX3pYini9FspZ8VTJiQ7aqc7BdSbaxDlqOdFxychB5nVZnNxakWZozdWaFp3ounGOSpno\nuNmJhZVK3NldCbOCN3RSVDrOYZzooL4TXSKe89DBOXz3mYXReM6Z6kQzxjqMsXsZY99mjD3EGHuv\nvv9Cxtg9jLE9jLG/YYwF+v5Q/71H/39H3XWYNsahlV4IwVz4LEE84fKtaxxnU/9Wf0j8ijV3T6lN\nnRPnSHScIwj0gSTchA4GaRe6siRCopMR0XKiEy0wC/2lamIrXgchqsc5RKZjoSfjEQF0ZHGAREj8\nf48dwZHFAc5iy6rZgFlfbwaBrF9fcyhmV3KiF9PJb8sDribZ2MY5xgrjDyc0OnAdhquevxUvu2hb\n+uXUF46KcwDA3IHKw0n9PFLviyLnIGbiHHCUyEs8OyfIiGizX/BYbedn5vsQEth3TD0f166w2x06\n0W6gxuz3qp2kJEKk+eQAycQJtfHySQAA62ZFtHbcKwrM7EltwHJEdBLBA0fkdHUmWu9jFmIv+/7l\nTTQGAB730BM+okSAex2oFsoWQij7HVCpTnSNKjZVaNKJTgblM7SGhZZy0Wlr6pp1hSuNmYlzNCGi\nIYuvwGTrCgP2Wf6yMYd0zIwTXWfb/ttflc8Kuw1ESMpOoDwlE13n/SyuG7/QTxBF0Wht6qRndyI/\nZZpwogcAXiGlvBzATgDXMMauBHAjgA9IKV8A4ASAt+vl3w7ghL7/A3q5NQ03JbTcAIL5uW6Xz5cx\ncLqAoze9FhNVG1ckY/nkPOcpTqtz6AOJdvdYRbGnBO3wS25S1YIoEdjC9HN3z1Zj+ao2dVURnWRq\nTnti1Ik+PK9e15PHl/GFB57BeUFfZaI13JtBx8JFVA8eVsrIitlTiJbh6tzukcV+vUz0WE4vG+cA\ngL/+n6/EL135vHS5ntATC4FaTrQ5CZA5wk0aUaadaOHauTJOJs4BACJWfx9bjPB89hR+4KZLgRN7\nIfvq4Oh1h9EKL1QHsahf8cDJkzRaEeS4wvGSEtFOZh+Slq3csyLaz+m0aURA4nXTTLRa5+pOtMis\no1vgRA/6PUR6rAHTr9Em0lHFiY6zdaLbKnGnxUjqRNeMc5TtcGdoa3KhKeXXZm53xImus12z82wK\n9iGTofVCgDkN1E+2KHFnO+biEeD23wAe/PsKY9Z1okvGOcz/00x0nYmixXMkFvtJaqCkJe6kaGey\nccPUFtFSYVSDr38kgFcAuFXffzOA1+rbr9F/Q///asb0TLA1ChfqQAk3gHD83NJdAV9G5HQzd+gI\nSGUnelTQ5lUEifXOysyBRAsYL6km9rKtxoGcEncJxxYYEa2cYSfoWmWik0ztWwccnPN0YtiRheEX\nYMQFtjijmWjhddFBzUw0HwzrRE/IRAczans+MzcYxjmsLo2PxTmSCVVB9P97wlUuULDBSkQb0SXN\nF3WeE23+56htwC2daGbyxfpETugv66OLAzyfHUQQzwNPfweIFjCQPkKT4wcQdJTYj3rV9tvsfprX\nsIdrJ9rtriSiq30+mRiPc+RPNI7dmbTtt1qZ6geT7JWhvO8DQDV9GkA3znHMCZGFq1dWREupXqvZ\n19ywnThH6kSb6hw14xxlHp8VlG3FOcYnorUVOQDU8SSu4SJmt1fRySPXzTkYqxcBSIrd0pExm6gI\nYr4ry3yXNJX9Ltv2+5RM9Opl3IWQWIwSJFkR3WbevGEayUQzxlzG2P0ADgP4EoDvATgpZfpNfgCA\nvu6M8wHsBwD9/zkAW1d4zncwxnYzxnYfOXKkidVcNbgQ8BlPRXRe1YFA9JC4M8M7TJxDVJ8slW22\n4uR8SIY7qzpoGhHtx9Wd6LBEWb1BIrCFLYH7G9Ix3aBrFeeIuRg5WfAzl+MPaxG9qePhom2z8KL5\nESda+jPoWlyKB5DJzEVpWb6VM9FL8MIN2Bh6ODTfV+JQJHbOzHicY1K7ceNEc1cdTDpb0klxVUgn\nhpo4R45A4GNxDpMDr+pepg6t3ge5dqKPLg7Qhd5m808Bg0UsojPy2v2OGnNQMROdLXPos8lxDtGf\nw0B6CLvZz6dd3d2sE5ybidbbzwlnx5xoizjHmIjO6w6aRH1EWkT3pRaYNuJgpOFGzj4f91RHOnOS\n21qdaC0u3QbqRJetzjHSya9FJ7rtChLm+yrcDEBaXT1Rz5PZXnnPIeWw9BtQr0lQpQYvDZW4K1sK\nEmhuMmPpyjnZZis1neiCvPlilEBKQJjv1JGmMmsvF92IiJZScinlTgDbAfwwgEsaeM6PSil3SSl3\nbdu2rfY6ribpJD83gNQieiUnOuECXdlH4mUO0qaiR8UJcOMdC3Mv3w5MDluPmzrR1bOlI5MZJ9aJ\n5tjCFiA6Zw2XDboIWYJ+VDGHPXayECJOXb3DCwMwpmIOf/6mFylRn8lEw5+FwyTigcUXUOZseqKY\nBZS7Fszi3E0hnpnrDzPgNpML+WDkdz/hCDwHp1yo0eu2JLQzbjmz2bjLaUOTvMmpqYjWJ2KWX3pu\n6l5t0kOqv48tRphl+n9zB8CiRSzLzoj7H2gRnVQU0ae4wsnK4lL05rGAGXSzVxwsM6bZOQo++ORK\nODr643dm4TKGWJpMdHVBknWii6qt8LiPgVTv5TK0iLbZZ0ec6Jx11jW405NcI6JXOwNp6tA24kRH\nquJGUW53xIluMxPdyeyvNUTQv3wYeOyfipfjkTqp9mvmsLP7TV5pRsEByGE0x+/WKP1WssSd4Oo9\nb6JShtk+RWNKOZyE6wVqG7fW9ltHK3hkX56xYGLhYl9pCJGaew1s2ynSaHUOKeVJAHcCuArAFsaY\ntlWwHYBpq/YUgAsAQP9/M4BjTa5H25jSUszTTvSEiUTLMccM+uBetgubcaKrfenFQqSNFYD8TLST\njDV4CbQTzatWOVCTpKCbyjgiWtHtGsQCW7AEdIci2tOxlaqCdrwzY/Zy/JGFAbbOBrhs+2Y8f6Ne\nJuNEm6jMYHmh0pgARr5kh7GKleIcy0Awi2dt7uDQQlZE24yp30ORAELg5FKMLV1/4rr1hf74enYT\nQaT5MjfrnHc1Y8yJlpZxDnfMiZaJujqxMEjQNdGbuQNw4qVTnOhOV61nUnEfynbvCxEj4isfHGR/\nDvNyBt1g+D4zSyd6PBM9yYlOBkq4Bt1NcByGhDUT5ygqqyfjQepEL6dOtE0mOnspPkccjItoN4Sa\nSGbXWKY0Row4HgBW34kGih10Pi0R3VAFibs/Ajzwt+XGNOXJzN82ZLdX3n6ftqo3J/I1utyVdYXN\n+jTqRBftP/q7w0yGbaSBV4W23029nxPeywUtokcy0W1WPmmYJqpzbGOMbdG3uwBeCeARKDH9er3Y\ntQBu07dv139D//8OmXfdcQ1g3FLmqrPGSZOXlgccs6wH4Z/qRIcV4xycj04szKsT7Yw3eNECJuTV\nnCcuhOpYaET4BLdrkAicxRZGRbQuT5ZE1V5nPFabOsCwVvSRhT62bdQfvp7Ks2Yz0Uxv237PQtCa\nL1c9sdBhgO+OOcJCqBxpMIvzNnVwaK4/LF1o5eqNHkwOLfRx3qbOCsupdVtOtOAyM5srkpZhM+uc\nF+fQsQuTibY9oLhytDqH5AMcW1L3nT+r9qXk5AE48SKW0B05cQln1OeGV46QDLdrwCaXn2SDBSxg\nZsT9doLqLpuUclRE50RIlhdVDKczq+MtTAsEq+oco9nvSRWCAMDUiQaARaEP1jYHsLJ51pWcaL0e\nq4oRl4zVj5CUdS/N56h7tnU30cqMV+eoI0aSQcnYSjx0LvH/s/fmUbYkd33nJ3K9a+1v631Vt3aE\ndiEjISHZYI+NMXjwwjbghXVsfAYvY3s8cBgYyzPjg8HWMci2vMAYYxuDAEuIzcJCSC1Qt4R6f728\nfv22qldVt+6aW8wfEZHLrbtk5q3uhun3O+edqrov80bmvZER3/jG9/f9Uf+7LCvnyANaWBFcVrV+\n87P7rK2JNkz0Mt13DtDCarKV0mW/jbTCX/37XLJY6E904S6ZWyy8zJnoc8CvCyEeAj4D/IqU8iPA\n3wS+TwjxBErz/CF9/IeAbf369wF/6wSu4SWNtCCI4yFtb65P9DCIaDPJts8BLJtIeHiyTinjXLGV\nhUz0PBBdTROdJlDq81NWOInhY38PDi4ASs6xzgDR2krPNc4K4biq9jtJEwtBFcswTPTVowmnu3oy\nHmsQnZNzmOIcwbCGW0ZOzjEOY3zHPi6rMMDKbXF2rcHVowmJqz/jOgVXCpNJwJXehDNr/ozjJsRY\njBN9PW5jJT2rKCHnSDXRmgWSNbV69pQ7h4wCdrW2/Q7tZicPnsOOhgxkg0bOEaWpmei44kLMVA7E\nbeHn+g9PfBx+7C3wgXthsIsV9DiSzYKcw/IMcC/fZiJJJUjSchbmSAz7Clw22+rmY8NE12Fop5jo\neAETTRwgLQVGUhC96sJvEUBNQXROEw2rySvKhPFPBp3MWLO9JM6KyixlL/XnsH2P0ve/GFUL0/Lm\njpJcrVoso4puNwWXK3ryT/8+HeaaToShzUnnFnF4eeCeJjOuCmhLWEFCUbbyYsk5TqLq5ZI2e2NT\nydXMKS9yufoTjpNw53hISvkGKeXrpJSvkVL+gH79vJTyLVLKe6SUXy+l8hqTUo713/fo/z+/6jW8\n1KEcJGKE4yFtd+727TCIaYkx0usUXg/tBo0ammhfROkKTiwA0Y7xpjZyDv2zUVnOkSj2W5/vCcUK\nB5e/CJ/8UcKfeB+QWdyJVk4TrR+SpOJDEsVSsd86XOIssbA34VQKoqeYLsDWbg5hLTmHHsiSkCCM\nMnu7OISf+ga48OlcCXcl54gSyWEKSOqA6KLV09XemNOzmOhgQGC1MmBWdzJJC58st7iLzQSm5Rxm\ngZJUZIWn5RxEE/YG6rO+qaUr/I2u4oU9LefIAG2rbdqsqMM2TLTXKfQfHv4F2H0UBlfh+c9hBYqJ\nzss5nBoFXiKTaIxaoHjEc+UcY9032x2tEbfql6fOf3+LvOoBnHiMo5+PjIl+AS3ujsk5TiDRr0zk\nE9FWsdUrLHBLMtHb96jF0NGL4NARTaaYyxVB9IvJROe/k0Wa6GNM9AkkFsLiBWsKaPNtrqiJXrZA\nybPfoMb3VQt4lZJzCEWSrAyiy8k50vyqvFTmxShXf8Jxo2LhCYSpqqfkHN5cOccojOkwQvhTINpq\n4svxwmz66YgSDS41IBcLHhI3GpBgZQ+HlmM0ZEUQLXXH122awhWXr15V7QwuweiAURCyQR8rx0Sb\nASEJ6mi/i3KOIEpIEsluP8dEz5Bz2PpzDsYr6JOBKJhk2ty9J+CxX4ZHPpIBZS3nANgNVmD14kkK\nUoPJmL1BwJnuDBA97jG221kfcxq1Bh+TcGeZRd2C4jumYqFJLBRevSS/VM5hQHQcsnukXtvxNPCU\nCRujZ3lOni5IaFqey1i6JFWTGc0C0++k/QdQRQ86Z9Tvu4/iBj2OZItWDkTbegdlUsFWL19lE6+D\nJ+YnFo4Hqm921lS/XUXOkT/HXaCJThJJQw7xNft9GOk26wCSqd2TuZHuFBk5x4oT9fnfgM/91OJj\n8glasBoTXWDcyzLRd6ufhxfqtVklDBMNLyKI1nZzNb3Us/cpyUSnIDp/nyuW/YbF1533Twadf7Jq\n4ZNli7CctAJeHNlKMFDzuhCrV71MpZBz5BwGRJsdZsfLyZBehkz0jcg00Zbjge3izpFzDMYhLSaF\nUsYAkd2kKSYLs+mnQ7HCUcoKL6pY6MYjxlZTPSAAtkNoNWjKEUnFNqflHGEsSQbXs3v5vZ+if7iP\nLWRBzmFWmnHFpLB8sRUw7hwJ+8OAKJE5OcfUdjHgNHQiWtXiHFAYAOJwnOlkdx/XP5/IMsO9Nmc1\niL461sfVZaI1oN3rKXA1U84x6RHY7YxRrTtppky09mxeMNBmIFrdnwHRcUXZiqkcmCUzTtjVTPS6\nU2z/F8W7ChIax7YY41W3m0uyNgu7RINdOHWf0q5e/jx+sM8eawVNtKtBdDQp//kWpFZeWycaz37O\nAq3XX19T4DKx6hdbkblFkEs0F0Sfv3ZEmzE729tYAg5jDaJfyGIr5vnUrixZBcGaoPaBfwG/+Q8X\nHxOHuiLaCRR4KQC9ktvx2/eonwcvMIiWsihbqbmoBlSeRxKV63+G5U+lOSsmv0FJEK37q9c+GSZ6\nYTLjLPZ7RU300sTCKeC+kpyjZLGVoJ/lxrzgTLSpHjtLzvHy1ES/7COWxp3DB9vDFRHRDCZ6Mh7j\nijhlSE1ETpMWk4XZ9NMRGTmH7viLmGgvHhLY7cJrgdOmw3BuwtOsUC4kOTmHYYV1kQqAaw//FsND\nXdY0l1hoHkxZ8cFUPtHHSyhf608AmSUWGqbLz8pEO0ZDu6I+OQoDPNsw0Y9nPw3b7GZM9PMjA6Jr\nAne9QNk7VNc8M7Fw0iNwOllVzJqDrJWCaC2TCBeA6Cl3DlOCO6m4KHLMYs8M2JqJbns2bjxipO3W\nnm29mgvuHcfOD4RXabJO8laQ/lpRnzy4Bq0dBaQf/gVsGfK7yb0FTbTxpq7iKhPnbRm99sJS4/G4\nTyIFa12dWGgsBGv5jBct7ubJOb7w9CUAzuzs0PYcDk2C6ipyDstdDIjHh8XM/5OQAJQBBpD1tVUK\nvJT1w84fmzLRz9ZrE+DRX4an/lu59grM5Qts/QaZnCPdij8Bd45Fco5jModVwGVJRxDd5hPXAzWn\nryTnqJjkZ5/A91m27Pekn8k9TypRdJ7F3UQRDCnRYLs3iq283CNlaG1Pgeg5cg7DOtmNIoiO+oH4\nVAAAIABJREFU7SYtMakMaL2cU8a8wiegZBthvkoiENltumI0vxzxvDZlWHDnCOOEZKiY6AeTu4h3\nnyQ4mgGi9UArKw5ARm8uNRPgETKJEobXnuUh/9v5yo++Bx7+CPSvquuynfRcX1cSjCtKDtRJ2ec5\nHg9ZM1Zzhom+fj5j17w2p7o+Xd/h87v685xUlJBIqQYdzdAe9BaB6CMCp52xmzUrTImkyAonc+Qc\ncSIRU2W/PddhJL3KThk2EZFw04lQxAF7gwk7XR+CIc/at3PZuYXf2P6GmZaCgfCxKoDoqACilZwj\n1JaFDHehfQp2XpECrs/yyoKEpI43dUGCpNnvuVUSJ30GNNL+FYsVXCv0pBVZjYUWd48+qxwjtre2\nafsO/UB779YC0Tl5zjImOu/hvqrTQTRZ/hmZhawBB/YSoL8oyjKX5tpAjX/NrdWY6F//P+C//+iS\n9qZBV2OFJL+SoMscm2eiV5EcWCVkTOaaTsKdo6Kc4wf/63k+/vCVFQFtSSY6Okkmuoqc44SY6Jyz\n1aw4pokuWDPe0ES/LCOKEzxdsVDYbipzmI7JQLGlXmu98HrstGgymV/VbFabBhzojm8tANF+MiJ0\nikx05HZoM65UQTBlot0mEgtPaHAw2ieRgoe5k43xRWIj72ge10RXfUiiWBd48fPAXZLsPs6aGGFH\nQ/j574EH/1+4613F+9ZMdLJK4RNgOByx2ZoC0UkEV7+ofvda2JbgrXdt8YnzR8pHu2qbSQTIdMLf\nPzIgeoacY9wjdLoEcaJ09KbYSkWnyLQctmYB5sk5RmGMbTzJNRPtOxYjPGTF+7RlpHS/eiIUyYTD\nUch604VwwMRd59vXP8hn23+k4MxhYiKa6jsvGQV9su5DUaTLN48Poa2ZaOBK824Cd70gITGlxqss\nFuJEZno/ncw479lOJgMmwk/bDK36gMTsRkVOK92xmRXnL6pEN8vv0PZtBpNYeaqvIufwu8fZy+vn\n4ZFfUr+PD4se7qsW6Igmy8eSaSbaeZGZaNuHjVtX00RH4/Lykbxu9wVyVjh2rH0SmuggtzO1IMlv\nXmLhKhVpYfGCQd9TgKMq5L6YTPRJ7izo2gNzw2iiYXVAO1U0bDp6M+UcL2+Lu5d9yJz9l3BmMNHh\nGOIwLfphPGFNJHXkHAa4GxAtw5mJiVGc0GJULPACJF6Xjhilq8JSbSYShwgcn8T2lJwjThDjfQ5p\nM+zcQUcesTl5Tp1QkHPUmzRDo4nOJzNGCWFfAfXDd/+QZoQl/LEfLpzrNbWNWi0QnQ2sw9GIjZan\nBuu9x+Hs69R/XPqcbkhd29vv3uGpvaGyuavaphns9Hsd9ge4tmCz5R0/dnJEpL/PMJZqYJdJZR3t\nQp/o/Wfgn74drj3GMIhwtNuEKbTj2RZDGsiKwMtJQqX71ROhFYcMJ7FK5tOFa672Jqmt4HSMrDZ+\nVF6eo6psFhl3GU1gqOs7tbYVEw2cb72+4MwB4KcgugL7HcusEJLXxllQbEWEQ8Yi2yUK7foMrXHn\nSJwmDvHMXaZxGHPlmt4p8jq0fYdBEKnS76sUW/G7x5mnT/4Y/Mw3qWOmQfSqtmjRuDwTrXfOlJzj\nJEpTl3VX8GD91tWY6Ghcws0hZ08GJ+MrXAYQH0ssXIG5THMkyhRbyYFomawsfZoF9g6GAYejMP2/\nQDocDsPV2O/UJ3pZYupUAqW3CoguKZUJ+tl3cGJM9PzEQkvkEgttV/2zHFV34Q9Z3ADRJxH5h1tb\n3KWAOA7hQ++D//xXCce6xG9zrXB64rRoiupMtEsIToNY2HMLn4yjhDYT4nyBFwC/Q5dRKvIvE4mU\nuDIE20VamnGPEuzxAQeyjb1zFwCvt7RrYUHOoR5MUXHAMyBIpMmMKrHQJDO6d/0R+DM/CV//Ydi4\nrXBus9UmkaK+PlkP1pPxSFUOHOwqMHDfV6tjLj2o7019tu+4extAJXFWrVhoBk7NRPcGQ053G1iW\nOH7s5IhYD3hhnKxQmnoKXOYH2d/9sGLan/ktRkGMQ0winDQ51XMsxtKrXvabkFh4uXL3I4ZhRNtz\nIBxi+R12+xOGQTyTiR7bbRoVigQVWOGcNzWDa+q19ik49yXQ3OR3W19WLPkNtBoegbQr2erFeQmJ\n11oo5xDhgNDOQHRkmQmsBhOtdxak28YlSitt5uNKb0xT6vf2O7Q9h8Ekqg+88lv60+Dg8IKauK89\nevJMtCnBvYi5NJKqVOt5QhZ3ZSsW2r4akw4v1C9vHgUVmOg8iH4R5BxpYuEJaGhLeNWn4CwF0fqc\nVV1lZnyf3/PTv8f3/+yDaZsBrgLVL8YC5RgTfQJyDlgM3l+QxMLZ3+XROGK74xflHKAX8jeY6Jdl\npNpSx8dyvGKFst/5IFx+CJ77NLGpnOcVWWHptmgxrqGJDsHxSMR8b+pRENNmROIW2xR+lzYVmehI\ns3q2j7S9VLbiBIcc0qFz7l4A3iC05KGZ10CqSVNUfDCjWBYYdyMhkaN9ANobO/Car4VXvP/YuQ3X\nZoiPqDvI5pjLzbaXJRXe8maVkHZdLxb0td13pstW2+Mo8asD96jIRPf7g8wDu3BdIUQjYlctxIIo\nqZ3ZbE8n+ZlrSGL43E+r3/eeZKhBtLQyvbmn5RxVJpQkUQxtYrnQWEci6MojhpNYMcBBH6fZIZFw\n8WA0k4me2B0aSfnPtqiJNrZ6gdJDg5JzdE7B33yah7wvOQaiG67NGK+Slr/oztHBmQbRuW1VOxoR\n50B0fAJyDum1cImZhMfHg93+hLYpr66Z6P5Kcg7tCjHLPs5U67vyhReGic7/nBU5H3dgxcTCKu4c\nObDXOa2ekdrAqwQTPVFVLwuJYbWZyxpyDvcEQfQi8K4XRd/604/wySd2V3N0WFJp8/y1AU9eG6S7\nOwFODkSv6BNdpUoiZHKOOguxshVFT1ITvSQ5tT+JON31c3IOvSjyVvDDfgnjBog+ichZ7whfaY2j\nOFGT5W/+Q2JsOHgWZ6TZr6liK7bfpsWE3qg8K5xO1LZPolnhWWzXOIxpizFyCkTbzbXKcg5hKr85\nHtL2U1bYCQ45kB3O3fFKAO62LjE8/YbMigjSAcGqWFQmjGVBE+1rCYk1PmAiXWyvNfdcx7YYrQKi\ncy4kGy0X+lfU/62dg1vfkh1rJDWW4NatFgPZqF6xcErOEUdjug3n+HF6IondHBNdczJJkwU9U2xF\n97/zvwFHzwMiB6KTIoi2LQY0ERWs/IyLTWK5YNlMnC7r8oijiWaig2GqY79wfZgVuMlF6HRoyYpM\ndA7QgmGiDYg+lR47DGIaU3KOlmczplrSXTwFoi0kcaT//s0PwA9swj9+HQRD3HhI7GR92LId5UBS\no8+axbzQyYyz8h12+wFtDBPdpePbmomuK+cYpztwx5loLeu6PANEm52x2proxVvGQE4TnWeiTyCx\nsIxPtOWCZWVb8qv4Uy8D7Snjrnc43RU00WV1u5DbhVhVQ1sWRKvFwhf2JA9dPFxNRxuHIPSzPvX5\nSim5ejTm8uGYA52bkjHRJ+CUsZSJnuFCAjWTjcPcs7boWTlBTfTSxMKQU10/585hQHT7Boh+2UaU\nrahEY50uI+UA0L8Ckx4PiFcDcHP/8+q4KSbabXZoiJD9foUt41jiSuUIklju3C3jURgr5skv6rCd\n5jodRhyNyg/uIpfYIW0XT0RMogQ/7NETHW4/d4qrUheNeOO3FU/WA4FdQ87hikwTbe7TnhxwJDqZ\n9/WcGIsGVm0QrSYlV0RsND0Y5hIm73lvdqyVAa+NpkufRn1NtGGTolAVeLn+FPzbP6OsriB1BEn0\ntQVxUps5SAufmEHWXMPF31U/7/4KuP4ko0AlFkpRZKL3ZQd7sl/+FjW4THRBkbG7zqY4IhgesW33\nIZ7Q0AVAwljOdudwuwpEl2Rl5jLRBkS3ttNjx2FMcwq4Nz2bI9nEDnql7zMyHu6Q9vvETNQXH1A/\nD56B3UfxknFWQh1wLEEg/JV8v/FaOESMZ8g59voBHZEx0S3fYRisIOeIAl0q2C9O0sEgs5288vkZ\nIPrFYKKnQPQqTHQeEJRhotMkvxX8sE2xmGXnjnXfbORA9Avt5mCOsT2lZRXWCy/n0CC6T1MRTil5\nUDNxPK2aWrzX/WFIGEv6k4hnLiviK3EaqzPRpTXR02W/9fhQZ6eosECZ066U6lk5MYu75RULT82S\nc9wA0S/jyIFLq7WBJSQiOEqZmI8FrwXg7YNfoy+6sHlH4fSGtmLrHZXX0WaaaA9pucVyxrkYjSc0\nRXCsSqLbXscTMcNh+Qcz3f63fbD9VELSiA4ZWB3OrjV4Rp7luuzSfsPXTZ3skmBjJdUmsTB158gS\nC8M4wQ0O6VvdJWfDuKIlWhq5ZBePSLlzGBDd2oK73zvztI2Wq+UcNTXRuSIkvmMrj9gnPg4//Q3w\nxK9mrJNOllJyjnoem5aMCIWbTvSpLOngaWifhrOvhf2nGY3H3G9dQOYWf75jsy+7uOPyINrsniR6\nhyJwN9igz9+3PsS3PP69ALRySbezmOjY7eCQlL7XONZ+6pCbTCZKE205Bdu1URjT8orsf8Ox6dHG\nrvB9GvY7sTIrvzT5+OiySjYDJlcepSHHhWfTSkH0Kkx0G1fEjOfKOTJNdMd3lG/rKu4c2h+/wCIe\nXtRtrMFzDyhwVJBznIA7h2l/XpjdIP8lYKINu5aWN6/RbhwCsgQTbeQc+tlZqRx2FSZazT8IsaIj\nSHk5hxQ2I3wFaL0VmOgoKC6qc3H1KLuPC5dUNd67bj6bgeh4oiRvldtckYmu+p2aIjzpnDLnsw1H\nKkHzpC3uZvRbKSVH44itjkfDipCI1PGp9m7YSxw3QPQJRFot0HaxWyqZzpr0UmujTyWvJsSmLQc8\n3H5TgbmEzK2j3zss3WYcx6k3tbS0DnsGE228qcWUhMTTyY2TYfk2U5bA8cDxtOduiB/3GVpr2Jbg\nQ+1v5+/6fwvLax47PbR8nMog2rhzZImFQaSA+9BZW3I2BKKJE9dk9QpyDg9G19WA5PiwdefM09ab\nLgexX13OkXc5AIg1E21cJEAlMuoJU/oZY1uX1XOSgFg4uYlef7/7z8Dm7bB1N8QBt/z+B3mb9TCH\nb/jO9FzPsbhOFzfYL88K6wVRoj1hA2+DDdHnbnGR00OlN19bW+evfPld3Hu6w1vv3Dr2HrGnv/Nx\nuX5rElPjHKAlDpUmurWtttx1jIL4mCbasgQD0cINy4No02cTy0v9b6WZWPpX4ba3AYLBxUdoiQlu\nM3s2HUswqclEG3mO5evEwhlyjr3+hC3X7EC0aXsO4zAhcVr1GL0CiM492z0Nou9+TzYx5kG07Sr2\nctUEuKWaaJEtMk+q2EqZRD/T11aRc5h7W3auYaKNnMPTnt11FgymLZksTtqEQvK12olYxZ2jBIge\n94jcLiAyaQXUTyzMERb5uNLL/r66q3asdra2MhAN9e617Pdpnn3TVl3ZSlKUsc1tN80d0McJofrt\nqproGf1vGCiyb7Pl0bRiZXdqdpO9dr1Kvy9xzBBd3ojKYcCl7WM11UfqhL2UiX5WnubJ5Cbuty7w\n5Po7ePPU6YaJHg7KbxmnTgpGWjFVFS2KE2xLEI3Ue1qNImtr6a2/oAKItnOMu9AWd4x6WEjGGtC6\nt72J/hzHj9jycJMJUZzg2OXWb1GsC1e4TSQi9b9txEcc+jctPT+wGnTj1VbwHhGbbVcB2nwp8z/2\nI1nylI6Npst+7CMnRywWmkxFajeny7jHgWJih3tqhe54CphoJlo0usCkyETXcOeIhQtOgwSBaxYb\nB8+q5Eldce3+R36Mzyb3cu5LvzU919dyDkvGCtDmk0jnhGGipXagiPwNNsXD2GT9Vnht/vZXv5K/\n/dWvnPkeibErG/dgbfn3n7HCHrYGNFY8UXKOnB4aYBwmhZLfJoZWGy96/tjri9p0iVSBIM26yzhQ\nORKDq4qJ3riN+NpjtJkQtLJn07YEE2qC6DggxMZ21S7ROLeo/sLFQ37x85fY7Qe81g1AdMCyaPvq\nfiO7gVdXQpKC6NykaUD0l/8vanJ8+hNw7vW5i12BvTQyB1gu5/Byki/HX4GJzo1pZXyiUya6RBGR\neZEyl8sSC6c00YZ5D/rgHF+ILm5zysrPXgAR4jBbJKxSajwOMqeNRZ/TpJfWO+iNo9USC+MJfatD\nB44B96u97D6SSZ+x67PWakwB99ExWebSqAqiU7eVmouFaYngXBA95adu2l7Z4eV4e7t99X87HZ/A\niomEm4FQr61kbn/I4gaIPoHIa4XN1rg9OYTD55jYHY5o8YS8mfu5wMWddxw/Xw9+40F5QGtKNuP4\nWs4REUaKERwFMX/yx36Lt921zftPmyqJU9IHzXjGw/LA3crLOZwGvhgiJkr3ONIg+gNf97q550un\nQYOAS4djbt2anxCYj8gw7o4Pjo8bRQyjhHZyxDVvfen5gdXESypKK8wKPrXVy2mi8wVk3vYdx05d\nb3kcyZZijKVcqtlOY2rAE0mg5BwDzZg21tWiLAXR68BVgjgGr563sE2kmGjLZmh1aSeHin06fA5e\n+3WwfQ8AkeXzNyZ/lZ/zs0RRpYnWfWp0vRSIjhOJKyKkZmcjf4PTHGUJJrB8YtLPSjQ6KDV4ZcDd\nzQGaEIZ7TLwNPvvELu+4ZweAYRDR9GbY6lkd/KiK1Eox7qrN3Hb+6LrqW92zsHMvjUtfUJVKO1k/\nXoWJtpKQCIeG4+ESFxIL/9Unn+ZnP/sct2+3+FonAFv1M+OLHdpNvLpyDts/LpUwco6de+Fr/uns\nc93GatpvWJ5YmO9P02x5pTZLlok215Qy0SvIOVLQVULO4bYzwGsYxclRcdFfJo75YS8Yp+NJ9ky5\njXr2gUmsrApLaaKPCBztoz8KwdUWqrU81UM+dz3inRbHrvvqUfZ3mxGh3Wa96TIMYiK7ocadukm4\n6PYWzQ3RSAFn8/91Fwvmvpax/NNMNOjvc8Wk35kgWr223fE4sgICfNKavF6N+gp/AOKGnOMEIi1a\nYbupxtLVTPS+exqAD0fv54fCP4/dPX38DTQrHA7K60vTanO2h9SlxoNYTZr/+OOP8fjVPr/4+Uup\nnMNpzgbRybgCiJaZnEO0Ntmgj6WTh0INaBuuPZPNA7C8Jr4IeWav/AAk0wWKi7B9mlZEEEvWZJ/Y\nK8F+2k3cpObgoz+jlh0psDG6XkhEmxUbTZeebKuFVRVmxkyUeSbayDna27B+iwK3WsZgdhYmBYu7\ninIOmSX5De01unFPMYgypuefg84ZuP9P8Gv3/W88Lc8VCpF4tpJzqJOvl2ovA7QKWMT+ppIziJzs\nYNrPfDrMDoqu/rks4nybqUNMAKMDnjxy+JZ/9RnCOOFwGLI/DDm3flyGFDjVbPWMN3UeRMs4yNxd\nOqdh+166A8W6NF/5vvRc2xLaDaSOnCMkxEkLPuU10Z9+Sn1Hz+wN2bAn6WKtpb/TwKpX9TIFjMeY\n6OeUrt6AyVlRl4nOg55lmuh8Lojxsl61wl0VJtrcfx0G3LSzrNrc+LCYOJ5noqtG2cWClFP3WXdB\nlPmpF/6eFeMeE1sBwqO8tKJmHYAjqeHb1CLlak85I221PTpiTOIqEA0wlPp+69xrOj7LjKiZedwo\nY6Ehx0RXTVafKqZVhYleBdCmUqvjz4lhok91fNbEiJGVG+9vWNy9fCMPaI3uzw2P4PACl9hBCPiM\nvJ+fiP8E3YZ7/A30OUkVfXLei1SD6EmUMJhE/ORvPcWtW02uDwIef/aSup7WlH5YD7TJpDzDZudk\nK6K1zZY4wp4UQfSicLwWDQKeuV7hQck5n+B4NEVENBnSZELS2Fx8LhA5TfyKtnrTSX4bnp50p+Uc\nM2K96XKEHtwrLFDSAU9PhpY0mmjNRK/fXGCibV06vgCiKzIHtgyJNaAdOut0k56ScgDf+9F99gYB\nfMO/46GN92BbAi8nwfEciwPDROd124tuMZZ4xEjNXiXNGd/fEiZa6GclLAmiIw1okxwTLZIQOT5k\nN24RRAlP7w74/EX17L3uluP9OHC6eHJSGghFM+QcxJFKKgS1ONlRLP+T3IJ/x9vSc21LMK5pcWdJ\nxUSjd6ZMsZWDT/0bvq3346yjJsuuNU77dtNV7GUgfEBWB7UpiJ7yie49r/rsoqjLRBdA9DLbrikm\nGlZI8jO/l2CiT0TOkfsuFrHRk17mzAFZhcaqeRlQ/j4NCHTymug6lQNzThSWsySx8JCxpb7PY9KK\nyu0GDMw4PdXmld6EM2sNzqw1aDPGanRTED1I9Pe5ChMNiz+rcFQkE+oy0SkxMzuBMo1pFxtQmKRk\n3smxSOsNhMcWf3s5JnpNDOmLXJs33DlevmHnE+70tranmeinw01ecTpjCWZ6/xoQXaHTWjngLrTd\nXBhLrg8C4kTyzW+/A9sSfPFppeX0joFo9beo4Dpg5xIorfY2mxzhThTDFZVghR2/SVOEPFuJic6A\nO7ZHw4oJdMlv0VwO3GO7lVVoKxtT0oqua0D0/nImuqWYaKDaIGQGVbeJFBYuEb5ra+C+o5jo8QEc\nXVIgSSduBlGuYmHFQdaROuEOGLnrrMleqkk7H+3wxUtqETAMYlqujchtP5rEQnVAOSY6TJJMWgHI\n5owFyRIm2tbfeVRywRkniSoUY7upftMjgvEBu6Fiex670uehiwqUv/bm430qTkFJuUVRbJJh9QIX\ntOSrrzL9FYi+D4BfbX1VYVvXWYGJtjQTje1hC8kk0G4dn/xRvtn5FX7O//tYJAXLS8NET4RmvqpK\nOgwbOV0N8PAirC0B0XWZ6LgkEx30MwABOVZ4hTLRdgmwaHTi5vj8+VVi0WLhk/8EPvVB9fvkKNND\nQ46Jrihjm25n0TWb41Imuu53mSNJpnczpmNyxNAymugQWde1AiAOOJKGfJiWc4w53fU5t96gI0a4\nzbUURPeTFZjowqJowX2Goww4Q7YQrAyip5lo/fflL8CPvxWO9M6YAa75XZvGWn0Qnb+3KXmOYaK3\n2z4dhgxEnoluq+Pr5i28RHEDRJ9AFJhor0uCoBNchdF1nphsFNittVkgWg+AVbxo81USDRMdRIla\noQO3brV48x2bCL0F1O1OgVzDeFbY8svkHD6ivYMjEtYHTwEQNE8tOFNfs9tk3Y14eq/CajNlKtTW\nuC9CJkeK+bTay/V+E2+DJuNqE+cUE73mJmoAmhwWNdEzYqPl0jMMR0nQVWjT9sD28QkV8zvYU1X1\n1m5R/3/1YWisKYCNsbirp4nOyznGzgbrHMH+MyRYXJLbPHpZTcKjIC5IOUCBvQMqMtFJDlxCoSy8\nKR6zjIm229qHfFTSncMAWstN+/yOOEREYy4FBkQf8fnnDrl9u6VcWKYi8fXzW9oRRHlTFxMLw5yc\n4wzc/mX8o9Zf43dP/eni/a0IoiPhpNrYKFB9fhQo1vBOcZmbxJ5aVHpFOcfIKBOrbhnn5RxJmEkl\neiVA9AvORE9rolcAtPmFdRl3jpOwuFsEuh769/DAh9Tv42km2mii6zDRJa388uMV1HfnSHN7tJON\nYbjDsfLHf/gXsmPHPYZaox3GklHiAKJ6H5ISEQcMTJ/PMdG9ccjlwzFn1hrctNGgK8b47XXWW+o5\n7sUrMtGmwMtSJjoHousuFtJ5bErOcfEBuPYIPPZfVd8ZHRSPA81EV5i/TCSJGgfM4lXf5zN7A/7+\nf/kCl3tj1hoOnmPRkQOVP2TC9Ns6LkEvYdwA0ScQIueUgWUxoMWZkSoJfTHZ5u7T2QpvppxDg2gn\n6JEk5fR6dp6JdjIQ3RuM+VH3n3Dr9U/xw1/7Or73ned0G0WLOwMonLD8QFuQc7RVMtZ2/3HGeOk2\n+8JwGnTsuJImWuRlK46PLyKigWI+nfZiVhgg8DXoLQn0gGzy0A9110lAlxlfJudYa+aZ6HKSAyDH\nyLgk/hprDGhZgRpQWluKiQYFov1uWoikwERXnMQcwtRubuJtsiGVnKPn7hDi8IgG0cMZIFoIwcRu\nEwu79GcbxTqx0Gxx5z7LyWnt3LCEifYaXSJpkZQE0bEuNS4tVy1GgDuEklVcmihQ9fjVIx567nAm\nCw1kLF9JEJ1WSbQzCYmVBMijy6pP+coZ49+Ov4ydzeIOkWNZjGVNOUcSEpHpsEPdj5vBdS7Yypv6\nTnEJPxml40EzBdE1y43HQbpLlP497qkF5DI5R11Hh/w5s673yu/DR75Pa4WnNNHT55duMwdIyvhE\nTxdbWRVET4Ou8SHsPalen/TmMNErguhF1zwNoutWScwz2vmqlwfPKH/8f/8X4ZFfUouzyRFHOeay\nN47rVRDUY+1I+iSIdFH03P6QL/2BX+H5wzHn1ht857vv4c41ifA7KRPdizQJVlcTbRY7ixZi4XAO\niK6b2zPlzjHQlZMf/WXFSH/8H6i/T0LOMccR5Nceucq//u1n+MTj19jpqmejJYf0ZP4+TVGZGyD6\nZRdWkgEggCPR4dbxo4Cytzuz5tPWk9VMOYftENhtOgxLl+G28sDd8WjoEtzx7hP8Sfu3ue+/fRd3\nJhe4u60HtmmQ67ZIsHCiAbJkoo1jKtzZrpIYADvDJ7kmN2l6JbwS3AZtK+TZ68PSbVLQm2+wzkDJ\nKgCvu5yJNpIBaQaOMpFzPglxaNlxBhRLaaL1YFBJE51tFyeNTTZFX2mUQcs5NCAZ7oK/hueoR3cS\nJVl54aqlqaX2MkZ5NjdECNce5ppzFiBloocz/JMBPMdmZK9XYqI9ItBt2vqz3JVryK279ZsuBtEN\nz6FPE1nys830yS64LSLLT0G0Wex8+ql9Lh6MZuqhVaP69ZI7C1GS4BLrZ1NNEg0CZP8KdE7z/MGI\nH//1JziYkchoWYIRXq1J2paaiTbuJ8EEkoRO0uPJxmsAA6KHOSZaPbcjNBiqOoFFEwUU8yDa2D4u\nZaLrVklcwkT/6g8qlnb/6SK7tqKGFkvZQS5louPwOBO9ioRk+neA0aFytdh7Qo0zhcRXqy+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pXT0KZtGncXRw20zd0vwObtc0/xHDsD0RUH2UzmoACt6/ocyDbj9k0kWKw1XG7batHXC6xZTHTT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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11a534f28>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "pred_y = model.predict(test_x, batch_size=256)\n",
    "plt.figure(figsize=(12,5))\n",
    "plt.plot(test_data.time, y_scaler.inverse_transform(test_data.cnt), label='Actual')\n",
    "plt.plot(test_data.time, y_scaler.inverse_transform(pred_y), label='Prediction')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r",
      "256/504 [==============>...............] - ETA: 0s"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0.31514962041188799"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.evaluate(test_x, test_data['cnt'], batch_size=256)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/sachin/anaconda/lib/python3.5/site-packages/ipykernel/__main__.py:7: UserWarning: The `Merge` layer is deprecated and will be removed after 08/2017. Use instead layers from `keras.layers.merge`, e.g. `add`, `concatenate`, etc.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/10\n",
      "16875/16875 [==============================] - 0s - loss: 0.9017     \n",
      "Epoch 2/10\n",
      "16875/16875 [==============================] - 0s - loss: 0.5516     \n",
      "Epoch 3/10\n",
      "16875/16875 [==============================] - 0s - loss: 0.4284     \n",
      "Epoch 4/10\n",
      "16875/16875 [==============================] - 0s - loss: 0.3578     \n",
      "Epoch 5/10\n",
      "16875/16875 [==============================] - 0s - loss: 0.3112     \n",
      "Epoch 6/10\n",
      "16875/16875 [==============================] - 0s - loss: 0.2783     \n",
      "Epoch 7/10\n",
      "16875/16875 [==============================] - 0s - loss: 0.2499     \n",
      "Epoch 8/10\n",
      "16875/16875 [==============================] - 0s - loss: 0.2251     \n",
      "Epoch 9/10\n",
      "16875/16875 [==============================] - 0s - loss: 0.2033     \n",
      "Epoch 10/10\n",
      "16875/16875 [==============================] - 0s - loss: 0.1821     \n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.callbacks.History at 0x11fcfcd30>"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "quant_model = Sequential()\n",
    "quant_model.add(Dense(len(quant_features),input_dim=len(quant_features)))\n",
    "model.add(BatchNormalization())\n",
    "quant_model.add(LeakyReLU())\n",
    "\n",
    "model = Sequential()\n",
    "model.add(Merge([cat_model, quant_model], mode='concat'))\n",
    "model.add(Dense(15))\n",
    "model.add(BatchNormalization())\n",
    "model.add(LeakyReLU())\n",
    "model.add(Dense(1))\n",
    "model.compile(optimizer='adadelta', loss='mse')\n",
    "\n",
    "model.fit(train_x, train_data['cnt'], batch_size=256)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r",
      "256/504 [==============>...............] - ETA: 0s"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0.22712602596434336"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.evaluate(test_x, test_data['cnt'], batch_size=256)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "anaconda-cloud": {},
  "kernelspec": {
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