fixed-time-horizon-prediction/Exp_1.2_SiouxFalls_2hour.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import datetime\n",
    "import glob\n",
    "import os.path\n",
    "from pandas.compat import StringIO"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### NREL Bird Model implementation: for obtaining clear sky GHI"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import itertools\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "sns.set_color_codes()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pvlib\n",
    "from pvlib import clearsky, atmosphere\n",
    "from pvlib.location import Location"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "sif = Location(43.544,-96.73, 'US/Central', 448.086, 'Sioux Falls')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "times2009 = pd.DatetimeIndex(start='2009-01-01', end='2010-01-01', freq='1min',\n",
    "                        tz=sif.tz)   # 12 months of 2009 - For testing\n",
    "times2010and2011 = pd.DatetimeIndex(start='2010-01-01', end='2012-01-01', freq='1min',\n",
    "                        tz=sif.tz)   # 24 months of 2010 and 2011 - For training"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "cs_2009 = sif.get_clearsky(times2009) \n",
    "cs_2010and2011 = sif.get_clearsky(times2010and2011) # ineichen with climatology table by default\n",
    "#cs_2011 = bvl.get_clearsky(times2011) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "cs_2009.drop(['dni','dhi'],axis=1, inplace=True) #updating the same dataframe by dropping two columns\n",
    "cs_2010and2011.drop(['dni','dhi'],axis=1, inplace=True) #updating the same dataframe by dropping two columns\n",
    "#cs_2011.drop(['dni','dhi'],axis=1, inplace=True) #updating the same dataframe by dropping two columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "cs_2009.reset_index(inplace=True)\n",
    "cs_2010and2011.reset_index(inplace=True)\n",
    "#cs_2011.reset_index(inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "cs_2009['index']=cs_2009['index'].apply(lambda x:x.to_datetime())\n",
    "cs_2009['year'] = cs_2009['index'].apply(lambda x:x.year)\n",
    "cs_2009['month'] = cs_2009['index'].apply(lambda x:x.month)\n",
    "cs_2009['day'] = cs_2009['index'].apply(lambda x:x.day)\n",
    "cs_2009['hour'] = cs_2009['index'].apply(lambda x:x.hour)\n",
    "cs_2009['min'] = cs_2009['index'].apply(lambda x:x.minute)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "cs_2010and2011['index']=cs_2010and2011['index'].apply(lambda x:x.to_datetime())\n",
    "cs_2010and2011['year'] = cs_2010and2011['index'].apply(lambda x:x.year)\n",
    "cs_2010and2011['month'] = cs_2010and2011['index'].apply(lambda x:x.month)\n",
    "cs_2010and2011['day'] = cs_2010and2011['index'].apply(lambda x:x.day)\n",
    "cs_2010and2011['hour'] = cs_2010and2011['index'].apply(lambda x:x.hour)\n",
    "cs_2010and2011['min'] = cs_2010and2011['index'].apply(lambda x:x.minute)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(525601, 7)\n",
      "(1051201, 7)\n"
     ]
    }
   ],
   "source": [
    "print(cs_2009.shape)\n",
    "print(cs_2010and2011.shape)\n",
    "#print(cs_2011.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "cs_2009.drop(cs_2009.index[-1], inplace=True)\n",
    "cs_2010and2011.drop(cs_2010and2011.index[-1], inplace=True)\n",
    "#cs_2011.drop(cs_2011.index[-1], inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(525600, 7)\n",
      "(1051200, 7)\n"
     ]
    }
   ],
   "source": [
    "print(cs_2009.shape)\n",
    "print(cs_2010and2011.shape)\n",
    "#print(cs_2011.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>index</th>\n",
       "      <th>ghi</th>\n",
       "      <th>year</th>\n",
       "      <th>month</th>\n",
       "      <th>day</th>\n",
       "      <th>hour</th>\n",
       "      <th>min</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2010-01-01 00:00:00-06:00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2010</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2010-01-01 00:01:00-06:00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2010</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2010-01-01 00:02:00-06:00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2010</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2010-01-01 00:03:00-06:00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2010</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2010-01-01 00:04:00-06:00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2010</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                      index  ghi  year  month  day  hour  min\n",
       "0 2010-01-01 00:00:00-06:00  0.0  2010      1    1     0    0\n",
       "1 2010-01-01 00:01:00-06:00  0.0  2010      1    1     0    1\n",
       "2 2010-01-01 00:02:00-06:00  0.0  2010      1    1     0    2\n",
       "3 2010-01-01 00:03:00-06:00  0.0  2010      1    1     0    3\n",
       "4 2010-01-01 00:04:00-06:00  0.0  2010      1    1     0    4"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cs_2010and2011.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Import files from each year in a separate dataframe"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "\n",
    "- year            integer\t year, i.e., 1995\n",
    "- jday            integer\t Julian day (1 through 365 [or 366])\n",
    "- month           integer\t number of the month (1-12)\n",
    "- day             integer\t day of the month(1-31)\n",
    "- hour            integer\t hour of the day (0-23)\n",
    "- min             integer\t minute of the hour (0-59)\n",
    "- dt              real\t decimal time (hour.decimalminutes, e.g., 23.5 = 2330)\n",
    "- zen             real\t solar zenith angle (degrees)\n",
    "- dw_solar        real\t downwelling global solar (Watts m^-2)\n",
    "- uw_solar        real\t upwelling global solar (Watts m^-2)\n",
    "- direct_n        real\t direct-normal solar (Watts m^-2)\n",
    "- diffuse         real\t downwelling diffuse solar (Watts m^-2)\n",
    "- dw_ir           real\t downwelling thermal infrared (Watts m^-2)\n",
    "- dw_casetemp     real\t downwelling IR case temp. (K)\n",
    "- dw_dometemp     real\t downwelling IR dome temp. (K)\n",
    "- uw_ir           real\t upwelling thermal infrared (Watts m^-2)\n",
    "- uw_casetemp     real\t upwelling IR case temp. (K)\n",
    "- uw_dometemp     real\t upwelling IR dome temp. (K)\n",
    "- uvb             real\t global UVB (milliWatts m^-2)\n",
    "- par             real\t photosynthetically active radiation (Watts m^-2)\n",
    "- netsolar        real\t net solar (dw_solar - uw_solar) (Watts m^-2)\n",
    "- netir           real\t net infrared (dw_ir - uw_ir) (Watts m^-2)\n",
    "- totalnet        real\t net radiation (netsolar+netir) (Watts m^-2)\n",
    "- temp            real\t 10-meter air temperature (?C)\n",
    "- rh              real\t relative humidity (%)\n",
    "- windspd         real\t wind speed (ms^-1)\n",
    "- winddir         real\t wind direction (degrees, clockwise from north)\n",
    "- pressure        real\t station pressure (mb)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "cols = ['year', 'jday', 'month', 'day','hour','min','dt','zen','dw_solar','dw_solar_QC','uw_solar',\n",
    "       'uw_solar_QC', 'direct_n','direct_n_QC','diffuse', 'diffuse_QC', 'dw_ir', 'dw_ir_QC', 'dw_casetemp',\n",
    "       'dw_casetemp_QC', 'dw_dometemp','dw_dometemp_QC','uw_ir', 'uw_ir_QC', 'uw_casetemp','uw_casetemp_QC',\n",
    "       'uw_dometemp','uw_dometemp_QC','uvb','uvb_QC','par','par_QC','netsolar','netsolar_QC','netir','netir_QC',\n",
    "       'totalnet','totalnet_QC','temp','temp_QC','rh','rh_QC','windspd','windspd_QC','winddir','winddir_QC',\n",
    "       'pressure','pressure_QC']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1049906, 48)"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "path = r'.\\\\data\\\\Sioux_Falls\\\\Exp_1_train'\n",
    "all_files = glob.glob(path + \"/*.dat\")\n",
    "all_files.sort()\n",
    "\n",
    "df_big_train = pd.concat([pd.read_csv(f, skipinitialspace = True, quotechar = '\"',skiprows=(2),delimiter=' ', \n",
    "                 index_col=False,header=None, names=cols) for f in all_files],ignore_index=True)\n",
    "df_big_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(524982, 48)"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "path = r'.\\\\data\\\\Sioux_Falls\\\\Exp_1_test'\n",
    "all_files = glob.glob(path + \"/*.dat\")\n",
    "all_files.sort()\n",
    "\n",
    "df_big_test = pd.concat((pd.read_csv(f, skipinitialspace = True, quotechar = '\"',skiprows=(2),delimiter=' ', \n",
    "                 index_col=False,header=None, names=cols) for f in all_files),ignore_index=True)\n",
    "df_big_test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(585, 48)"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_big_test[df_big_test['dw_solar']==-9999.9].shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Merging Clear Sky GHI And the big dataframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1049906, 50)"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_train = pd.merge(df_big_train, cs_2010and2011, on=['year','month','day','hour','min'])\n",
    "df_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(524982, 50)"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_test = pd.merge(df_big_test, cs_2009, on=['year','month','day','hour','min'])\n",
    "df_test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_train.drop(['index'],axis=1, inplace=True) #updating the same dataframe by dropping the index columns from clear sky model\n",
    "df_test.drop(['index'], axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1049906, 49)"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_train.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Managing missing values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Resetting index\n",
    "df_train.reset_index(drop=True, inplace=True)\n",
    "df_test.reset_index(drop=True, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Dropping rows with two or more -9999.9 values in columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1048493, 49)"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Step1: Get indices of all rows with 2 or more -999\n",
    "missing_data_indices = np.where((df_train <=-9999.9).apply(sum, axis=1)>=2)[0]\n",
    "# Step2: Drop those indices\n",
    "df_train.drop(missing_data_indices, axis=0, inplace=True)\n",
    "# Checking that the rows are dropped\n",
    "df_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(524310, 49)"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "missing_data_indices_test = np.where((df_test <= -9999.9).apply(sum, axis=1)>=2)[0]\n",
    "df_test.drop(missing_data_indices_test, axis=0, inplace=True)\n",
    "df_test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "# For the rows with only one cell as -9999.9, replacing this cell with the mean of the column"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### First resetting index after dropping rows in the previous part of the code"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 2nd time - Reseting Index\n",
    "df_train.reset_index(drop=True, inplace=True)\n",
    "df_test.reset_index(drop=True, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "one_miss_train_idx = np.where((df_train <=-9999.9).apply(sum, axis=1)==1)[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "6872"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(one_miss_train_idx)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1048493, 49)"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\s269596\\AppData\\Local\\Continuum\\Anaconda3\\lib\\site-packages\\pandas\\core\\indexing.py:194: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  self._setitem_with_indexer(indexer, value)\n"
     ]
    }
   ],
   "source": [
    "col_names = df_train.columns\n",
    "from collections import defaultdict\n",
    "stats = defaultdict(int)\n",
    "total_single_missing_values = 0\n",
    "for name in col_names:\n",
    "    col_mean = df_train[~(df_train[name] == -9999.9)][name].mean()\n",
    "    missing_indices = np.where((df_train[name] == -9999.9))\n",
    "    stats[name] = len(missing_indices[0])\n",
    "    df_train[name].loc[missing_indices] = col_mean\n",
    "    total_single_missing_values += sum(df_train[name] == -9999.9)\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_col_min = df_train.apply(min, axis=0)\n",
    "df_col_max = df_train.apply(max, axis =0)\n",
    "#print(df_col_min, df_col_max)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "train = np.where((df_train <=-9999.9).apply(sum, axis=1)==1)[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "# doing the same thing on test dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1576"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "one_miss_test_idx = np.where((df_test <=-9999.9).apply(sum, axis=1)==1)[0]\n",
    "len(one_miss_test_idx)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\s269596\\AppData\\Local\\Continuum\\Anaconda3\\lib\\site-packages\\pandas\\core\\indexing.py:194: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  self._setitem_with_indexer(indexer, value)\n"
     ]
    }
   ],
   "source": [
    "col_names_test = df_test.columns\n",
    "from collections import defaultdict\n",
    "stats_test = defaultdict(int)\n",
    "total_single_missing_values_test = 0\n",
    "for name in col_names_test:\n",
    "    col_mean = df_test[~(df_test[name] == -9999.9)][name].mean()\n",
    "    missing_indices = np.where((df_test[name] == -9999.9))\n",
    "    stats_test[name] = len(missing_indices[0])\n",
    "    df_test[name].loc[missing_indices] = col_mean\n",
    "    total_single_missing_values_test += sum(df_test[name] == -9999.9)\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "test = np.where((df_test <=-9999.9).apply(sum, axis=1)==1)[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1048493, 49)"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(524310, 49)"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_test.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Exploratory Data Analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "dw_solar_everyday = df_test.groupby(['jday'])['dw_solar'].mean()\n",
    "ghi_everyday = df_test.groupby(['jday'])['ghi'].mean()\n",
    "j_day = df_test['jday'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAY4AAAEgCAYAAACjEpTiAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\ncIQSHAqFQqFwxP8DOob1BZgzGgIAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure()\n",
    "\n",
    "axes1 = fig.add_axes([0.1,0.1,0.8,0.8])\n",
    "#axes2 = fig.add_axes([0.1,0.1,0.8,0.8])\n",
    "\n",
    "axes1.scatter(j_day,dw_solar_everyday,label='Observed dw_solar',color='red')\n",
    "axes1.scatter(j_day, ghi_everyday, label='Clear Sky GHI',color='green')\n",
    "\n",
    "axes1.set_xlabel('Days')\n",
    "axes1.set_ylabel('Solar Irradiance (Watts /m^2)')\n",
    "axes1.set_title('Solar Irradiance - Test Year 2009')\n",
    "axes1.legend(loc='best')\n",
    "\n",
    "fig.savefig('Figure2a.png', bbox_inches = 'tight')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\s269596\\AppData\\Local\\Continuum\\Anaconda3\\lib\\site-packages\\matplotlib\\axes\\_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n",
      "  warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAawAAAGpCAYAAADRBQIfAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nYCUSiUSiLPgPlxFzXFJAU3UAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x432 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.jointplot(x=dw_solar_everyday,y=ghi_everyday,kind='reg')\n",
    "#plt.title('observed dw_solar vs clear sky ghi')\n",
    "plt.xlabel('Observed global downwelling solar (Watts/m^2)')\n",
    "plt.ylabel('Clear Sky GHI (Watts/m^2)')\n",
    "plt.savefig('Figure3a.png', bbox_inches='tight')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### making the Kt (clear sky index at time t) column by first removing rows with ghi==0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_train = df_train[df_train['ghi']!=0]\n",
    "df_test = df_test[df_test['ghi']!=0]\n",
    "df_train['Kt'] = df_train['dw_solar']/df_train['ghi']\n",
    "df_test['Kt'] = df_test['dw_solar']/df_test['ghi']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_train.reset_index(inplace=True)\n",
    "df_test.reset_index(inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "test Kt max: 16815.23562858055\n",
      "test Kt min: -59.82928662131299\n",
      "test Kt mean: 2.5854594971725517\n",
      "\n",
      "\n",
      "train Kt max: 35403.71260380253\n",
      "train Kt min: -1080.8061639132102\n",
      "train Kt mean: 2.849421609595172\n"
     ]
    }
   ],
   "source": [
    "print(\"test Kt max: \"+str(df_test['Kt'].max()))\n",
    "print(\"test Kt min: \"+str(df_test['Kt'].min()))\n",
    "print(\"test Kt mean: \"+str(df_test['Kt'].mean()))\n",
    "print(\"\\n\")\n",
    "print(\"train Kt max: \"+str(df_train['Kt'].max()))\n",
    "print(\"train Kt min: \"+str(df_train['Kt'].min()))\n",
    "print(\"train Kt mean: \"+str(df_train['Kt'].mean()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0xce340b8>]"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAX8AAAD3CAYAAAD10FRmAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nkQBS+IuIBJDCX0QkgBT+IiIB9P8B8A+REn1VHc0AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(df_train['Kt'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0xce06780>]"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAX8AAAD5CAYAAADP2jUWAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(df_test['Kt'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_train= df_train[df_train['Kt']< 5000]\n",
    "df_train= df_train[df_train['Kt']> -1000]\n",
    "df_test= df_test[df_test['Kt']< 5000]\n",
    "df_test= df_test[df_test['Kt']> -1000]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Group the data (train dataframe)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "zen = df_train.groupby(['year','month','day','hour'])['zen'].mean()\n",
    "dw_solar = df_train.groupby(['year','month','day','hour'])['dw_solar'].mean()\n",
    "uw_solar = df_train.groupby(['year','month','day','hour'])['uw_solar'].mean()\n",
    "direct_n = df_train.groupby(['year','month','day','hour'])['direct_n'].mean()\n",
    "diffuse = df_train.groupby(['year','month','day','hour'])['diffuse'].mean()\n",
    "dw_ir = df_train.groupby(['year','month','day','hour'])['dw_ir'].mean()\n",
    "dw_casetemp = df_train.groupby(['year','month','day','hour'])['dw_casetemp'].mean()\n",
    "dw_dometemp = df_train.groupby(['year','month','day','hour'])['dw_dometemp'].mean()\n",
    "uw_ir = df_train.groupby(['year','month','day','hour'])['uw_ir'].mean()\n",
    "uw_casetemp = df_train.groupby(['year','month','day','hour'])['uw_casetemp'].mean()\n",
    "uw_dometemp = df_train.groupby(['year','month','day','hour'])['uw_dometemp'].mean()\n",
    "uvb = df_train.groupby(['year','month','day','hour'])['uvb'].mean()\n",
    "par = df_train.groupby(['year','month','day','hour'])['par'].mean()\n",
    "netsolar = df_train.groupby(['year','month','day','hour'])['netsolar'].mean()\n",
    "netir = df_train.groupby(['year','month','day','hour'])['netir'].mean()\n",
    "totalnet = df_train.groupby(['year','month','day','hour'])['totalnet'].mean()\n",
    "temp = df_train.groupby(['year','month','day','hour'])['temp'].mean()\n",
    "rh = df_train.groupby(['year','month','day','hour'])['rh'].mean()\n",
    "windspd = df_train.groupby(['year','month','day','hour'])['windspd'].mean()\n",
    "winddir = df_train.groupby(['year','month','day','hour'])['winddir'].mean()\n",
    "pressure = df_train.groupby(['year','month','day','hour'])['pressure'].mean()\n",
    "ghi = df_train.groupby(['year','month','day','hour'])['ghi'].mean()\n",
    "Kt = df_train.groupby(['year','month','day','hour'])['Kt'].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_new_train = pd.concat([zen,dw_solar,uw_solar,direct_n,diffuse,dw_ir,dw_casetemp,dw_dometemp,uw_ir,uw_casetemp,uw_dometemp,\n",
    "                    uvb,par,netsolar,netir,totalnet,temp,rh,windspd,winddir,pressure,ghi,Kt], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>zen</th>\n",
       "      <th>dw_solar</th>\n",
       "      <th>uw_solar</th>\n",
       "      <th>direct_n</th>\n",
       "      <th>diffuse</th>\n",
       "      <th>dw_ir</th>\n",
       "      <th>dw_casetemp</th>\n",
       "      <th>dw_dometemp</th>\n",
       "      <th>uw_ir</th>\n",
       "      <th>uw_casetemp</th>\n",
       "      <th>...</th>\n",
       "      <th>netsolar</th>\n",
       "      <th>netir</th>\n",
       "      <th>totalnet</th>\n",
       "      <th>temp</th>\n",
       "      <th>rh</th>\n",
       "      <th>windspd</th>\n",
       "      <th>winddir</th>\n",
       "      <th>pressure</th>\n",
       "      <th>ghi</th>\n",
       "      <th>Kt</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>year</th>\n",
       "      <th>month</th>\n",
       "      <th>day</th>\n",
       "      <th>hour</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">2010.0</th>\n",
       "      <th rowspan=\"5\" valign=\"top\">1.0</th>\n",
       "      <th rowspan=\"5\" valign=\"top\">1.0</th>\n",
       "      <th>8.0</th>\n",
       "      <td>147.640000</td>\n",
       "      <td>-3.319298</td>\n",
       "      <td>-0.385965</td>\n",
       "      <td>2.254386</td>\n",
       "      <td>0.217544</td>\n",
       "      <td>240.949123</td>\n",
       "      <td>257.463158</td>\n",
       "      <td>257.122807</td>\n",
       "      <td>241.624561</td>\n",
       "      <td>255.159649</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-1.271930</td>\n",
       "      <td>-1.271930</td>\n",
       "      <td>-17.843860</td>\n",
       "      <td>86.363158</td>\n",
       "      <td>3.459649</td>\n",
       "      <td>322.194737</td>\n",
       "      <td>971.714035</td>\n",
       "      <td>56.746103</td>\n",
       "      <td>-0.069423</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9.0</th>\n",
       "      <td>137.909167</td>\n",
       "      <td>-3.186667</td>\n",
       "      <td>-0.033333</td>\n",
       "      <td>1.803333</td>\n",
       "      <td>0.180000</td>\n",
       "      <td>245.716667</td>\n",
       "      <td>258.640000</td>\n",
       "      <td>258.251667</td>\n",
       "      <td>245.268333</td>\n",
       "      <td>256.210000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.618333</td>\n",
       "      <td>-0.618333</td>\n",
       "      <td>-16.991667</td>\n",
       "      <td>84.353333</td>\n",
       "      <td>3.388333</td>\n",
       "      <td>340.455000</td>\n",
       "      <td>972.018333</td>\n",
       "      <td>173.258555</td>\n",
       "      <td>-0.019518</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10.0</th>\n",
       "      <td>127.232667</td>\n",
       "      <td>-3.213333</td>\n",
       "      <td>0.013333</td>\n",
       "      <td>1.315000</td>\n",
       "      <td>0.186667</td>\n",
       "      <td>234.545000</td>\n",
       "      <td>258.533333</td>\n",
       "      <td>258.103333</td>\n",
       "      <td>243.210000</td>\n",
       "      <td>256.148333</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-8.660000</td>\n",
       "      <td>-8.660000</td>\n",
       "      <td>-17.196667</td>\n",
       "      <td>84.298333</td>\n",
       "      <td>3.696667</td>\n",
       "      <td>342.185000</td>\n",
       "      <td>972.093333</td>\n",
       "      <td>291.141970</td>\n",
       "      <td>-0.011144</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11.0</th>\n",
       "      <td>116.433167</td>\n",
       "      <td>-3.265000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.536667</td>\n",
       "      <td>0.241667</td>\n",
       "      <td>225.940000</td>\n",
       "      <td>258.170000</td>\n",
       "      <td>257.753333</td>\n",
       "      <td>240.096667</td>\n",
       "      <td>255.845000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-14.153333</td>\n",
       "      <td>-14.153333</td>\n",
       "      <td>-17.428333</td>\n",
       "      <td>84.266667</td>\n",
       "      <td>3.226667</td>\n",
       "      <td>335.816667</td>\n",
       "      <td>972.341667</td>\n",
       "      <td>369.552873</td>\n",
       "      <td>-0.008872</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12.0</th>\n",
       "      <td>105.860667</td>\n",
       "      <td>-3.126667</td>\n",
       "      <td>-0.001667</td>\n",
       "      <td>1.645000</td>\n",
       "      <td>0.226667</td>\n",
       "      <td>240.656667</td>\n",
       "      <td>258.306667</td>\n",
       "      <td>257.915000</td>\n",
       "      <td>243.390000</td>\n",
       "      <td>255.910000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-2.736667</td>\n",
       "      <td>-2.736667</td>\n",
       "      <td>-17.298333</td>\n",
       "      <td>84.818333</td>\n",
       "      <td>3.295000</td>\n",
       "      <td>331.678333</td>\n",
       "      <td>972.923333</td>\n",
       "      <td>397.404717</td>\n",
       "      <td>-0.007868</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 23 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                              zen  dw_solar  uw_solar  direct_n   diffuse  \\\n",
       "year   month day hour                                                       \n",
       "2010.0 1.0   1.0 8.0   147.640000 -3.319298 -0.385965  2.254386  0.217544   \n",
       "                 9.0   137.909167 -3.186667 -0.033333  1.803333  0.180000   \n",
       "                 10.0  127.232667 -3.213333  0.013333  1.315000  0.186667   \n",
       "                 11.0  116.433167 -3.265000  0.000000  1.536667  0.241667   \n",
       "                 12.0  105.860667 -3.126667 -0.001667  1.645000  0.226667   \n",
       "\n",
       "                            dw_ir  dw_casetemp  dw_dometemp       uw_ir  \\\n",
       "year   month day hour                                                     \n",
       "2010.0 1.0   1.0 8.0   240.949123   257.463158   257.122807  241.624561   \n",
       "                 9.0   245.716667   258.640000   258.251667  245.268333   \n",
       "                 10.0  234.545000   258.533333   258.103333  243.210000   \n",
       "                 11.0  225.940000   258.170000   257.753333  240.096667   \n",
       "                 12.0  240.656667   258.306667   257.915000  243.390000   \n",
       "\n",
       "                       uw_casetemp    ...     netsolar      netir   totalnet  \\\n",
       "year   month day hour                 ...                                      \n",
       "2010.0 1.0   1.0 8.0    255.159649    ...          0.0  -1.271930  -1.271930   \n",
       "                 9.0    256.210000    ...          0.0  -0.618333  -0.618333   \n",
       "                 10.0   256.148333    ...          0.0  -8.660000  -8.660000   \n",
       "                 11.0   255.845000    ...          0.0 -14.153333 -14.153333   \n",
       "                 12.0   255.910000    ...          0.0  -2.736667  -2.736667   \n",
       "\n",
       "                            temp         rh   windspd     winddir    pressure  \\\n",
       "year   month day hour                                                           \n",
       "2010.0 1.0   1.0 8.0  -17.843860  86.363158  3.459649  322.194737  971.714035   \n",
       "                 9.0  -16.991667  84.353333  3.388333  340.455000  972.018333   \n",
       "                 10.0 -17.196667  84.298333  3.696667  342.185000  972.093333   \n",
       "                 11.0 -17.428333  84.266667  3.226667  335.816667  972.341667   \n",
       "                 12.0 -17.298333  84.818333  3.295000  331.678333  972.923333   \n",
       "\n",
       "                              ghi        Kt  \n",
       "year   month day hour                        \n",
       "2010.0 1.0   1.0 8.0    56.746103 -0.069423  \n",
       "                 9.0   173.258555 -0.019518  \n",
       "                 10.0  291.141970 -0.011144  \n",
       "                 11.0  369.552873 -0.008872  \n",
       "                 12.0  397.404717 -0.007868  \n",
       "\n",
       "[5 rows x 23 columns]"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_new_train.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Groupdata - test dataframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [],
   "source": [
    "test_zen = df_test.groupby(['month','day','hour'])['zen'].mean()\n",
    "test_dw_solar = df_test.groupby(['month','day','hour'])['dw_solar'].mean()\n",
    "test_uw_solar = df_test.groupby(['month','day','hour'])['uw_solar'].mean()\n",
    "test_direct_n = df_test.groupby(['month','day','hour'])['direct_n'].mean()\n",
    "test_diffuse = df_test.groupby(['month','day','hour'])['diffuse'].mean()\n",
    "test_dw_ir = df_test.groupby(['month','day','hour'])['dw_ir'].mean()\n",
    "test_dw_casetemp = df_test.groupby(['month','day','hour'])['dw_casetemp'].mean()\n",
    "test_dw_dometemp = df_test.groupby(['month','day','hour'])['dw_dometemp'].mean()\n",
    "test_uw_ir = df_test.groupby(['month','day','hour'])['uw_ir'].mean()\n",
    "test_uw_casetemp = df_test.groupby(['month','day','hour'])['uw_casetemp'].mean()\n",
    "test_uw_dometemp = df_test.groupby(['month','day','hour'])['uw_dometemp'].mean()\n",
    "test_uvb = df_test.groupby(['month','day','hour'])['uvb'].mean()\n",
    "test_par = df_test.groupby(['month','day','hour'])['par'].mean()\n",
    "test_netsolar = df_test.groupby(['month','day','hour'])['netsolar'].mean()\n",
    "test_netir = df_test.groupby(['month','day','hour'])['netir'].mean()\n",
    "test_totalnet = df_test.groupby(['month','day','hour'])['totalnet'].mean()\n",
    "test_temp = df_test.groupby(['month','day','hour'])['temp'].mean()\n",
    "test_rh = df_test.groupby(['month','day','hour'])['rh'].mean()\n",
    "test_windspd = df_test.groupby(['month','day','hour'])['windspd'].mean()\n",
    "test_winddir = df_test.groupby(['month','day','hour'])['winddir'].mean()\n",
    "test_pressure = df_test.groupby(['month','day','hour'])['pressure'].mean()\n",
    "test_ghi = df_test.groupby(['month','day','hour'])['ghi'].mean()\n",
    "test_Kt = df_test.groupby(['month','day','hour'])['Kt'].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_new_test = pd.concat([test_zen,test_dw_solar,test_uw_solar,test_direct_n,test_diffuse,test_dw_ir,\n",
    "                         test_dw_casetemp,test_dw_dometemp,test_uw_ir,test_uw_casetemp,test_uw_dometemp,\n",
    "                    test_uvb,test_par,test_netsolar,test_netir,test_totalnet,test_temp,test_rh,\n",
    "                         test_windspd,test_winddir,test_pressure,test_ghi,test_Kt], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>zen</th>\n",
       "      <th>dw_solar</th>\n",
       "      <th>uw_solar</th>\n",
       "      <th>direct_n</th>\n",
       "      <th>diffuse</th>\n",
       "      <th>dw_ir</th>\n",
       "      <th>dw_casetemp</th>\n",
       "      <th>dw_dometemp</th>\n",
       "      <th>uw_ir</th>\n",
       "      <th>uw_casetemp</th>\n",
       "      <th>...</th>\n",
       "      <th>netsolar</th>\n",
       "      <th>netir</th>\n",
       "      <th>totalnet</th>\n",
       "      <th>temp</th>\n",
       "      <th>rh</th>\n",
       "      <th>windspd</th>\n",
       "      <th>winddir</th>\n",
       "      <th>pressure</th>\n",
       "      <th>ghi</th>\n",
       "      <th>Kt</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>hour</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>7.0</th>\n",
       "      <td>145.536667</td>\n",
       "      <td>-5.036111</td>\n",
       "      <td>-0.005556</td>\n",
       "      <td>1.050000</td>\n",
       "      <td>-1.691667</td>\n",
       "      <td>225.650000</td>\n",
       "      <td>271.297222</td>\n",
       "      <td>270.644444</td>\n",
       "      <td>290.319444</td>\n",
       "      <td>268.880556</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-64.661111</td>\n",
       "      <td>-64.661111</td>\n",
       "      <td>-4.211111</td>\n",
       "      <td>89.900000</td>\n",
       "      <td>3.958333</td>\n",
       "      <td>150.894444</td>\n",
       "      <td>952.938889</td>\n",
       "      <td>38.153234</td>\n",
       "      <td>-0.171464</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8.0</th>\n",
       "      <td>140.396000</td>\n",
       "      <td>-4.896667</td>\n",
       "      <td>-0.068333</td>\n",
       "      <td>1.795000</td>\n",
       "      <td>-1.573333</td>\n",
       "      <td>236.036667</td>\n",
       "      <td>270.815000</td>\n",
       "      <td>270.221667</td>\n",
       "      <td>288.866667</td>\n",
       "      <td>268.450000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-52.828333</td>\n",
       "      <td>-52.828333</td>\n",
       "      <td>-4.593333</td>\n",
       "      <td>91.183333</td>\n",
       "      <td>3.235000</td>\n",
       "      <td>148.135000</td>\n",
       "      <td>952.245000</td>\n",
       "      <td>148.814364</td>\n",
       "      <td>-0.037425</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9.0</th>\n",
       "      <td>131.753500</td>\n",
       "      <td>-4.581667</td>\n",
       "      <td>-0.096667</td>\n",
       "      <td>3.088333</td>\n",
       "      <td>-1.426667</td>\n",
       "      <td>247.976667</td>\n",
       "      <td>270.580000</td>\n",
       "      <td>269.986667</td>\n",
       "      <td>288.435000</td>\n",
       "      <td>268.101667</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-40.468333</td>\n",
       "      <td>-40.468333</td>\n",
       "      <td>-4.898333</td>\n",
       "      <td>92.058333</td>\n",
       "      <td>2.476667</td>\n",
       "      <td>131.661667</td>\n",
       "      <td>951.585000</td>\n",
       "      <td>314.244805</td>\n",
       "      <td>-0.015046</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10.0</th>\n",
       "      <td>121.700500</td>\n",
       "      <td>-4.665000</td>\n",
       "      <td>-0.340000</td>\n",
       "      <td>3.340000</td>\n",
       "      <td>-1.415000</td>\n",
       "      <td>241.571667</td>\n",
       "      <td>270.898333</td>\n",
       "      <td>270.323333</td>\n",
       "      <td>287.598333</td>\n",
       "      <td>268.296667</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-46.030000</td>\n",
       "      <td>-46.030000</td>\n",
       "      <td>-4.451667</td>\n",
       "      <td>91.293333</td>\n",
       "      <td>2.421667</td>\n",
       "      <td>145.975000</td>\n",
       "      <td>950.940000</td>\n",
       "      <td>454.239484</td>\n",
       "      <td>-0.010324</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11.0</th>\n",
       "      <td>111.024500</td>\n",
       "      <td>-4.480000</td>\n",
       "      <td>-0.051667</td>\n",
       "      <td>4.360000</td>\n",
       "      <td>-1.233333</td>\n",
       "      <td>271.590000</td>\n",
       "      <td>271.455000</td>\n",
       "      <td>270.916667</td>\n",
       "      <td>294.383333</td>\n",
       "      <td>268.693333</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-22.785000</td>\n",
       "      <td>-22.785000</td>\n",
       "      <td>-4.213333</td>\n",
       "      <td>91.341667</td>\n",
       "      <td>2.233333</td>\n",
       "      <td>118.871667</td>\n",
       "      <td>950.326667</td>\n",
       "      <td>548.885814</td>\n",
       "      <td>-0.008169</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12.0</th>\n",
       "      <td>100.208500</td>\n",
       "      <td>-4.666667</td>\n",
       "      <td>-0.300000</td>\n",
       "      <td>3.253333</td>\n",
       "      <td>-1.396667</td>\n",
       "      <td>240.323333</td>\n",
       "      <td>271.436667</td>\n",
       "      <td>270.723333</td>\n",
       "      <td>287.046667</td>\n",
       "      <td>268.766667</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-46.735000</td>\n",
       "      <td>-46.735000</td>\n",
       "      <td>-4.025000</td>\n",
       "      <td>91.908333</td>\n",
       "      <td>2.181667</td>\n",
       "      <td>99.051667</td>\n",
       "      <td>949.630000</td>\n",
       "      <td>589.040966</td>\n",
       "      <td>-0.007920</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13.0</th>\n",
       "      <td>89.412833</td>\n",
       "      <td>6.941667</td>\n",
       "      <td>2.568333</td>\n",
       "      <td>37.210000</td>\n",
       "      <td>9.313333</td>\n",
       "      <td>229.436667</td>\n",
       "      <td>270.868333</td>\n",
       "      <td>270.236667</td>\n",
       "      <td>288.171667</td>\n",
       "      <td>268.686667</td>\n",
       "      <td>...</td>\n",
       "      <td>9.796667</td>\n",
       "      <td>-58.736667</td>\n",
       "      <td>-48.943333</td>\n",
       "      <td>-4.318333</td>\n",
       "      <td>94.170000</td>\n",
       "      <td>3.183333</td>\n",
       "      <td>115.316667</td>\n",
       "      <td>948.886667</td>\n",
       "      <td>571.239451</td>\n",
       "      <td>0.012708</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14.0</th>\n",
       "      <td>79.509000</td>\n",
       "      <td>66.015000</td>\n",
       "      <td>14.283333</td>\n",
       "      <td>39.245000</td>\n",
       "      <td>70.763333</td>\n",
       "      <td>269.898333</td>\n",
       "      <td>272.405000</td>\n",
       "      <td>272.001667</td>\n",
       "      <td>301.948333</td>\n",
       "      <td>269.031667</td>\n",
       "      <td>...</td>\n",
       "      <td>61.178333</td>\n",
       "      <td>-32.058333</td>\n",
       "      <td>29.125000</td>\n",
       "      <td>-3.945000</td>\n",
       "      <td>94.998333</td>\n",
       "      <td>2.643333</td>\n",
       "      <td>128.601667</td>\n",
       "      <td>948.573333</td>\n",
       "      <td>497.007676</td>\n",
       "      <td>0.135205</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15.0</th>\n",
       "      <td>70.543833</td>\n",
       "      <td>184.720000</td>\n",
       "      <td>39.195000</td>\n",
       "      <td>60.050000</td>\n",
       "      <td>178.240000</td>\n",
       "      <td>284.471667</td>\n",
       "      <td>275.201667</td>\n",
       "      <td>274.968333</td>\n",
       "      <td>316.086667</td>\n",
       "      <td>271.068333</td>\n",
       "      <td>...</td>\n",
       "      <td>160.998333</td>\n",
       "      <td>-31.616667</td>\n",
       "      <td>129.378333</td>\n",
       "      <td>-2.130000</td>\n",
       "      <td>93.173333</td>\n",
       "      <td>2.938333</td>\n",
       "      <td>140.046667</td>\n",
       "      <td>947.636667</td>\n",
       "      <td>373.110606</td>\n",
       "      <td>0.519083</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16.0</th>\n",
       "      <td>63.113167</td>\n",
       "      <td>392.156667</td>\n",
       "      <td>84.996667</td>\n",
       "      <td>374.610000</td>\n",
       "      <td>232.673333</td>\n",
       "      <td>275.468333</td>\n",
       "      <td>278.751667</td>\n",
       "      <td>278.428333</td>\n",
       "      <td>335.985000</td>\n",
       "      <td>273.831667</td>\n",
       "      <td>...</td>\n",
       "      <td>325.068333</td>\n",
       "      <td>-60.511667</td>\n",
       "      <td>264.566667</td>\n",
       "      <td>0.220000</td>\n",
       "      <td>86.426667</td>\n",
       "      <td>2.755000</td>\n",
       "      <td>146.100000</td>\n",
       "      <td>946.630000</td>\n",
       "      <td>213.855930</td>\n",
       "      <td>2.104288</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17.0</th>\n",
       "      <td>57.950847</td>\n",
       "      <td>600.271186</td>\n",
       "      <td>128.689831</td>\n",
       "      <td>738.779661</td>\n",
       "      <td>221.118644</td>\n",
       "      <td>273.759322</td>\n",
       "      <td>284.210169</td>\n",
       "      <td>283.540678</td>\n",
       "      <td>361.523729</td>\n",
       "      <td>277.961017</td>\n",
       "      <td>...</td>\n",
       "      <td>484.508475</td>\n",
       "      <td>-87.757627</td>\n",
       "      <td>396.744068</td>\n",
       "      <td>3.664407</td>\n",
       "      <td>71.598305</td>\n",
       "      <td>3.384746</td>\n",
       "      <td>126.791525</td>\n",
       "      <td>945.572881</td>\n",
       "      <td>62.208640</td>\n",
       "      <td>13.667905</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>11 rows × 23 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "             zen    dw_solar    uw_solar    direct_n     diffuse       dw_ir  \\\n",
       "hour                                                                           \n",
       "7.0   145.536667   -5.036111   -0.005556    1.050000   -1.691667  225.650000   \n",
       "8.0   140.396000   -4.896667   -0.068333    1.795000   -1.573333  236.036667   \n",
       "9.0   131.753500   -4.581667   -0.096667    3.088333   -1.426667  247.976667   \n",
       "10.0  121.700500   -4.665000   -0.340000    3.340000   -1.415000  241.571667   \n",
       "11.0  111.024500   -4.480000   -0.051667    4.360000   -1.233333  271.590000   \n",
       "12.0  100.208500   -4.666667   -0.300000    3.253333   -1.396667  240.323333   \n",
       "13.0   89.412833    6.941667    2.568333   37.210000    9.313333  229.436667   \n",
       "14.0   79.509000   66.015000   14.283333   39.245000   70.763333  269.898333   \n",
       "15.0   70.543833  184.720000   39.195000   60.050000  178.240000  284.471667   \n",
       "16.0   63.113167  392.156667   84.996667  374.610000  232.673333  275.468333   \n",
       "17.0   57.950847  600.271186  128.689831  738.779661  221.118644  273.759322   \n",
       "\n",
       "      dw_casetemp  dw_dometemp       uw_ir  uw_casetemp    ...      \\\n",
       "hour                                                       ...       \n",
       "7.0    271.297222   270.644444  290.319444   268.880556    ...       \n",
       "8.0    270.815000   270.221667  288.866667   268.450000    ...       \n",
       "9.0    270.580000   269.986667  288.435000   268.101667    ...       \n",
       "10.0   270.898333   270.323333  287.598333   268.296667    ...       \n",
       "11.0   271.455000   270.916667  294.383333   268.693333    ...       \n",
       "12.0   271.436667   270.723333  287.046667   268.766667    ...       \n",
       "13.0   270.868333   270.236667  288.171667   268.686667    ...       \n",
       "14.0   272.405000   272.001667  301.948333   269.031667    ...       \n",
       "15.0   275.201667   274.968333  316.086667   271.068333    ...       \n",
       "16.0   278.751667   278.428333  335.985000   273.831667    ...       \n",
       "17.0   284.210169   283.540678  361.523729   277.961017    ...       \n",
       "\n",
       "        netsolar      netir    totalnet      temp         rh   windspd  \\\n",
       "hour                                                                     \n",
       "7.0     0.000000 -64.661111  -64.661111 -4.211111  89.900000  3.958333   \n",
       "8.0     0.000000 -52.828333  -52.828333 -4.593333  91.183333  3.235000   \n",
       "9.0     0.000000 -40.468333  -40.468333 -4.898333  92.058333  2.476667   \n",
       "10.0    0.000000 -46.030000  -46.030000 -4.451667  91.293333  2.421667   \n",
       "11.0    0.000000 -22.785000  -22.785000 -4.213333  91.341667  2.233333   \n",
       "12.0    0.000000 -46.735000  -46.735000 -4.025000  91.908333  2.181667   \n",
       "13.0    9.796667 -58.736667  -48.943333 -4.318333  94.170000  3.183333   \n",
       "14.0   61.178333 -32.058333   29.125000 -3.945000  94.998333  2.643333   \n",
       "15.0  160.998333 -31.616667  129.378333 -2.130000  93.173333  2.938333   \n",
       "16.0  325.068333 -60.511667  264.566667  0.220000  86.426667  2.755000   \n",
       "17.0  484.508475 -87.757627  396.744068  3.664407  71.598305  3.384746   \n",
       "\n",
       "         winddir    pressure         ghi         Kt  \n",
       "hour                                                 \n",
       "7.0   150.894444  952.938889   38.153234  -0.171464  \n",
       "8.0   148.135000  952.245000  148.814364  -0.037425  \n",
       "9.0   131.661667  951.585000  314.244805  -0.015046  \n",
       "10.0  145.975000  950.940000  454.239484  -0.010324  \n",
       "11.0  118.871667  950.326667  548.885814  -0.008169  \n",
       "12.0   99.051667  949.630000  589.040966  -0.007920  \n",
       "13.0  115.316667  948.886667  571.239451   0.012708  \n",
       "14.0  128.601667  948.573333  497.007676   0.135205  \n",
       "15.0  140.046667  947.636667  373.110606   0.519083  \n",
       "16.0  146.100000  946.630000  213.855930   2.104288  \n",
       "17.0  126.791525  945.572881   62.208640  13.667905  \n",
       "\n",
       "[11 rows x 23 columns]"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_new_test.loc[2].xs(17,level='day')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Shifting Kt values to make 1 hour ahead forecast"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Train dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [],
   "source": [
    "levels_index= []\n",
    "for m in df_new_train.index.levels:\n",
    "    levels_index.append(m)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "for i in levels_index[0]:\n",
    "    for j in levels_index[1]:\n",
    "        df_new_train.loc[i].loc[j]['Kt'] = df_new_train.loc[i].loc[j]['Kt'].shift(-2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_new_train = df_new_train[~(df_new_train['Kt'].isnull())]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Test dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [],
   "source": [
    "levels_index2= []\n",
    "for m in df_new_test.index.levels:\n",
    "    levels_index2.append(m)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [],
   "source": [
    "for i in levels_index2[0]:\n",
    "    for j in levels_index2[1]:\n",
    "        df_new_test.loc[i].loc[j]['Kt'] = df_new_test.loc[i].loc[j]['Kt'].shift(-2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_new_test = df_new_test[~(df_new_test['Kt'].isnull())]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>zen</th>\n",
       "      <th>dw_solar</th>\n",
       "      <th>uw_solar</th>\n",
       "      <th>direct_n</th>\n",
       "      <th>diffuse</th>\n",
       "      <th>dw_ir</th>\n",
       "      <th>dw_casetemp</th>\n",
       "      <th>dw_dometemp</th>\n",
       "      <th>uw_ir</th>\n",
       "      <th>uw_casetemp</th>\n",
       "      <th>...</th>\n",
       "      <th>netsolar</th>\n",
       "      <th>netir</th>\n",
       "      <th>totalnet</th>\n",
       "      <th>temp</th>\n",
       "      <th>rh</th>\n",
       "      <th>windspd</th>\n",
       "      <th>winddir</th>\n",
       "      <th>pressure</th>\n",
       "      <th>ghi</th>\n",
       "      <th>Kt</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>month</th>\n",
       "      <th>day</th>\n",
       "      <th>hour</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2.0</th>\n",
       "      <th>18.0</th>\n",
       "      <th>16.0</th>\n",
       "      <td>62.7875</td>\n",
       "      <td>468.13</td>\n",
       "      <td>100.736667</td>\n",
       "      <td>896.976667</td>\n",
       "      <td>65.341667</td>\n",
       "      <td>193.108333</td>\n",
       "      <td>270.663333</td>\n",
       "      <td>269.246667</td>\n",
       "      <td>298.706667</td>\n",
       "      <td>266.065</td>\n",
       "      <td>...</td>\n",
       "      <td>374.961667</td>\n",
       "      <td>-105.586667</td>\n",
       "      <td>269.363333</td>\n",
       "      <td>-7.67</td>\n",
       "      <td>58.155</td>\n",
       "      <td>10.326667</td>\n",
       "      <td>340.07</td>\n",
       "      <td>956.018333</td>\n",
       "      <td>218.474611</td>\n",
       "      <td>2382.577671</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1 rows × 23 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                     zen  dw_solar    uw_solar    direct_n    diffuse  \\\n",
       "month day  hour                                                         \n",
       "2.0   18.0 16.0  62.7875    468.13  100.736667  896.976667  65.341667   \n",
       "\n",
       "                      dw_ir  dw_casetemp  dw_dometemp       uw_ir  \\\n",
       "month day  hour                                                     \n",
       "2.0   18.0 16.0  193.108333   270.663333   269.246667  298.706667   \n",
       "\n",
       "                 uw_casetemp     ...         netsolar       netir    totalnet  \\\n",
       "month day  hour                  ...                                            \n",
       "2.0   18.0 16.0      266.065     ...       374.961667 -105.586667  269.363333   \n",
       "\n",
       "                 temp      rh    windspd  winddir    pressure         ghi  \\\n",
       "month day  hour                                                             \n",
       "2.0   18.0 16.0 -7.67  58.155  10.326667   340.07  956.018333  218.474611   \n",
       "\n",
       "                          Kt  \n",
       "month day  hour               \n",
       "2.0   18.0 16.0  2382.577671  \n",
       "\n",
       "[1 rows x 23 columns]"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_new_test[df_new_test['Kt']==df_new_test['Kt'].max()]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Normalize train and test dataframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_norm = (df_new_train - df_new_train.mean()) / (df_new_train.max() - df_new_train.min())\n",
    "test_norm =  (df_new_test - df_new_test.mean()) / (df_new_test.max() - df_new_test.min())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_norm.reset_index(inplace=True,drop=True)\n",
    "test_norm.reset_index(inplace=True,drop=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "### Making train and test sets with train_norm and test_norm"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "#### finding the gcf (greatest common factor) of train and test dataset's length and chop off the extra rows to make it divisible with the batchsize"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\s269596\\AppData\\Local\\Continuum\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:2: DeprecationWarning: fractions.gcd() is deprecated. Use math.gcd() instead.\n",
      "  \n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "1"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from fractions import gcd\n",
    "gcd(train_norm.shape[0],test_norm.shape[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [],
   "source": [
    "import math\n",
    "def roundup(x):\n",
    "    return int(math.ceil(x / 100.0)) * 100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_lim = roundup(train_norm.shape[0])\n",
    "test_lim = roundup(test_norm.shape[0])\n",
    "\n",
    "train_random = train_norm.sample(train_lim-train_norm.shape[0])\n",
    "test_random = test_norm.sample(test_lim-test_norm.shape[0])\n",
    "\n",
    "train_norm = train_norm.append(train_random)\n",
    "test_norm = test_norm.append(test_random)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [],
   "source": [
    "X1 = train_norm.drop('Kt',axis=1)\n",
    "y1 = train_norm['Kt']\n",
    "\n",
    "X2 = test_norm.drop('Kt',axis=1)\n",
    "y2 = test_norm['Kt']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "X1_train shape is (9600, 22)\n",
      "y1_train shape is (9600,)\n",
      "X2_test shape is (4100, 22)\n",
      "y2_test shape is (4100,)\n"
     ]
    }
   ],
   "source": [
    "print(\"X1_train shape is {}\".format(X1.shape))\n",
    "print(\"y1_train shape is {}\".format(y1.shape))\n",
    "print(\"X2_test shape is {}\".format(X2.shape))\n",
    "print(\"y2_test shape is {}\".format(y2.shape))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train = np.array(X1)\n",
    "y_train  = np.array(y1)\n",
    "X_test = np.array(X2)\n",
    "y_test = np.array(y2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### start of RNN"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [],
   "source": [
    "import torch\n",
    "import torch.nn as nn\n",
    "import torchvision.transforms as transforms\n",
    "import torchvision.datasets as dsets\n",
    "from torch.autograd import Variable"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [],
   "source": [
    "class RNNModel(nn.Module):\n",
    "    def __init__(self, input_dim, hidden_dim, layer_dim, output_dim):\n",
    "        super(RNNModel, self).__init__()\n",
    "        #Hidden Dimension\n",
    "        self.hidden_dim = hidden_dim\n",
    "        \n",
    "        # Number of hidden layers\n",
    "        self.layer_dim = layer_dim\n",
    "        \n",
    "        #Building the RNN\n",
    "        self.rnn = nn.RNN(input_dim, hidden_dim, layer_dim, batch_first=True, nonlinearity='relu')\n",
    "        \n",
    "        # Readout layer\n",
    "        self.fc = nn.Linear(hidden_dim, output_dim)\n",
    "        \n",
    "    def forward(self, x):\n",
    "        # Initializing the hidden state with zeros\n",
    "        # (layer_dim, batch_size, hidden_dim)\n",
    "        h0 = Variable(torch.zeros(self.layer_dim, x.size(0), self.hidden_dim))\n",
    "        \n",
    "        #One time step (the last one perhaps?)\n",
    "        out, hn = self.rnn(x, h0)\n",
    "        \n",
    "        # Indexing hidden state of the last time step\n",
    "        # out.size() --> ??\n",
    "        #out[:,-1,:] --> is it going to be 100,100\n",
    "        out = self.fc(out[:,-1,:])\n",
    "        # out.size() --> 100,1\n",
    "        return out\n",
    "        "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Instantiating Model Class\n",
    "input_dim = 22\n",
    "hidden_dim = 15\n",
    "layer_dim = 1\n",
    "output_dim = 1\n",
    "batch_size = 100\n",
    "\n",
    "model = RNNModel(input_dim, hidden_dim, layer_dim, output_dim)\n",
    "\n",
    "# Instantiating Loss Class\n",
    "criterion = nn.MSELoss()\n",
    "\n",
    "# Instantiate Optimizer Class\n",
    "learning_rate = 0.001\n",
    "optimizer = torch.optim.SGD(model.parameters(), lr=learning_rate)\n",
    "\n",
    "# converting numpy array to torch tensor\n",
    "X_train = torch.from_numpy(X_train)\n",
    "y_train = torch.from_numpy(y_train)\n",
    "X_test = torch.from_numpy(X_test)\n",
    "y_test = torch.from_numpy(y_test)\n",
    "\n",
    "# initializing lists to store losses over epochs:\n",
    "train_loss = []\n",
    "test_loss = []\n",
    "train_iter = []\n",
    "test_iter = []"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 0 Iteration: 0. Train_MSE: 0.1369161605834961. Test_MSE: 0.003520561985898844\n",
      "Epoch: 1 Iteration: 100. Train_MSE: 0.06133745238184929. Test_MSE: 0.002628870456975256\n",
      "Epoch: 2 Iteration: 200. Train_MSE: 0.020976895466446877. Test_MSE: 0.001998323934692096\n",
      "Epoch: 3 Iteration: 300. Train_MSE: 0.010557648725807667. Test_MSE: 0.001542959992870799\n",
      "Epoch: 4 Iteration: 400. Train_MSE: 0.011697902344167233. Test_MSE: 0.0012075730533155655\n",
      "Epoch: 5 Iteration: 500. Train_MSE: 0.009204409085214138. Test_MSE: 0.0009602275758945156\n",
      "Epoch: 6 Iteration: 600. Train_MSE: 0.00564443226903677. Test_MSE: 0.0007776120103262198\n",
      "Epoch: 7 Iteration: 700. Train_MSE: 0.0017134903464466333. Test_MSE: 0.0006460196295420462\n",
      "Epoch: 8 Iteration: 800. Train_MSE: 0.0010988668072968721. Test_MSE: 0.0005518331755803391\n",
      "Epoch: 9 Iteration: 900. Train_MSE: 0.002561698667705059. Test_MSE: 0.0004840173415895187\n",
      "Epoch: 10 Iteration: 1000. Train_MSE: 0.0016655370127409697. Test_MSE: 0.00043201588285885883\n",
      "Epoch: 11 Iteration: 1100. Train_MSE: 0.0016757382545620203. Test_MSE: 0.0003988148319473278\n",
      "Epoch: 12 Iteration: 1200. Train_MSE: 0.0012040267465636134. Test_MSE: 0.00037757403857108647\n",
      "Epoch: 13 Iteration: 1300. Train_MSE: 0.0006665721302852035. Test_MSE: 0.0003623088430123937\n",
      "Epoch: 14 Iteration: 1400. Train_MSE: 0.0010130864102393389. Test_MSE: 0.0003493202491459912\n",
      "Epoch: 15 Iteration: 1500. Train_MSE: 0.007309232838451862. Test_MSE: 0.0003382204943533564\n",
      "Epoch: 16 Iteration: 1600. Train_MSE: 0.000870311341714114. Test_MSE: 0.00032978059699913606\n",
      "Epoch: 17 Iteration: 1700. Train_MSE: 0.0010241593699902296. Test_MSE: 0.00032389355764100814\n",
      "Epoch: 18 Iteration: 1800. Train_MSE: 0.0013726381585001945. Test_MSE: 0.00032023533424906705\n",
      "Epoch: 20 Iteration: 1900. Train_MSE: 0.0031364625319838524. Test_MSE: 0.00031539154973980505\n",
      "Epoch: 21 Iteration: 2000. Train_MSE: 0.002143679652363062. Test_MSE: 0.00031143322774113754\n",
      "Epoch: 22 Iteration: 2100. Train_MSE: 0.0017448334256187081. Test_MSE: 0.00030994315514037144\n",
      "Epoch: 23 Iteration: 2200. Train_MSE: 0.0024294776376336813. Test_MSE: 0.0003089200536117461\n",
      "Epoch: 24 Iteration: 2300. Train_MSE: 0.001867957878857851. Test_MSE: 0.000306557804266223\n",
      "Epoch: 25 Iteration: 2400. Train_MSE: 0.001372967497445643. Test_MSE: 0.00030376028565759483\n",
      "Epoch: 26 Iteration: 2500. Train_MSE: 0.0008817738271318376. Test_MSE: 0.000300628338667328\n",
      "Epoch: 27 Iteration: 2600. Train_MSE: 0.0004645077569875866. Test_MSE: 0.00029874173335368354\n",
      "Epoch: 28 Iteration: 2700. Train_MSE: 0.0006209284183569252. Test_MSE: 0.00029765851402336875\n",
      "Epoch: 29 Iteration: 2800. Train_MSE: 0.0009861440630629659. Test_MSE: 0.0002968183192969739\n",
      "Epoch: 30 Iteration: 2900. Train_MSE: 0.0006583803333342075. Test_MSE: 0.00029379287718203645\n",
      "Epoch: 31 Iteration: 3000. Train_MSE: 0.00164805690292269. Test_MSE: 0.0002930465982242473\n",
      "Epoch: 32 Iteration: 3100. Train_MSE: 0.001259748823940754. Test_MSE: 0.00029371046840089774\n",
      "Epoch: 33 Iteration: 3200. Train_MSE: 0.000636681099422276. Test_MSE: 0.00029401482728974647\n",
      "Epoch: 34 Iteration: 3300. Train_MSE: 0.0008862668182700872. Test_MSE: 0.0002927975953520402\n",
      "Epoch: 35 Iteration: 3400. Train_MSE: 0.007152964361011982. Test_MSE: 0.0002905082946831432\n",
      "Epoch: 36 Iteration: 3500. Train_MSE: 0.0006465765764005482. Test_MSE: 0.0002885400128679387\n",
      "Epoch: 37 Iteration: 3600. Train_MSE: 0.000836190942209214. Test_MSE: 0.00028726165896355427\n",
      "Epoch: 38 Iteration: 3700. Train_MSE: 0.0012536675203591585. Test_MSE: 0.00028698745070320826\n",
      "Epoch: 40 Iteration: 3800. Train_MSE: 0.002927283989265561. Test_MSE: 0.0002851031446820694\n",
      "Epoch: 41 Iteration: 3900. Train_MSE: 0.001992435660213232. Test_MSE: 0.0002833960117948683\n",
      "Epoch: 42 Iteration: 4000. Train_MSE: 0.0014015588676556945. Test_MSE: 0.0002834056073537603\n",
      "Epoch: 43 Iteration: 4100. Train_MSE: 0.002009123796597123. Test_MSE: 0.0002835060577385971\n",
      "Epoch: 44 Iteration: 4200. Train_MSE: 0.0015777993248775601. Test_MSE: 0.00028223453916620216\n",
      "Epoch: 45 Iteration: 4300. Train_MSE: 0.0012134808348491788. Test_MSE: 0.00028042258810134947\n",
      "Epoch: 46 Iteration: 4400. Train_MSE: 0.0007301746518351138. Test_MSE: 0.0002781946063239993\n",
      "Epoch: 47 Iteration: 4500. Train_MSE: 0.0003884311008732766. Test_MSE: 0.00027700229667644734\n",
      "Epoch: 48 Iteration: 4600. Train_MSE: 0.0005616131238639355. Test_MSE: 0.00027649317265644957\n",
      "Epoch: 49 Iteration: 4700. Train_MSE: 0.0009071569656953216. Test_MSE: 0.0002762234643919858\n",
      "Epoch: 50 Iteration: 4800. Train_MSE: 0.0005709647666662931. Test_MSE: 0.00027386266457704876\n",
      "Epoch: 51 Iteration: 4900. Train_MSE: 0.0014097574166953564. Test_MSE: 0.0002734961807737323\n",
      "Epoch: 52 Iteration: 5000. Train_MSE: 0.001146997557953. Test_MSE: 0.00027447648129698697\n",
      "Epoch: 53 Iteration: 5100. Train_MSE: 0.0005822752136737108. Test_MSE: 0.000275067481435335\n",
      "Epoch: 54 Iteration: 5200. Train_MSE: 0.0008439227822236717. Test_MSE: 0.0002742513871275339\n",
      "Epoch: 55 Iteration: 5300. Train_MSE: 0.007132142782211304. Test_MSE: 0.00027239575200541414\n",
      "Epoch: 56 Iteration: 5400. Train_MSE: 0.0005641353782266378. Test_MSE: 0.0002708584597762106\n",
      "Epoch: 57 Iteration: 5500. Train_MSE: 0.000718767405487597. Test_MSE: 0.00026988754001898207\n",
      "Epoch: 58 Iteration: 5600. Train_MSE: 0.0011227934155613184. Test_MSE: 0.00026987847482895353\n",
      "Epoch: 60 Iteration: 5700. Train_MSE: 0.0028924571815878153. Test_MSE: 0.00026834592236733574\n",
      "Epoch: 61 Iteration: 5800. Train_MSE: 0.0019065163796767592. Test_MSE: 0.00026695189049389686\n",
      "Epoch: 62 Iteration: 5900. Train_MSE: 0.0011535033117979765. Test_MSE: 0.00026717735881503184\n",
      "Epoch: 63 Iteration: 6000. Train_MSE: 0.0017013194737955928. Test_MSE: 0.0002674443780522719\n",
      "Epoch: 64 Iteration: 6100. Train_MSE: 0.0013929822016507387. Test_MSE: 0.00026642624671416846\n",
      "Epoch: 65 Iteration: 6200. Train_MSE: 0.0011059596436098218. Test_MSE: 0.0002649044388175418\n",
      "Epoch: 66 Iteration: 6300. Train_MSE: 0.000626630790065974. Test_MSE: 0.00026299656525474133\n",
      "Epoch: 67 Iteration: 6400. Train_MSE: 0.000345354899764061. Test_MSE: 0.0002620585965353539\n",
      "Epoch: 68 Iteration: 6500. Train_MSE: 0.000529065728187561. Test_MSE: 0.00026177978397036444\n",
      "Epoch: 69 Iteration: 6600. Train_MSE: 0.0008578371489420533. Test_MSE: 0.00026175774456442394\n",
      "Epoch: 70 Iteration: 6700. Train_MSE: 0.0005216326680965722. Test_MSE: 0.0002596778987589589\n",
      "Epoch: 71 Iteration: 6800. Train_MSE: 0.001231278758496046. Test_MSE: 0.0002594648540454651\n",
      "Epoch: 72 Iteration: 6900. Train_MSE: 0.0010693484218791127. Test_MSE: 0.00026060678885726714\n",
      "Epoch: 73 Iteration: 7000. Train_MSE: 0.0005577122792601585. Test_MSE: 0.0002613482825156666\n",
      "Epoch: 74 Iteration: 7100. Train_MSE: 0.0008225326309911907. Test_MSE: 0.0002607588464846413\n",
      "Epoch: 75 Iteration: 7200. Train_MSE: 0.007088347803801298. Test_MSE: 0.0002591523695735283\n",
      "Epoch: 76 Iteration: 7300. Train_MSE: 0.0005045938887633383. Test_MSE: 0.0002578751340135596\n",
      "Epoch: 77 Iteration: 7400. Train_MSE: 0.0006453772075474262. Test_MSE: 0.00025706727109050734\n",
      "Epoch: 78 Iteration: 7500. Train_MSE: 0.001017424394376576. Test_MSE: 0.00025719314461216606\n",
      "Epoch: 80 Iteration: 7600. Train_MSE: 0.0028728318866342306. Test_MSE: 0.0002558310469879103\n",
      "Epoch: 81 Iteration: 7700. Train_MSE: 0.0018475742544978857. Test_MSE: 0.0002545982153945147\n",
      "Epoch: 82 Iteration: 7800. Train_MSE: 0.0009760273969732225. Test_MSE: 0.00025494155334038177\n",
      "Epoch: 83 Iteration: 7900. Train_MSE: 0.0014756562886759639. Test_MSE: 0.0002553009004125997\n",
      "Epoch: 84 Iteration: 8000. Train_MSE: 0.001264571095816791. Test_MSE: 0.0002544292860298674\n",
      "Epoch: 85 Iteration: 8100. Train_MSE: 0.001022652955725789. Test_MSE: 0.0002530814224143539\n",
      "Epoch: 86 Iteration: 8200. Train_MSE: 0.0005470465985126793. Test_MSE: 0.0002513701944012652\n",
      "Epoch: 87 Iteration: 8300. Train_MSE: 0.00031790920183993876. Test_MSE: 0.0002505848061233948\n",
      "Epoch: 88 Iteration: 8400. Train_MSE: 0.0005094927037134767. Test_MSE: 0.0002504368123669378\n",
      "Epoch: 89 Iteration: 8500. Train_MSE: 0.0008223072509281337. Test_MSE: 0.0002505509044064105\n",
      "Epoch: 90 Iteration: 8600. Train_MSE: 0.0004900856874883175. Test_MSE: 0.0002486304725962075\n",
      "Epoch: 91 Iteration: 8700. Train_MSE: 0.0010950318537652493. Test_MSE: 0.0002484896356013084\n",
      "Epoch: 92 Iteration: 8800. Train_MSE: 0.0010123976971954107. Test_MSE: 0.000249731451624334\n",
      "Epoch: 93 Iteration: 8900. Train_MSE: 0.0005485457950271666. Test_MSE: 0.00025056469794965873\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 94 Iteration: 9000. Train_MSE: 0.0008118210243992507. Test_MSE: 0.00025012267273854703\n",
      "Epoch: 95 Iteration: 9100. Train_MSE: 0.007031602784991264. Test_MSE: 0.00024867523051243945\n",
      "Epoch: 96 Iteration: 9200. Train_MSE: 0.00045895911171101034. Test_MSE: 0.00024756760728949866\n",
      "Epoch: 97 Iteration: 9300. Train_MSE: 0.0005965488962829113. Test_MSE: 0.00024686131854414676\n",
      "Epoch: 98 Iteration: 9400. Train_MSE: 0.0009335752693004906. Test_MSE: 0.0002470593648906663\n",
      "Epoch: 100 Iteration: 9500. Train_MSE: 0.002858369844034314. Test_MSE: 0.0002457800596802359\n",
      "Epoch: 101 Iteration: 9600. Train_MSE: 0.001805297564715147. Test_MSE: 0.0002446205542990488\n",
      "Epoch: 102 Iteration: 9700. Train_MSE: 0.0008458262891508639. Test_MSE: 0.0002450256399918885\n",
      "Epoch: 103 Iteration: 9800. Train_MSE: 0.001302738906815648. Test_MSE: 0.000245428784786137\n",
      "Epoch: 104 Iteration: 9900. Train_MSE: 0.001171901123598218. Test_MSE: 0.00024464802863964814\n",
      "Epoch: 105 Iteration: 10000. Train_MSE: 0.0009549749083817005. Test_MSE: 0.00024341832791218934\n",
      "Epoch: 106 Iteration: 10100. Train_MSE: 0.0004842744383495301. Test_MSE: 0.00024184651674889442\n",
      "Epoch: 107 Iteration: 10200. Train_MSE: 0.00029949567397125065. Test_MSE: 0.00024116224266860058\n",
      "Epoch: 108 Iteration: 10300. Train_MSE: 0.00049676897469908. Test_MSE: 0.00024109465153511277\n",
      "Epoch: 109 Iteration: 10400. Train_MSE: 0.0007932689622975886. Test_MSE: 0.0002412867890545318\n",
      "Epoch: 110 Iteration: 10500. Train_MSE: 0.0004672351060435176. Test_MSE: 0.00023945410596760427\n",
      "Epoch: 111 Iteration: 10600. Train_MSE: 0.0009875104296952486. Test_MSE: 0.0002393498986534262\n",
      "Epoch: 112 Iteration: 10700. Train_MSE: 0.0009669906576164067. Test_MSE: 0.00024066166158600506\n",
      "Epoch: 113 Iteration: 10800. Train_MSE: 0.0005454845959320664. Test_MSE: 0.0002415580299002735\n",
      "Epoch: 114 Iteration: 10900. Train_MSE: 0.0008056981023401022. Test_MSE: 0.000241224096595811\n",
      "Epoch: 115 Iteration: 11000. Train_MSE: 0.006967686582356691. Test_MSE: 0.00023989813437822223\n",
      "Epoch: 116 Iteration: 11100. Train_MSE: 0.00042300322093069553. Test_MSE: 0.00023892545860156817\n",
      "Epoch: 117 Iteration: 11200. Train_MSE: 0.0005619846051558852. Test_MSE: 0.00023828601966739221\n",
      "Epoch: 118 Iteration: 11300. Train_MSE: 0.0008621240267530084. Test_MSE: 0.00023851728303536367\n",
      "Epoch: 120 Iteration: 11400. Train_MSE: 0.0028393599204719067. Test_MSE: 0.00023728081386214302\n",
      "Epoch: 121 Iteration: 11500. Train_MSE: 0.0017728947568684816. Test_MSE: 0.00023616244634763758\n",
      "Epoch: 122 Iteration: 11600. Train_MSE: 0.0007444507791660726. Test_MSE: 0.00023660279880452942\n",
      "Epoch: 123 Iteration: 11700. Train_MSE: 0.0011651365784928203. Test_MSE: 0.000237031436331695\n",
      "Epoch: 124 Iteration: 11800. Train_MSE: 0.0011022195685654879. Test_MSE: 0.00023630983484097007\n",
      "Epoch: 125 Iteration: 11900. Train_MSE: 0.0009013634989969432. Test_MSE: 0.0002351600881818891\n",
      "Epoch: 126 Iteration: 12000. Train_MSE: 0.00043410074431449175. Test_MSE: 0.00023368431672748835\n",
      "Epoch: 127 Iteration: 12100. Train_MSE: 0.00028591789305210114. Test_MSE: 0.0002330724618755686\n",
      "Epoch: 128 Iteration: 12200. Train_MSE: 0.0004867405805271119. Test_MSE: 0.0002330566834575873\n",
      "Epoch: 129 Iteration: 12300. Train_MSE: 0.0007671314524486661. Test_MSE: 0.00023329287082106209\n",
      "Epoch: 130 Iteration: 12400. Train_MSE: 0.0004493104061111808. Test_MSE: 0.00023149981266560059\n",
      "Epoch: 131 Iteration: 12500. Train_MSE: 0.0009007030166685581. Test_MSE: 0.000231400709487786\n",
      "Epoch: 132 Iteration: 12600. Train_MSE: 0.0009288563742302358. Test_MSE: 0.00023275558316534673\n",
      "Epoch: 133 Iteration: 12700. Train_MSE: 0.0005453258636407554. Test_MSE: 0.00023368819570209064\n",
      "Epoch: 134 Iteration: 12800. Train_MSE: 0.0008020687964744866. Test_MSE: 0.00023342507083966158\n",
      "Epoch: 135 Iteration: 12900. Train_MSE: 0.00690245209261775. Test_MSE: 0.00023217998559370477\n",
      "Epoch: 136 Iteration: 13000. Train_MSE: 0.0003933848347514868. Test_MSE: 0.00023130043665126163\n",
      "Epoch: 137 Iteration: 13100. Train_MSE: 0.000536410603672266. Test_MSE: 0.00023069363972916075\n",
      "Epoch: 138 Iteration: 13200. Train_MSE: 0.0008001659298315644. Test_MSE: 0.00023093675986660632\n",
      "Epoch: 140 Iteration: 13300. Train_MSE: 0.002812172519043088. Test_MSE: 0.00022971738520750584\n",
      "Epoch: 141 Iteration: 13400. Train_MSE: 0.0017476545181125402. Test_MSE: 0.00022861889464689082\n",
      "Epoch: 142 Iteration: 13500. Train_MSE: 0.0006655952311120927. Test_MSE: 0.0002290732060534001\n",
      "Epoch: 143 Iteration: 13600. Train_MSE: 0.0010539585491642356. Test_MSE: 0.00022951343599489997\n",
      "Epoch: 144 Iteration: 13700. Train_MSE: 0.0010483890073373914. Test_MSE: 0.00022883514086920523\n",
      "Epoch: 145 Iteration: 13800. Train_MSE: 0.0008575219544582069. Test_MSE: 0.00022775678163685753\n",
      "Epoch: 146 Iteration: 13900. Train_MSE: 0.00039355686749331653. Test_MSE: 0.0002263726207238761\n",
      "Epoch: 147 Iteration: 14000. Train_MSE: 0.0002756614703685045. Test_MSE: 0.0002258221441241284\n",
      "Epoch: 148 Iteration: 14100. Train_MSE: 0.00047800171887502074. Test_MSE: 0.00022584726079233003\n",
      "Epoch: 149 Iteration: 14200. Train_MSE: 0.0007424698560498655. Test_MSE: 0.00022611797031191316\n",
      "Epoch: 150 Iteration: 14300. Train_MSE: 0.0004341774038039148. Test_MSE: 0.00022437336238152064\n",
      "Epoch: 151 Iteration: 14400. Train_MSE: 0.0008292821003124118. Test_MSE: 0.00022428164698528493\n",
      "Epoch: 152 Iteration: 14500. Train_MSE: 0.0008960310951806605. Test_MSE: 0.00022566515199086556\n",
      "Epoch: 153 Iteration: 14600. Train_MSE: 0.0005460401298478246. Test_MSE: 0.0002266269983742239\n",
      "Epoch: 154 Iteration: 14700. Train_MSE: 0.0007989648729562759. Test_MSE: 0.0002264272680853216\n",
      "Epoch: 155 Iteration: 14800. Train_MSE: 0.006838198751211166. Test_MSE: 0.0002252646632817216\n",
      "Epoch: 156 Iteration: 14900. Train_MSE: 0.0003689044388011098. Test_MSE: 0.00022447805306082994\n",
      "Epoch: 157 Iteration: 15000. Train_MSE: 0.0005145061295479536. Test_MSE: 0.00022390668541847378\n",
      "Epoch: 158 Iteration: 15100. Train_MSE: 0.0007465500384569168. Test_MSE: 0.00022415438745102608\n",
      "Epoch: 160 Iteration: 15200. Train_MSE: 0.0027775776106864214. Test_MSE: 0.0002229548733402216\n",
      "Epoch: 161 Iteration: 15300. Train_MSE: 0.0017266754293814301. Test_MSE: 0.00022187399841365368\n",
      "Epoch: 162 Iteration: 15400. Train_MSE: 0.0006025988841429353. Test_MSE: 0.0002223335427375409\n",
      "Epoch: 163 Iteration: 15500. Train_MSE: 0.0009621962672099471. Test_MSE: 0.00022277425137379167\n",
      "Epoch: 164 Iteration: 15600. Train_MSE: 0.0010064966045320034. Test_MSE: 0.00022213162386455464\n",
      "Epoch: 165 Iteration: 15700. Train_MSE: 0.000820278306491673. Test_MSE: 0.00022111689180733841\n",
      "Epoch: 166 Iteration: 15800. Train_MSE: 0.00036027244641445577. Test_MSE: 0.00021981381355135797\n",
      "Epoch: 167 Iteration: 15900. Train_MSE: 0.0002671229012776166. Test_MSE: 0.00021931306632823891\n",
      "Epoch: 168 Iteration: 16000. Train_MSE: 0.0004692719376180321. Test_MSE: 0.0002193662384455411\n",
      "Epoch: 169 Iteration: 16100. Train_MSE: 0.0007181081455200911. Test_MSE: 0.00021965806167025998\n",
      "Epoch: 170 Iteration: 16200. Train_MSE: 0.0004206970625091344. Test_MSE: 0.00021796355333700675\n",
      "Epoch: 171 Iteration: 16300. Train_MSE: 0.0007703466690145433. Test_MSE: 0.00021786932750128028\n",
      "Epoch: 172 Iteration: 16400. Train_MSE: 0.0008664689376018941. Test_MSE: 0.00021926193457734019\n",
      "Epoch: 173 Iteration: 16500. Train_MSE: 0.0005457272636704147. Test_MSE: 0.00022023728573764803\n",
      "Epoch: 174 Iteration: 16600. Train_MSE: 0.0007957142661325634. Test_MSE: 0.0002200870817980317\n",
      "Epoch: 175 Iteration: 16700. Train_MSE: 0.006777013652026653. Test_MSE: 0.00021899010778322973\n",
      "Epoch: 176 Iteration: 16800. Train_MSE: 0.00034843114553950727. Test_MSE: 0.00021827561419053562\n",
      "Epoch: 177 Iteration: 16900. Train_MSE: 0.0004964073887094855. Test_MSE: 0.0002177238325112609\n",
      "Epoch: 178 Iteration: 17000. Train_MSE: 0.0007006400846876204. Test_MSE: 0.00021795947797005003\n",
      "Epoch: 180 Iteration: 17100. Train_MSE: 0.0027351987082511187. Test_MSE: 0.00021677009519862925\n",
      "Epoch: 181 Iteration: 17200. Train_MSE: 0.0017076226649805903. Test_MSE: 0.0002156971747546112\n",
      "Epoch: 182 Iteration: 17300. Train_MSE: 0.0005507757305167615. Test_MSE: 0.00021614573419420955\n",
      "Epoch: 183 Iteration: 17400. Train_MSE: 0.0008858743822202086. Test_MSE: 0.00021657171029837254\n",
      "Epoch: 184 Iteration: 17500. Train_MSE: 0.0009732248727232218. Test_MSE: 0.00021595055915256967\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 185 Iteration: 17600. Train_MSE: 0.0007882328936830163. Test_MSE: 0.0002149815731425126\n",
      "Epoch: 186 Iteration: 17700. Train_MSE: 0.0003324775316286832. Test_MSE: 0.00021374143572648255\n",
      "Epoch: 187 Iteration: 17800. Train_MSE: 0.0002599232830107212. Test_MSE: 0.00021327367664965996\n",
      "Epoch: 188 Iteration: 17900. Train_MSE: 0.0004609735042322427. Test_MSE: 0.00021334013483910376\n",
      "Epoch: 189 Iteration: 18000. Train_MSE: 0.0006932339747436345. Test_MSE: 0.0002136386712354986\n",
      "Epoch: 190 Iteration: 18100. Train_MSE: 0.0004082323284819722. Test_MSE: 0.00021198555499003907\n",
      "Epoch: 191 Iteration: 18200. Train_MSE: 0.0007208215538412333. Test_MSE: 0.00021188301915039384\n",
      "Epoch: 192 Iteration: 18300. Train_MSE: 0.0008398043573834002. Test_MSE: 0.0002132683460218822\n",
      "Epoch: 193 Iteration: 18400. Train_MSE: 0.0005441199173219502. Test_MSE: 0.00021424123180501005\n",
      "Epoch: 194 Iteration: 18500. Train_MSE: 0.0007924804231151938. Test_MSE: 0.0002141263403118965\n",
      "Epoch: 195 Iteration: 18600. Train_MSE: 0.006719001568853855. Test_MSE: 0.00021308574487383596\n",
      "Epoch: 196 Iteration: 18700. Train_MSE: 0.00033104702015407383. Test_MSE: 0.00021243518000265606\n",
      "Epoch: 197 Iteration: 18800. Train_MSE: 0.0004813681880477816. Test_MSE: 0.0002118994084752449\n",
      "Epoch: 198 Iteration: 18900. Train_MSE: 0.0006607964169234037. Test_MSE: 0.00021212130862010566\n",
      "Epoch: 200 Iteration: 19000. Train_MSE: 0.0026872139424085617. Test_MSE: 0.0002109422028621269\n",
      "Epoch: 201 Iteration: 19100. Train_MSE: 0.001689544296823442. Test_MSE: 0.00020987901075711582\n",
      "Epoch: 202 Iteration: 19200. Train_MSE: 0.0005071205087006092. Test_MSE: 0.00021031411217827472\n",
      "Epoch: 203 Iteration: 19300. Train_MSE: 0.0008219823939725757. Test_MSE: 0.00021072130543509153\n",
      "Epoch: 204 Iteration: 19400. Train_MSE: 0.0009466232149861753. Test_MSE: 0.00021011481155421097\n",
      "Epoch: 205 Iteration: 19500. Train_MSE: 0.0007597862859256566. Test_MSE: 0.00020918673126279783\n",
      "Epoch: 206 Iteration: 19600. Train_MSE: 0.00030942665762268007. Test_MSE: 0.00020800378720035116\n",
      "Epoch: 207 Iteration: 19700. Train_MSE: 0.0002538567641749978. Test_MSE: 0.00020756604833820574\n",
      "Epoch: 208 Iteration: 19800. Train_MSE: 0.0004524403193499893. Test_MSE: 0.00020763960425415932\n",
      "Epoch: 209 Iteration: 19900. Train_MSE: 0.0006690153386443853. Test_MSE: 0.00020794012127442447\n",
      "Epoch: 210 Iteration: 20000. Train_MSE: 0.0003965591313317418. Test_MSE: 0.00020632871851190772\n",
      "Epoch: 211 Iteration: 20100. Train_MSE: 0.000678440323099494. Test_MSE: 0.00020621478462171046\n",
      "Epoch: 212 Iteration: 20200. Train_MSE: 0.0008149518980644643. Test_MSE: 0.00020758452373605146\n",
      "Epoch: 213 Iteration: 20300. Train_MSE: 0.0005412871832959354. Test_MSE: 0.00020854842581855473\n",
      "Epoch: 214 Iteration: 20400. Train_MSE: 0.000788936682511121. Test_MSE: 0.00020846128793234583\n",
      "Epoch: 215 Iteration: 20500. Train_MSE: 0.00666408846154809. Test_MSE: 0.00020747286491580043\n",
      "Epoch: 216 Iteration: 20600. Train_MSE: 0.0003160381456837058. Test_MSE: 0.00020688051472492888\n",
      "Epoch: 217 Iteration: 20700. Train_MSE: 0.0004686694301199168. Test_MSE: 0.00020636140116115064\n",
      "Epoch: 218 Iteration: 20800. Train_MSE: 0.0006256960332393646. Test_MSE: 0.00020656602571245123\n",
      "Epoch: 220 Iteration: 20900. Train_MSE: 0.002634840551763773. Test_MSE: 0.00020540570753993482\n",
      "Epoch: 221 Iteration: 21000. Train_MSE: 0.0016724045854061842. Test_MSE: 0.00020436201456329047\n",
      "Epoch: 222 Iteration: 21100. Train_MSE: 0.0004700657445937395. Test_MSE: 0.0002047853071124175\n",
      "Epoch: 223 Iteration: 21200. Train_MSE: 0.0007672880310565233. Test_MSE: 0.00020517725378252428\n",
      "Epoch: 224 Iteration: 21300. Train_MSE: 0.0009250605944544077. Test_MSE: 0.00020458956342541754\n",
      "Epoch: 225 Iteration: 21400. Train_MSE: 0.0007350817904807627. Test_MSE: 0.00020370636688208103\n",
      "Epoch: 226 Iteration: 21500. Train_MSE: 0.0002902499109040946. Test_MSE: 0.00020258543999923147\n",
      "Epoch: 227 Iteration: 21600. Train_MSE: 0.00024807502632029355. Test_MSE: 0.00020217424663123337\n",
      "Epoch: 228 Iteration: 21700. Train_MSE: 0.000443752360297367. Test_MSE: 0.00020225475497804803\n",
      "Epoch: 229 Iteration: 21800. Train_MSE: 0.0006455348921008408. Test_MSE: 0.00020255639873845082\n",
      "Epoch: 230 Iteration: 21900. Train_MSE: 0.0003852935624308884. Test_MSE: 0.00020099700013729197\n",
      "Epoch: 231 Iteration: 22000. Train_MSE: 0.0006409806082956493. Test_MSE: 0.00020087823123036877\n",
      "Epoch: 232 Iteration: 22100. Train_MSE: 0.0007919117342680693. Test_MSE: 0.00020222786245865587\n",
      "Epoch: 233 Iteration: 22200. Train_MSE: 0.0005377679481171072. Test_MSE: 0.00020318032989645124\n",
      "Epoch: 234 Iteration: 22300. Train_MSE: 0.0007848371751606464. Test_MSE: 0.00020311885363173717\n",
      "Epoch: 235 Iteration: 22400. Train_MSE: 0.006612634751945734. Test_MSE: 0.00020218054452907119\n",
      "Epoch: 236 Iteration: 22500. Train_MSE: 0.00030295512988232076. Test_MSE: 0.00020163874761715132\n",
      "Epoch: 237 Iteration: 22600. Train_MSE: 0.00045692850835621357. Test_MSE: 0.00020113180499380307\n",
      "Epoch: 238 Iteration: 22700. Train_MSE: 0.0005947508616372943. Test_MSE: 0.00020131897665872202\n",
      "Epoch: 240 Iteration: 22800. Train_MSE: 0.002578247571364045. Test_MSE: 0.000200176837236305\n",
      "Epoch: 241 Iteration: 22900. Train_MSE: 0.0016560123767703772. Test_MSE: 0.00019915078651899028\n",
      "Epoch: 242 Iteration: 23000. Train_MSE: 0.0004383748455438763. Test_MSE: 0.0001995561930915213\n",
      "Epoch: 243 Iteration: 23100. Train_MSE: 0.0007193900528363883. Test_MSE: 0.00019992535798910808\n",
      "Epoch: 244 Iteration: 23200. Train_MSE: 0.0009070943342521787. Test_MSE: 0.00019934987081502738\n",
      "Epoch: 245 Iteration: 23300. Train_MSE: 0.0007135983323678374. Test_MSE: 0.00019850285132818165\n",
      "Epoch: 246 Iteration: 23400. Train_MSE: 0.00027416730881668627. Test_MSE: 0.0001974324277257665\n",
      "Epoch: 247 Iteration: 23500. Train_MSE: 0.00024279733770526946. Test_MSE: 0.00019704305372381337\n",
      "Epoch: 248 Iteration: 23600. Train_MSE: 0.00043503500637598336. Test_MSE: 0.00019712380450802647\n",
      "Epoch: 249 Iteration: 23700. Train_MSE: 0.000622703053522855. Test_MSE: 0.0001974194827200853\n",
      "Epoch: 250 Iteration: 23800. Train_MSE: 0.00037418349529616535. Test_MSE: 0.00019590514050752973\n",
      "Epoch: 251 Iteration: 23900. Train_MSE: 0.0006084371125325561. Test_MSE: 0.00019577941925154433\n",
      "Epoch: 252 Iteration: 24000. Train_MSE: 0.0007701826980337501. Test_MSE: 0.0001971014800214714\n",
      "Epoch: 253 Iteration: 24100. Train_MSE: 0.0005333133158273995. Test_MSE: 0.00019803533426400905\n",
      "Epoch: 254 Iteration: 24200. Train_MSE: 0.0007802944746799767. Test_MSE: 0.00019799163862277636\n",
      "Epoch: 255 Iteration: 24300. Train_MSE: 0.006564899813383818. Test_MSE: 0.00019709587350964295\n",
      "Epoch: 256 Iteration: 24400. Train_MSE: 0.0002914265787694603. Test_MSE: 0.0001965986360962852\n",
      "Epoch: 257 Iteration: 24500. Train_MSE: 0.0004466354148462415. Test_MSE: 0.00019610249546667368\n",
      "Epoch: 258 Iteration: 24600. Train_MSE: 0.0005671688704751432. Test_MSE: 0.00019627145698933914\n",
      "Epoch: 260 Iteration: 24700. Train_MSE: 0.002518638037145138. Test_MSE: 0.0001951507631955558\n",
      "Epoch: 261 Iteration: 24800. Train_MSE: 0.001640197355300188. Test_MSE: 0.00019414256102296527\n",
      "Epoch: 262 Iteration: 24900. Train_MSE: 0.0004110952722840011. Test_MSE: 0.0001945293172888468\n",
      "Epoch: 263 Iteration: 25000. Train_MSE: 0.0006774243665859103. Test_MSE: 0.00019487704812961887\n",
      "Epoch: 264 Iteration: 25100. Train_MSE: 0.0008920847903937101. Test_MSE: 0.00019431240767864364\n",
      "Epoch: 265 Iteration: 25200. Train_MSE: 0.0006947210640646517. Test_MSE: 0.00019349857603213045\n",
      "Epoch: 266 Iteration: 25300. Train_MSE: 0.0002604605397209525. Test_MSE: 0.0001924769397623089\n",
      "Epoch: 267 Iteration: 25400. Train_MSE: 0.00023779028560966253. Test_MSE: 0.00019210850873666496\n",
      "Epoch: 268 Iteration: 25500. Train_MSE: 0.0004265812458470464. Test_MSE: 0.00019219124606443256\n",
      "Epoch: 269 Iteration: 25600. Train_MSE: 0.0006002990994602442. Test_MSE: 0.00019248364157394652\n",
      "Epoch: 270 Iteration: 25700. Train_MSE: 0.0003633128071669489. Test_MSE: 0.0001910199181027577\n",
      "Epoch: 271 Iteration: 25800. Train_MSE: 0.0005798692000098526. Test_MSE: 0.00019089153530895938\n",
      "Epoch: 272 Iteration: 25900. Train_MSE: 0.0007496413309127092. Test_MSE: 0.00019218671094958897\n",
      "Epoch: 273 Iteration: 26000. Train_MSE: 0.0005279015749692917. Test_MSE: 0.0001931034835382247\n",
      "Epoch: 274 Iteration: 26100. Train_MSE: 0.0007753917598165572. Test_MSE: 0.00019307654113125483\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 275 Iteration: 26200. Train_MSE: 0.0065206303261220455. Test_MSE: 0.00019222332007409062\n",
      "Epoch: 276 Iteration: 26300. Train_MSE: 0.0002811878512147814. Test_MSE: 0.0001917685235885203\n",
      "Epoch: 277 Iteration: 26400. Train_MSE: 0.0004375993739813566. Test_MSE: 0.00019128518979400877\n",
      "Epoch: 278 Iteration: 26500. Train_MSE: 0.000542316643986851. Test_MSE: 0.00019143942402839604\n",
      "Epoch: 280 Iteration: 26600. Train_MSE: 0.00245849066413939. Test_MSE: 0.00019034311276985863\n",
      "Epoch: 281 Iteration: 26700. Train_MSE: 0.0016249555628746748. Test_MSE: 0.00018935617623132238\n",
      "Epoch: 282 Iteration: 26800. Train_MSE: 0.0003875688125845045. Test_MSE: 0.00018972515831797924\n",
      "Epoch: 283 Iteration: 26900. Train_MSE: 0.0006403569132089615. Test_MSE: 0.000190052640254222\n",
      "Epoch: 284 Iteration: 27000. Train_MSE: 0.0008791410946287215. Test_MSE: 0.00018949871830883154\n",
      "Epoch: 285 Iteration: 27100. Train_MSE: 0.0006779351970180869. Test_MSE: 0.0001887165732670033\n",
      "Epoch: 286 Iteration: 27200. Train_MSE: 0.00024890893837437034. Test_MSE: 0.00018774057719195654\n",
      "Epoch: 287 Iteration: 27300. Train_MSE: 0.00023315141152124852. Test_MSE: 0.00018739122765258124\n",
      "Epoch: 288 Iteration: 27400. Train_MSE: 0.00041820225305855274. Test_MSE: 0.00018747515743299128\n",
      "Epoch: 289 Iteration: 27500. Train_MSE: 0.0005781740183010697. Test_MSE: 0.00018776210941579176\n",
      "Epoch: 290 Iteration: 27600. Train_MSE: 0.0003526141226757318. Test_MSE: 0.00018634839254724408\n",
      "Epoch: 291 Iteration: 27700. Train_MSE: 0.0005543852457776666. Test_MSE: 0.00018621994744222082\n",
      "Epoch: 292 Iteration: 27800. Train_MSE: 0.0007301184814423323. Test_MSE: 0.00018748831202893015\n",
      "Epoch: 293 Iteration: 27900. Train_MSE: 0.0005219461163505912. Test_MSE: 0.00018838613313232563\n",
      "Epoch: 294 Iteration: 28000. Train_MSE: 0.0007702471339143813. Test_MSE: 0.00018837077198599056\n",
      "Epoch: 295 Iteration: 28100. Train_MSE: 0.006479719188064337. Test_MSE: 0.00018755217187386598\n",
      "Epoch: 296 Iteration: 28200. Train_MSE: 0.0002718751784414053. Test_MSE: 0.00018713154972113054\n",
      "Epoch: 297 Iteration: 28300. Train_MSE: 0.0004297572886571288. Test_MSE: 0.00018665578043646354\n",
      "Epoch: 298 Iteration: 28400. Train_MSE: 0.0005197199061512947. Test_MSE: 0.00018679034499745226\n",
      "Epoch: 300 Iteration: 28500. Train_MSE: 0.002398334676399827. Test_MSE: 0.00018571075559491244\n",
      "Epoch: 301 Iteration: 28600. Train_MSE: 0.0016100816428661346. Test_MSE: 0.00018473910538151266\n",
      "Epoch: 302 Iteration: 28700. Train_MSE: 0.0003668700228445232. Test_MSE: 0.0001850863192741591\n",
      "Epoch: 303 Iteration: 28800. Train_MSE: 0.0006073737749829888. Test_MSE: 0.00018539040336413198\n",
      "Epoch: 304 Iteration: 28900. Train_MSE: 0.0008678683079779148. Test_MSE: 0.000184841970168292\n",
      "Epoch: 305 Iteration: 29000. Train_MSE: 0.0006631180876865983. Test_MSE: 0.00018408553604616455\n",
      "Epoch: 306 Iteration: 29100. Train_MSE: 0.0002388594875810668. Test_MSE: 0.000183148403895723\n",
      "Epoch: 307 Iteration: 29200. Train_MSE: 0.00022853640257380903. Test_MSE: 0.0001828138360221928\n",
      "Epoch: 308 Iteration: 29300. Train_MSE: 0.000409764819778502. Test_MSE: 0.00018289663406257707\n",
      "Epoch: 309 Iteration: 29400. Train_MSE: 0.0005566304898820817. Test_MSE: 0.00018317856918913147\n",
      "Epoch: 310 Iteration: 29500. Train_MSE: 0.00034212623722851276. Test_MSE: 0.00018181500914699603\n",
      "Epoch: 311 Iteration: 29600. Train_MSE: 0.0005313770961947739. Test_MSE: 0.0001816867881253488\n",
      "Epoch: 312 Iteration: 29700. Train_MSE: 0.0007116714841686189. Test_MSE: 0.00018292505926611175\n",
      "Epoch: 313 Iteration: 29800. Train_MSE: 0.0005157407140359282. Test_MSE: 0.0001838054951660475\n",
      "Epoch: 314 Iteration: 29900. Train_MSE: 0.0007648809114471078. Test_MSE: 0.0001838026417184472\n",
      "Epoch: 315 Iteration: 30000. Train_MSE: 0.0064415233209729195. Test_MSE: 0.00018302131580040116\n",
      "Epoch: 316 Iteration: 30100. Train_MSE: 0.0002634818374644965. Test_MSE: 0.00018263790249844602\n",
      "Epoch: 317 Iteration: 30200. Train_MSE: 0.00042274207226000726. Test_MSE: 0.00018217661656854545\n",
      "Epoch: 318 Iteration: 30300. Train_MSE: 0.0004990110755898058. Test_MSE: 0.00018230140826224706\n",
      "Epoch: 320 Iteration: 30400. Train_MSE: 0.0023388625122606754. Test_MSE: 0.0001812549552122586\n",
      "Epoch: 321 Iteration: 30500. Train_MSE: 0.0015958416042849422. Test_MSE: 0.00018031244324100753\n",
      "Epoch: 322 Iteration: 30600. Train_MSE: 0.0003483880718704313. Test_MSE: 0.00018064921878984292\n",
      "Epoch: 323 Iteration: 30700. Train_MSE: 0.0005782807129435241. Test_MSE: 0.00018094020188940776\n",
      "Epoch: 324 Iteration: 30800. Train_MSE: 0.0008579098503105342. Test_MSE: 0.0001804061698979707\n",
      "Epoch: 325 Iteration: 30900. Train_MSE: 0.0006503226468339562. Test_MSE: 0.000179681900868933\n",
      "Epoch: 326 Iteration: 31000. Train_MSE: 0.00022991877631284297. Test_MSE: 0.00017878866229019638\n",
      "Epoch: 327 Iteration: 31100. Train_MSE: 0.00022419940796680748. Test_MSE: 0.00017847331347616168\n",
      "Epoch: 328 Iteration: 31200. Train_MSE: 0.00040132307913154364. Test_MSE: 0.00017855921935356085\n",
      "Epoch: 329 Iteration: 31300. Train_MSE: 0.0005363173549994826. Test_MSE: 0.00017884164383555868\n",
      "Epoch: 330 Iteration: 31400. Train_MSE: 0.0003321197291370481. Test_MSE: 0.00017753053191413585\n",
      "Epoch: 331 Iteration: 31500. Train_MSE: 0.0005103796138428152. Test_MSE: 0.0001774090488613953\n",
      "Epoch: 332 Iteration: 31600. Train_MSE: 0.0006941308965906501. Test_MSE: 0.00017862325886597929\n",
      "Epoch: 333 Iteration: 31700. Train_MSE: 0.0005091778002679348. Test_MSE: 0.00017948722391078638\n",
      "Epoch: 334 Iteration: 31800. Train_MSE: 0.0007592106121592224. Test_MSE: 0.00017949648817222095\n",
      "Epoch: 335 Iteration: 31900. Train_MSE: 0.006405293475836515. Test_MSE: 0.00017875069241619576\n",
      "Epoch: 336 Iteration: 32000. Train_MSE: 0.00025585058028809726. Test_MSE: 0.00017840121641414647\n",
      "Epoch: 337 Iteration: 32100. Train_MSE: 0.00041644994053058326. Test_MSE: 0.00017795175171311716\n",
      "Epoch: 338 Iteration: 32200. Train_MSE: 0.00047957408241927624. Test_MSE: 0.0001780647690683739\n",
      "Epoch: 340 Iteration: 32300. Train_MSE: 0.0022804439067840576. Test_MSE: 0.00017704247279441354\n",
      "Epoch: 341 Iteration: 32400. Train_MSE: 0.0015821914421394467. Test_MSE: 0.00017612398991754456\n",
      "Epoch: 342 Iteration: 32500. Train_MSE: 0.00033174172858707607. Test_MSE: 0.00017644870072030993\n",
      "Epoch: 343 Iteration: 32600. Train_MSE: 0.0005519958795048296. Test_MSE: 0.0001767246256511284\n",
      "Epoch: 344 Iteration: 32700. Train_MSE: 0.0008492065826430917. Test_MSE: 0.0001762006062056086\n",
      "Epoch: 345 Iteration: 32800. Train_MSE: 0.0006388332112692297. Test_MSE: 0.00017550508805227018\n",
      "Epoch: 346 Iteration: 32900. Train_MSE: 0.0002221233444288373. Test_MSE: 0.00017465207922388404\n",
      "Epoch: 347 Iteration: 33000. Train_MSE: 0.0002200401941081509. Test_MSE: 0.00017435413162963232\n",
      "Epoch: 348 Iteration: 33100. Train_MSE: 0.0003929029044229537. Test_MSE: 0.00017444216163948155\n",
      "Epoch: 349 Iteration: 33200. Train_MSE: 0.0005168039351701736. Test_MSE: 0.000174723042563928\n",
      "Epoch: 350 Iteration: 33300. Train_MSE: 0.00032231368822976947. Test_MSE: 0.00017346318672815302\n",
      "Epoch: 351 Iteration: 33400. Train_MSE: 0.0004911113646812737. Test_MSE: 0.0001733467583105504\n",
      "Epoch: 352 Iteration: 33500. Train_MSE: 0.0006773246568627656. Test_MSE: 0.00017453223167755656\n",
      "Epoch: 353 Iteration: 33600. Train_MSE: 0.0005022587138228118. Test_MSE: 0.000175378597869833\n",
      "Epoch: 354 Iteration: 33700. Train_MSE: 0.0007532018353231251. Test_MSE: 0.00017539671298831395\n",
      "Epoch: 355 Iteration: 33800. Train_MSE: 0.006371796131134033. Test_MSE: 0.00017468278321405568\n",
      "Epoch: 356 Iteration: 33900. Train_MSE: 0.00024890044005587697. Test_MSE: 0.00017436340781773124\n",
      "Epoch: 357 Iteration: 34000. Train_MSE: 0.00041049145511351526. Test_MSE: 0.0001739255238627188\n",
      "Epoch: 358 Iteration: 34100. Train_MSE: 0.0004617634695023298. Test_MSE: 0.00017402843060066356\n",
      "Epoch: 360 Iteration: 34200. Train_MSE: 0.0022228665184229612. Test_MSE: 0.00017303341454966376\n",
      "Epoch: 361 Iteration: 34300. Train_MSE: 0.0015689837746322155. Test_MSE: 0.00017213846948099259\n",
      "Epoch: 362 Iteration: 34400. Train_MSE: 0.00031676419894210994. Test_MSE: 0.00017244901803683667\n",
      "Epoch: 363 Iteration: 34500. Train_MSE: 0.0005278578610159457. Test_MSE: 0.00017270861947189515\n",
      "Epoch: 364 Iteration: 34600. Train_MSE: 0.0008414607727900147. Test_MSE: 0.0001721935852406692\n",
      "Epoch: 365 Iteration: 34700. Train_MSE: 0.0006284830742515624. Test_MSE: 0.0001715213769884011\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 366 Iteration: 34800. Train_MSE: 0.0002152433735318482. Test_MSE: 0.00017070298747791222\n",
      "Epoch: 367 Iteration: 34900. Train_MSE: 0.0002159882860723883. Test_MSE: 0.000170418255955848\n",
      "Epoch: 368 Iteration: 35000. Train_MSE: 0.0003845322644338012. Test_MSE: 0.0001705055264980491\n",
      "Epoch: 369 Iteration: 35100. Train_MSE: 0.0004975814954377711. Test_MSE: 0.00017078154758620016\n",
      "Epoch: 370 Iteration: 35200. Train_MSE: 0.00031272045453079045. Test_MSE: 0.00016956973877118686\n",
      "Epoch: 371 Iteration: 35300. Train_MSE: 0.00047337671276181936. Test_MSE: 0.00016945644075697943\n",
      "Epoch: 372 Iteration: 35400. Train_MSE: 0.0006611321005038917. Test_MSE: 0.00017061309039547768\n",
      "Epoch: 373 Iteration: 35500. Train_MSE: 0.0004951133159920573. Test_MSE: 0.00017143944239690422\n",
      "Epoch: 374 Iteration: 35600. Train_MSE: 0.0007475149468518794. Test_MSE: 0.00017146476329588643\n",
      "Epoch: 375 Iteration: 35700. Train_MSE: 0.006340880412608385. Test_MSE: 0.00017077983454635214\n",
      "Epoch: 376 Iteration: 35800. Train_MSE: 0.0002425478887744248. Test_MSE: 0.00017048773632351416\n",
      "Epoch: 377 Iteration: 35900. Train_MSE: 0.0004049735434819013. Test_MSE: 0.00017006060601271696\n",
      "Epoch: 378 Iteration: 36000. Train_MSE: 0.0004453091823961586. Test_MSE: 0.00017015383283756016\n",
      "Epoch: 380 Iteration: 36100. Train_MSE: 0.002166046528145671. Test_MSE: 0.00016918547926332746\n",
      "Epoch: 381 Iteration: 36200. Train_MSE: 0.0015561330365017056. Test_MSE: 0.00016831419597462534\n",
      "Epoch: 382 Iteration: 36300. Train_MSE: 0.0003034879919141531. Test_MSE: 0.00016861108452748466\n",
      "Epoch: 383 Iteration: 36400. Train_MSE: 0.0005057260277681053. Test_MSE: 0.00016885506915957863\n",
      "Epoch: 384 Iteration: 36500. Train_MSE: 0.0008345264941453934. Test_MSE: 0.00016834750558371054\n",
      "Epoch: 385 Iteration: 36600. Train_MSE: 0.0006189548876136541. Test_MSE: 0.00016769657141854164\n",
      "Epoch: 386 Iteration: 36700. Train_MSE: 0.00020906131248921156. Test_MSE: 0.000166909689296397\n",
      "Epoch: 387 Iteration: 36800. Train_MSE: 0.00021220410417299718. Test_MSE: 0.00016663760670397268\n",
      "Epoch: 388 Iteration: 36900. Train_MSE: 0.0003762560954783112. Test_MSE: 0.0001667246014312777\n",
      "Epoch: 389 Iteration: 37000. Train_MSE: 0.00047909197746776044. Test_MSE: 0.0001669958252027243\n",
      "Epoch: 390 Iteration: 37100. Train_MSE: 0.00030352637986652553. Test_MSE: 0.00016582900980355889\n",
      "Epoch: 391 Iteration: 37200. Train_MSE: 0.00045708310790359974. Test_MSE: 0.00016572096586664885\n",
      "Epoch: 392 Iteration: 37300. Train_MSE: 0.0006457766867242754. Test_MSE: 0.00016685117469806353\n",
      "Epoch: 393 Iteration: 37400. Train_MSE: 0.00048797414638102055. Test_MSE: 0.0001676607275936306\n",
      "Epoch: 394 Iteration: 37500. Train_MSE: 0.0007418713648803532. Test_MSE: 0.00016769216932363944\n",
      "Epoch: 395 Iteration: 37600. Train_MSE: 0.006311718840152025. Test_MSE: 0.00016703315481514486\n",
      "Epoch: 396 Iteration: 37700. Train_MSE: 0.00023666989000048488. Test_MSE: 0.00016676623661540568\n",
      "Epoch: 397 Iteration: 37800. Train_MSE: 0.00039978165295906365. Test_MSE: 0.00016634992612784395\n",
      "Epoch: 398 Iteration: 37900. Train_MSE: 0.0004298984713386744. Test_MSE: 0.00016643345550842334\n",
      "Epoch: 400 Iteration: 38000. Train_MSE: 0.002110630739480257. Test_MSE: 0.0001654885461179019\n",
      "Epoch: 401 Iteration: 38100. Train_MSE: 0.0015436720568686724. Test_MSE: 0.00016463984003496773\n",
      "Epoch: 402 Iteration: 38200. Train_MSE: 0.0002915353106800467. Test_MSE: 0.00016492585290860711\n",
      "Epoch: 403 Iteration: 38300. Train_MSE: 0.0004855430161114782. Test_MSE: 0.00016515688013742013\n",
      "Epoch: 404 Iteration: 38400. Train_MSE: 0.000828133022878319. Test_MSE: 0.00016465810553497657\n",
      "Epoch: 405 Iteration: 38500. Train_MSE: 0.0006099743186496198. Test_MSE: 0.00016403197318084453\n",
      "Epoch: 406 Iteration: 38600. Train_MSE: 0.0002034229109995067. Test_MSE: 0.00016327927385046778\n",
      "Epoch: 407 Iteration: 38700. Train_MSE: 0.00020858246716670692. Test_MSE: 0.00016302160760848591\n",
      "Epoch: 408 Iteration: 38800. Train_MSE: 0.0003682314127217978. Test_MSE: 0.00016311142308235267\n",
      "Epoch: 409 Iteration: 38900. Train_MSE: 0.000461541028926149. Test_MSE: 0.0001633810928179264\n",
      "Epoch: 410 Iteration: 39000. Train_MSE: 0.0002948588225990534. Test_MSE: 0.00016226046612742586\n",
      "Epoch: 411 Iteration: 39100. Train_MSE: 0.0004420092736836523. Test_MSE: 0.00016215993459509022\n",
      "Epoch: 412 Iteration: 39200. Train_MSE: 0.0006313765770755708. Test_MSE: 0.0001632658305995945\n",
      "Epoch: 413 Iteration: 39300. Train_MSE: 0.0004811494145542383. Test_MSE: 0.0001640607195085365\n",
      "Epoch: 414 Iteration: 39400. Train_MSE: 0.0007362125325016677. Test_MSE: 0.00016410125703813673\n",
      "Epoch: 415 Iteration: 39500. Train_MSE: 0.006282916758209467. Test_MSE: 0.00016347092405379654\n",
      "Epoch: 416 Iteration: 39600. Train_MSE: 0.0002309500559931621. Test_MSE: 0.00016323071323998784\n",
      "Epoch: 417 Iteration: 39700. Train_MSE: 0.00039481426938436925. Test_MSE: 0.0001628264309453756\n",
      "Epoch: 418 Iteration: 39800. Train_MSE: 0.0004154325579293072. Test_MSE: 0.00016290251792109442\n",
      "Epoch: 420 Iteration: 39900. Train_MSE: 0.002056823344901204. Test_MSE: 0.00016198394318122936\n",
      "Epoch: 421 Iteration: 40000. Train_MSE: 0.001531846821308136. Test_MSE: 0.00016115854429923598\n",
      "Epoch: 422 Iteration: 40100. Train_MSE: 0.00028056083829142153. Test_MSE: 0.0001614333824703758\n",
      "Epoch: 423 Iteration: 40200. Train_MSE: 0.0004670830676332116. Test_MSE: 0.00016165135059932197\n",
      "Epoch: 424 Iteration: 40300. Train_MSE: 0.0008225227938964963. Test_MSE: 0.00016116005862538879\n",
      "Epoch: 425 Iteration: 40400. Train_MSE: 0.0006015735561959445. Test_MSE: 0.0001605541608305614\n",
      "Epoch: 426 Iteration: 40500. Train_MSE: 0.0001983096299227327. Test_MSE: 0.00015983113044543048\n",
      "Epoch: 427 Iteration: 40600. Train_MSE: 0.00020505762950051576. Test_MSE: 0.00015958584947984732\n",
      "Epoch: 428 Iteration: 40700. Train_MSE: 0.00036029116017743945. Test_MSE: 0.00015967598368749316\n",
      "Epoch: 429 Iteration: 40800. Train_MSE: 0.0004449003317859024. Test_MSE: 0.00015994187366509823\n",
      "Epoch: 430 Iteration: 40900. Train_MSE: 0.00028673914493992925. Test_MSE: 0.00015886300636919619\n",
      "Epoch: 431 Iteration: 41000. Train_MSE: 0.0004278702544979751. Test_MSE: 0.0001587666058643932\n",
      "Epoch: 432 Iteration: 41100. Train_MSE: 0.000617461686488241. Test_MSE: 0.00015984617122342213\n",
      "Epoch: 433 Iteration: 41200. Train_MSE: 0.00047423370415344834. Test_MSE: 0.00016062342544103316\n",
      "Epoch: 434 Iteration: 41300. Train_MSE: 0.0007304219761863351. Test_MSE: 0.0001606695380870407\n",
      "Epoch: 435 Iteration: 41400. Train_MSE: 0.006255617830902338. Test_MSE: 0.00016006372487548904\n",
      "Epoch: 436 Iteration: 41500. Train_MSE: 0.00022571247245650738. Test_MSE: 0.0001598460953592409\n",
      "Epoch: 437 Iteration: 41600. Train_MSE: 0.0003901792224496603. Test_MSE: 0.00015945175520908382\n",
      "Epoch: 438 Iteration: 41700. Train_MSE: 0.0004017669416498393. Test_MSE: 0.00015951976430436253\n",
      "Epoch: 440 Iteration: 41800. Train_MSE: 0.0020047009456902742. Test_MSE: 0.0001586254931311823\n",
      "Epoch: 441 Iteration: 41900. Train_MSE: 0.0015204207738861442. Test_MSE: 0.0001578203159622014\n",
      "Epoch: 442 Iteration: 42000. Train_MSE: 0.0002705022634472698. Test_MSE: 0.00015808389508795923\n",
      "Epoch: 443 Iteration: 42100. Train_MSE: 0.0004500440845731646. Test_MSE: 0.00015828982286716318\n",
      "Epoch: 444 Iteration: 42200. Train_MSE: 0.0008175926632247865. Test_MSE: 0.00015780646530653476\n",
      "Epoch: 445 Iteration: 42300. Train_MSE: 0.0005938164540566504. Test_MSE: 0.00015721952874777397\n",
      "Epoch: 446 Iteration: 42400. Train_MSE: 0.00019368749053683132. Test_MSE: 0.00015652293328083296\n",
      "Epoch: 447 Iteration: 42500. Train_MSE: 0.00020163845329079777. Test_MSE: 0.00015628748434179404\n",
      "Epoch: 448 Iteration: 42600. Train_MSE: 0.0003525294305291027. Test_MSE: 0.00015637660198348035\n",
      "Epoch: 449 Iteration: 42700. Train_MSE: 0.00042895186925306916. Test_MSE: 0.0001566370751145471\n",
      "Epoch: 450 Iteration: 42800. Train_MSE: 0.0002789878926705569. Test_MSE: 0.00015559781730182693\n",
      "Epoch: 451 Iteration: 42900. Train_MSE: 0.00041449072887189686. Test_MSE: 0.0001555056604900408\n",
      "Epoch: 452 Iteration: 43000. Train_MSE: 0.0006040605949237943. Test_MSE: 0.00015655978761787006\n",
      "Epoch: 453 Iteration: 43100. Train_MSE: 0.00046727678272873163. Test_MSE: 0.0001573201147619013\n",
      "Epoch: 454 Iteration: 43200. Train_MSE: 0.0007244761800393462. Test_MSE: 0.00015736958418431163\n",
      "Epoch: 455 Iteration: 43300. Train_MSE: 0.006229899358004332. Test_MSE: 0.00015678569637379576\n",
      "Epoch: 456 Iteration: 43400. Train_MSE: 0.0002204177581006661. Test_MSE: 0.00015658791153634673\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 457 Iteration: 43500. Train_MSE: 0.00038583643618039787. Test_MSE: 0.00015620328932512505\n",
      "Epoch: 458 Iteration: 43600. Train_MSE: 0.00038885034155100584. Test_MSE: 0.0001562634096935487\n",
      "Epoch: 460 Iteration: 43700. Train_MSE: 0.00195383676327765. Test_MSE: 0.0001553918435351317\n",
      "Epoch: 461 Iteration: 43800. Train_MSE: 0.0015093302354216576. Test_MSE: 0.00015460818679255244\n",
      "Epoch: 462 Iteration: 43900. Train_MSE: 0.0002613019896671176. Test_MSE: 0.00015486099957438056\n",
      "Epoch: 463 Iteration: 44000. Train_MSE: 0.0004344593035057187. Test_MSE: 0.0001550552340678561\n",
      "Epoch: 464 Iteration: 44100. Train_MSE: 0.0008132460643537343. Test_MSE: 0.0001545793447642016\n",
      "Epoch: 465 Iteration: 44200. Train_MSE: 0.0005866079591214657. Test_MSE: 0.00015401031681565558\n",
      "Epoch: 466 Iteration: 44300. Train_MSE: 0.0001894681336125359. Test_MSE: 0.00015334178532549293\n",
      "Epoch: 467 Iteration: 44400. Train_MSE: 0.00019798538414761424. Test_MSE: 0.00015311659669893244\n",
      "Epoch: 468 Iteration: 44500. Train_MSE: 0.00034470189712010324. Test_MSE: 0.00015320505065974114\n",
      "Epoch: 469 Iteration: 44600. Train_MSE: 0.00041370224789716303. Test_MSE: 0.00015346149366760267\n",
      "Epoch: 470 Iteration: 44700. Train_MSE: 0.00027155529824085534. Test_MSE: 0.00015246193873131276\n",
      "Epoch: 471 Iteration: 44800. Train_MSE: 0.0004020040505565703. Test_MSE: 0.00015237439637726558\n",
      "Epoch: 472 Iteration: 44900. Train_MSE: 0.0005910939071327448. Test_MSE: 0.00015340370896316713\n",
      "Epoch: 473 Iteration: 45000. Train_MSE: 0.00046036471030674875. Test_MSE: 0.000154146887860763\n",
      "Epoch: 474 Iteration: 45100. Train_MSE: 0.0007186840521171689. Test_MSE: 0.00015420040797132253\n",
      "Epoch: 475 Iteration: 45200. Train_MSE: 0.006205440033227205. Test_MSE: 0.00015363678437882344\n",
      "Epoch: 476 Iteration: 45300. Train_MSE: 0.00021543062757700682. Test_MSE: 0.0001534581105673699\n",
      "Epoch: 477 Iteration: 45400. Train_MSE: 0.00038175209192559123. Test_MSE: 0.0001530831812096567\n",
      "Epoch: 478 Iteration: 45500. Train_MSE: 0.00037672650068998337. Test_MSE: 0.00015313584071121632\n",
      "Epoch: 480 Iteration: 45600. Train_MSE: 0.0019047423265874386. Test_MSE: 0.00015228498712945392\n",
      "Epoch: 481 Iteration: 45700. Train_MSE: 0.0014985634479671717. Test_MSE: 0.00015152093266470628\n",
      "Epoch: 482 Iteration: 45800. Train_MSE: 0.000252860423643142. Test_MSE: 0.00015176266801651968\n",
      "Epoch: 483 Iteration: 45900. Train_MSE: 0.00041986111318692565. Test_MSE: 0.0001519439790192794\n",
      "Epoch: 484 Iteration: 46000. Train_MSE: 0.0008093643118627369. Test_MSE: 0.00015147573758533993\n",
      "Epoch: 485 Iteration: 46100. Train_MSE: 0.0005799475475214422. Test_MSE: 0.00015092433148509955\n",
      "Epoch: 486 Iteration: 46200. Train_MSE: 0.00018568265659268945. Test_MSE: 0.00015028168405039043\n",
      "Epoch: 487 Iteration: 46300. Train_MSE: 0.00019435712601989508. Test_MSE: 0.00015006638865365496\n",
      "Epoch: 488 Iteration: 46400. Train_MSE: 0.0003368405159562826. Test_MSE: 0.00015015468100140268\n",
      "Epoch: 489 Iteration: 46500. Train_MSE: 0.00039900882984511554. Test_MSE: 0.0001504079953319218\n",
      "Epoch: 490 Iteration: 46600. Train_MSE: 0.0002643312036525458. Test_MSE: 0.00014944783510300233\n",
      "Epoch: 491 Iteration: 46700. Train_MSE: 0.00039034293149597943. Test_MSE: 0.00014936753807610575\n",
      "Epoch: 492 Iteration: 46800. Train_MSE: 0.0005785130779258907. Test_MSE: 0.0001503733025636169\n",
      "Epoch: 493 Iteration: 46900. Train_MSE: 0.00045339754433371127. Test_MSE: 0.00015109818158793993\n",
      "Epoch: 494 Iteration: 47000. Train_MSE: 0.0007128491415642202. Test_MSE: 0.00015115239007781024\n",
      "Epoch: 495 Iteration: 47100. Train_MSE: 0.006183560937643051. Test_MSE: 0.0001506053509602588\n",
      "Epoch: 496 Iteration: 47200. Train_MSE: 0.0002108722401317209. Test_MSE: 0.00015044100247634044\n",
      "Epoch: 497 Iteration: 47300. Train_MSE: 0.00037794242962263525. Test_MSE: 0.0001500715299878489\n",
      "Epoch: 498 Iteration: 47400. Train_MSE: 0.00036567103234119713. Test_MSE: 0.0001501148909932013\n",
      "Epoch: 500 Iteration: 47500. Train_MSE: 0.0018571476684883237. Test_MSE: 0.0001492830804585293\n",
      "Epoch: 501 Iteration: 47600. Train_MSE: 0.0014880555681884289. Test_MSE: 0.00014853570600026897\n",
      "Epoch: 502 Iteration: 47700. Train_MSE: 0.00024490500800311565. Test_MSE: 0.000148764996221291\n",
      "Epoch: 503 Iteration: 47800. Train_MSE: 0.0004062491643708199. Test_MSE: 0.00014893284305228359\n",
      "Epoch: 504 Iteration: 47900. Train_MSE: 0.0008059445535764098. Test_MSE: 0.00014846746400329377\n",
      "Epoch: 505 Iteration: 48000. Train_MSE: 0.000573841156437993. Test_MSE: 0.00014792758314092371\n",
      "Epoch: 506 Iteration: 48100. Train_MSE: 0.00018223602091893554. Test_MSE: 0.0001473034237491213\n",
      "Epoch: 507 Iteration: 48200. Train_MSE: 0.0001909171842271462. Test_MSE: 0.00014709328095726906\n",
      "Epoch: 508 Iteration: 48300. Train_MSE: 0.0003291891480330378. Test_MSE: 0.00014717880979550404\n",
      "Epoch: 509 Iteration: 48400. Train_MSE: 0.00038509705336764455. Test_MSE: 0.00014742595825361317\n",
      "Epoch: 510 Iteration: 48500. Train_MSE: 0.00025745079619809985. Test_MSE: 0.00014649679210804487\n",
      "Epoch: 511 Iteration: 48600. Train_MSE: 0.0003794830699916929. Test_MSE: 0.00014641815334445662\n",
      "Epoch: 512 Iteration: 48700. Train_MSE: 0.0005662794574163854. Test_MSE: 0.0001474002357427145\n",
      "Epoch: 513 Iteration: 48800. Train_MSE: 0.0004463837540242821. Test_MSE: 0.00014810985334793423\n",
      "Epoch: 514 Iteration: 48900. Train_MSE: 0.0007070499123074114. Test_MSE: 0.00014816834709305786\n",
      "Epoch: 515 Iteration: 49000. Train_MSE: 0.006163288373500109. Test_MSE: 0.00014764074569530131\n",
      "Epoch: 516 Iteration: 49100. Train_MSE: 0.0002067931927740574. Test_MSE: 0.00014749289903355544\n",
      "Epoch: 517 Iteration: 49200. Train_MSE: 0.0003744099522009492. Test_MSE: 0.0001471328012121575\n",
      "Epoch: 518 Iteration: 49300. Train_MSE: 0.0003552946727722883. Test_MSE: 0.00014717130148468434\n",
      "Epoch: 520 Iteration: 49400. Train_MSE: 0.0018112696707248688. Test_MSE: 0.0001463620671552582\n",
      "Epoch: 521 Iteration: 49500. Train_MSE: 0.0014777560718357563. Test_MSE: 0.0001456346813236987\n",
      "Epoch: 522 Iteration: 49600. Train_MSE: 0.00023758549650665373. Test_MSE: 0.0001458543411464331\n",
      "Epoch: 523 Iteration: 49700. Train_MSE: 0.00039365983684547246. Test_MSE: 0.00014601163860453412\n",
      "Epoch: 524 Iteration: 49800. Train_MSE: 0.0008026573341339827. Test_MSE: 0.00014555315029531307\n",
      "Epoch: 525 Iteration: 49900. Train_MSE: 0.0005681298789568245. Test_MSE: 0.00014502868709737193\n",
      "Epoch: 526 Iteration: 50000. Train_MSE: 0.0001790655660443008. Test_MSE: 0.00014442664170100233\n",
      "Epoch: 527 Iteration: 50100. Train_MSE: 0.00018759898375719786. Test_MSE: 0.00014422445201149817\n",
      "Epoch: 528 Iteration: 50200. Train_MSE: 0.00032179642585106194. Test_MSE: 0.0001443095488292989\n",
      "Epoch: 529 Iteration: 50300. Train_MSE: 0.00037183149834163487. Test_MSE: 0.00014455280503668816\n",
      "Epoch: 530 Iteration: 50400. Train_MSE: 0.00025084312073886395. Test_MSE: 0.00014365873580614395\n",
      "Epoch: 531 Iteration: 50500. Train_MSE: 0.0003693414037115872. Test_MSE: 0.00014358604878857752\n",
      "Epoch: 532 Iteration: 50600. Train_MSE: 0.0005545933963730931. Test_MSE: 0.0001445454329777445\n",
      "Epoch: 533 Iteration: 50700. Train_MSE: 0.0004394963616505265. Test_MSE: 0.00014523944795509988\n",
      "Epoch: 534 Iteration: 50800. Train_MSE: 0.0007014876464381814. Test_MSE: 0.0001453000466066821\n",
      "Epoch: 535 Iteration: 50900. Train_MSE: 0.006143955979496241. Test_MSE: 0.00014479075313326813\n",
      "Epoch: 536 Iteration: 51000. Train_MSE: 0.00020302853954490274. Test_MSE: 0.0001446594323840063\n",
      "Epoch: 537 Iteration: 51100. Train_MSE: 0.00037106822128407657. Test_MSE: 0.0001443103628853928\n",
      "Epoch: 538 Iteration: 51200. Train_MSE: 0.00034563045483082533. Test_MSE: 0.00014434653910334885\n",
      "Epoch: 540 Iteration: 51300. Train_MSE: 0.001766656176187098. Test_MSE: 0.00014356053047780398\n",
      "Epoch: 541 Iteration: 51400. Train_MSE: 0.0014677586732432246. Test_MSE: 0.00014285514437303664\n",
      "Epoch: 542 Iteration: 51500. Train_MSE: 0.00023083816631697118. Test_MSE: 0.00014306822780788336\n",
      "Epoch: 543 Iteration: 51600. Train_MSE: 0.0003819815465249121. Test_MSE: 0.00014321802789776856\n",
      "Epoch: 544 Iteration: 51700. Train_MSE: 0.0007986818091012537. Test_MSE: 0.00014276921782833233\n",
      "Epoch: 545 Iteration: 51800. Train_MSE: 0.0005627732607536018. Test_MSE: 0.0001422624995176988\n",
      "Epoch: 546 Iteration: 51900. Train_MSE: 0.00017616013064980507. Test_MSE: 0.0001416842321051082\n",
      "Epoch: 547 Iteration: 52000. Train_MSE: 0.0001843954814830795. Test_MSE: 0.00014149307945696024\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 548 Iteration: 52100. Train_MSE: 0.0003146012022625655. Test_MSE: 0.00014158116699366222\n",
      "Epoch: 549 Iteration: 52200. Train_MSE: 0.000359176512574777. Test_MSE: 0.00014182259387594644\n",
      "Epoch: 550 Iteration: 52300. Train_MSE: 0.0002444964775349945. Test_MSE: 0.00014096446285863539\n",
      "Epoch: 551 Iteration: 52400. Train_MSE: 0.0003597362956497818. Test_MSE: 0.0001408982503714763\n",
      "Epoch: 552 Iteration: 52500. Train_MSE: 0.0005432439502328634. Test_MSE: 0.00014183632217048544\n",
      "Epoch: 553 Iteration: 52600. Train_MSE: 0.0004326476773712784. Test_MSE: 0.00014251615833199959\n",
      "Epoch: 554 Iteration: 52700. Train_MSE: 0.0006959789316169918. Test_MSE: 0.00014258013110574392\n",
      "Epoch: 555 Iteration: 52800. Train_MSE: 0.006125730462372303. Test_MSE: 0.00014208841615013788\n",
      "Epoch: 556 Iteration: 52900. Train_MSE: 0.00019951650756411254. Test_MSE: 0.00014197230960479395\n",
      "Epoch: 557 Iteration: 53000. Train_MSE: 0.0003679046640172601. Test_MSE: 0.00014163348485370135\n",
      "Epoch: 558 Iteration: 53100. Train_MSE: 0.0003364012809470296. Test_MSE: 0.00014166550829843459\n",
      "Epoch: 560 Iteration: 53200. Train_MSE: 0.0017234673723578453. Test_MSE: 0.00014090221474626256\n",
      "Epoch: 561 Iteration: 53300. Train_MSE: 0.0014581530122086406. Test_MSE: 0.00014021573730186711\n",
      "Epoch: 562 Iteration: 53400. Train_MSE: 0.00022458445164375007. Test_MSE: 0.00014042082766984178\n",
      "Epoch: 563 Iteration: 53500. Train_MSE: 0.0003709550073835999. Test_MSE: 0.0001405614521865861\n",
      "Epoch: 564 Iteration: 53600. Train_MSE: 0.0007949545979499817. Test_MSE: 0.0001401201547150426\n",
      "Epoch: 565 Iteration: 53700. Train_MSE: 0.0005577580886892974. Test_MSE: 0.0001396281522204572\n",
      "Epoch: 566 Iteration: 53800. Train_MSE: 0.00017346230742987245. Test_MSE: 0.00013907087946053414\n",
      "Epoch: 567 Iteration: 53900. Train_MSE: 0.00018130482931155711. Test_MSE: 0.00013888738262006015\n",
      "Epoch: 568 Iteration: 54000. Train_MSE: 0.00030755632906220853. Test_MSE: 0.00013897472216681454\n",
      "Epoch: 569 Iteration: 54100. Train_MSE: 0.0003470472292974591. Test_MSE: 0.00013921285128813156\n",
      "Epoch: 570 Iteration: 54200. Train_MSE: 0.0002384057588642463. Test_MSE: 0.00013838958451943166\n",
      "Epoch: 571 Iteration: 54300. Train_MSE: 0.0003506630309857428. Test_MSE: 0.00013832988703829595\n",
      "Epoch: 572 Iteration: 54400. Train_MSE: 0.0005322463694028556. Test_MSE: 0.00013924436459627023\n",
      "Epoch: 573 Iteration: 54500. Train_MSE: 0.00042591485544107854. Test_MSE: 0.00013990780890917262\n",
      "Epoch: 574 Iteration: 54600. Train_MSE: 0.0006905618938617408. Test_MSE: 0.00013997344487765123\n",
      "Epoch: 575 Iteration: 54700. Train_MSE: 0.00610828772187233. Test_MSE: 0.00013949783386096632\n",
      "Epoch: 576 Iteration: 54800. Train_MSE: 0.0001962210808414966. Test_MSE: 0.00013939540860137244\n",
      "Epoch: 577 Iteration: 54900. Train_MSE: 0.0003648724814411253. Test_MSE: 0.00013906526057525605\n",
      "Epoch: 578 Iteration: 55000. Train_MSE: 0.0003275918716099113. Test_MSE: 0.0001390928241829887\n",
      "Epoch: 580 Iteration: 55100. Train_MSE: 0.0016815678682178259. Test_MSE: 0.00013835093035445613\n",
      "Epoch: 581 Iteration: 55200. Train_MSE: 0.001448855153284967. Test_MSE: 0.00013768401907032532\n",
      "Epoch: 582 Iteration: 55300. Train_MSE: 0.00021874757658224553. Test_MSE: 0.00013788160690789708\n",
      "Epoch: 583 Iteration: 55400. Train_MSE: 0.00036054995143786073. Test_MSE: 0.00013801495058214007\n",
      "Epoch: 584 Iteration: 55500. Train_MSE: 0.0007914479356259108. Test_MSE: 0.00013758209995292636\n",
      "Epoch: 585 Iteration: 55600. Train_MSE: 0.0005530111375264823. Test_MSE: 0.00013710455064154623\n",
      "Epoch: 586 Iteration: 55700. Train_MSE: 0.00017094353097490966. Test_MSE: 0.00013656647289674086\n",
      "Epoch: 587 Iteration: 55800. Train_MSE: 0.0001783408224582672. Test_MSE: 0.0001363903928872597\n",
      "Epoch: 588 Iteration: 55900. Train_MSE: 0.0003007561608683318. Test_MSE: 0.0001364779642631207\n",
      "Epoch: 589 Iteration: 56000. Train_MSE: 0.00033546279883012176. Test_MSE: 0.00013671359015382533\n",
      "Epoch: 590 Iteration: 56100. Train_MSE: 0.0002325225214008242. Test_MSE: 0.00013592288151525995\n",
      "Epoch: 591 Iteration: 56200. Train_MSE: 0.0003420737339183688. Test_MSE: 0.00013586832568381597\n",
      "Epoch: 592 Iteration: 56300. Train_MSE: 0.000521583016961813. Test_MSE: 0.00013676165196067666\n",
      "Epoch: 593 Iteration: 56400. Train_MSE: 0.00041924259858205914. Test_MSE: 0.00013741135893177825\n",
      "Epoch: 594 Iteration: 56500. Train_MSE: 0.0006853108643554151. Test_MSE: 0.000137479570528059\n",
      "Epoch: 595 Iteration: 56600. Train_MSE: 0.006091575138270855. Test_MSE: 0.0001370205215924732\n",
      "Epoch: 596 Iteration: 56700. Train_MSE: 0.0001931215520016849. Test_MSE: 0.00013693180276105706\n",
      "Epoch: 597 Iteration: 56800. Train_MSE: 0.00036193602136336267. Test_MSE: 0.0001366120399901653\n",
      "Epoch: 598 Iteration: 56900. Train_MSE: 0.0003191417781636119. Test_MSE: 0.0001366373583369717\n",
      "Epoch: 600 Iteration: 57000. Train_MSE: 0.0016413013217970729. Test_MSE: 0.0001359169038624123\n",
      "Epoch: 601 Iteration: 57100. Train_MSE: 0.001439957763068378. Test_MSE: 0.00013526849626410597\n",
      "Epoch: 602 Iteration: 57200. Train_MSE: 0.00021323283726815134. Test_MSE: 0.00013545882026673973\n",
      "Epoch: 603 Iteration: 57300. Train_MSE: 0.0003508775553200394. Test_MSE: 0.00013558371355312882\n",
      "Epoch: 604 Iteration: 57400. Train_MSE: 0.0007882151985540986. Test_MSE: 0.00013515869411681984\n",
      "Epoch: 605 Iteration: 57500. Train_MSE: 0.0005483473651111126. Test_MSE: 0.00013469518484592627\n",
      "Epoch: 606 Iteration: 57600. Train_MSE: 0.00016856510774232447. Test_MSE: 0.00013417655375354487\n",
      "Epoch: 607 Iteration: 57700. Train_MSE: 0.00017548822506796569. Test_MSE: 0.0001340084076244454\n",
      "Epoch: 608 Iteration: 57800. Train_MSE: 0.000294151424895972. Test_MSE: 0.000134096449368354\n",
      "Epoch: 609 Iteration: 57900. Train_MSE: 0.0003244771796744317. Test_MSE: 0.00013432888397415184\n",
      "Epoch: 610 Iteration: 58000. Train_MSE: 0.00022688310127705336. Test_MSE: 0.00013356925768518165\n",
      "Epoch: 611 Iteration: 58100. Train_MSE: 0.00033389159943908453. Test_MSE: 0.00013351985942958722\n",
      "Epoch: 612 Iteration: 58200. Train_MSE: 0.0005112412036396563. Test_MSE: 0.00013439292475916727\n",
      "Epoch: 613 Iteration: 58300. Train_MSE: 0.00041259994031861424. Test_MSE: 0.00013502922053201997\n",
      "Epoch: 614 Iteration: 58400. Train_MSE: 0.0006801920244470239. Test_MSE: 0.00013510007665979947\n",
      "Epoch: 615 Iteration: 58500. Train_MSE: 0.006075550336390734. Test_MSE: 0.00013465616247901452\n",
      "Epoch: 616 Iteration: 58600. Train_MSE: 0.00019019054889213294. Test_MSE: 0.00013458038717283245\n",
      "Epoch: 617 Iteration: 58700. Train_MSE: 0.0003591389977373183. Test_MSE: 0.00013427053645608593\n",
      "Epoch: 618 Iteration: 58800. Train_MSE: 0.00031103380024433136. Test_MSE: 0.00013429376224543998\n",
      "Epoch: 620 Iteration: 58900. Train_MSE: 0.0016029604012146592. Test_MSE: 0.00013359459671259406\n",
      "Epoch: 621 Iteration: 59000. Train_MSE: 0.0014316645683720708. Test_MSE: 0.00013296374799006439\n",
      "Epoch: 622 Iteration: 59100. Train_MSE: 0.00020799419144168496. Test_MSE: 0.00013314824189373118\n",
      "Epoch: 623 Iteration: 59200. Train_MSE: 0.0003416727122385055. Test_MSE: 0.0001332671225642555\n",
      "Epoch: 624 Iteration: 59300. Train_MSE: 0.0007851689588278532. Test_MSE: 0.00013285011840611762\n",
      "Epoch: 625 Iteration: 59400. Train_MSE: 0.0005439022206701338. Test_MSE: 0.00013239993719588652\n",
      "Epoch: 626 Iteration: 59500. Train_MSE: 0.00016631039034109563. Test_MSE: 0.00013189897894455097\n",
      "Epoch: 627 Iteration: 59600. Train_MSE: 0.00017270118405576795. Test_MSE: 0.00013173811486685022\n",
      "Epoch: 628 Iteration: 59700. Train_MSE: 0.0002876519865822047. Test_MSE: 0.00013182615503064125\n",
      "Epoch: 629 Iteration: 59800. Train_MSE: 0.0003141078632324934. Test_MSE: 0.00013205527949352127\n",
      "Epoch: 630 Iteration: 59900. Train_MSE: 0.0002216049615526572. Test_MSE: 0.00013132321575785893\n",
      "Epoch: 631 Iteration: 60000. Train_MSE: 0.00032607780303806067. Test_MSE: 0.0001312772735463612\n",
      "Epoch: 632 Iteration: 60100. Train_MSE: 0.0005012204637750983. Test_MSE: 0.00013213035762357257\n",
      "Epoch: 633 Iteration: 60200. Train_MSE: 0.00040609337156638503. Test_MSE: 0.00013275245825077932\n",
      "Epoch: 634 Iteration: 60300. Train_MSE: 0.0006752359913662076. Test_MSE: 0.00013282424363194608\n",
      "Epoch: 635 Iteration: 60400. Train_MSE: 0.006060205865651369. Test_MSE: 0.00013239369743596737\n",
      "Epoch: 636 Iteration: 60500. Train_MSE: 0.0001874157605925575. Test_MSE: 0.00013232888958591784\n",
      "Epoch: 637 Iteration: 60600. Train_MSE: 0.0003564271319191903. Test_MSE: 0.0001320267575537506\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 638 Iteration: 60700. Train_MSE: 0.0003032798122148961. Test_MSE: 0.00013204677923381222\n",
      "Epoch: 640 Iteration: 60800. Train_MSE: 0.001566331833600998. Test_MSE: 0.00013136573218279924\n",
      "Epoch: 641 Iteration: 60900. Train_MSE: 0.001423763227649033. Test_MSE: 0.00013075081239087861\n",
      "Epoch: 642 Iteration: 61000. Train_MSE: 0.00020306295482441783. Test_MSE: 0.00013092805132282253\n",
      "Epoch: 643 Iteration: 61100. Train_MSE: 0.00033299042843282223. Test_MSE: 0.0001310393452047189\n",
      "Epoch: 644 Iteration: 61200. Train_MSE: 0.0007824111962690949. Test_MSE: 0.00013062942707599764\n",
      "Epoch: 645 Iteration: 61300. Train_MSE: 0.000539609172847122. Test_MSE: 0.00013019156456670403\n",
      "Epoch: 646 Iteration: 61400. Train_MSE: 0.00016415829304605722. Test_MSE: 0.0001297070989652776\n",
      "Epoch: 647 Iteration: 61500. Train_MSE: 0.00016986210539471358. Test_MSE: 0.00012955251691092187\n",
      "Epoch: 648 Iteration: 61600. Train_MSE: 0.0002811949234455824. Test_MSE: 0.00012964116439530496\n",
      "Epoch: 649 Iteration: 61700. Train_MSE: 0.00030427536694332957. Test_MSE: 0.0001298668196190568\n",
      "Epoch: 650 Iteration: 61800. Train_MSE: 0.00021655892487615347. Test_MSE: 0.00012916157618974588\n",
      "Epoch: 651 Iteration: 61900. Train_MSE: 0.00031864328775554895. Test_MSE: 0.00012911836332607488\n",
      "Epoch: 652 Iteration: 62000. Train_MSE: 0.0004915444878861308. Test_MSE: 0.00012995166591358155\n",
      "Epoch: 653 Iteration: 62100. Train_MSE: 0.0003996671293862164. Test_MSE: 0.00013055928524337393\n",
      "Epoch: 654 Iteration: 62200. Train_MSE: 0.0006704162224195898. Test_MSE: 0.0001306309994091896\n",
      "Epoch: 655 Iteration: 62300. Train_MSE: 0.006045441143214703. Test_MSE: 0.00013021304418813873\n",
      "Epoch: 656 Iteration: 62400. Train_MSE: 0.00018477969570085406. Test_MSE: 0.00013015876201422866\n",
      "Epoch: 657 Iteration: 62500. Train_MSE: 0.00035374227445572615. Test_MSE: 0.0001298648061582318\n",
      "Epoch: 658 Iteration: 62600. Train_MSE: 0.000295912817819044. Test_MSE: 0.00012988186997946156\n",
      "Epoch: 660 Iteration: 62700. Train_MSE: 0.0015312833711504936. Test_MSE: 0.00012921828323989254\n",
      "Epoch: 661 Iteration: 62800. Train_MSE: 0.001416174927726388. Test_MSE: 0.00012861758759614167\n",
      "Epoch: 662 Iteration: 62900. Train_MSE: 0.000198419249500148. Test_MSE: 0.00012878775803512173\n",
      "Epoch: 663 Iteration: 63000. Train_MSE: 0.00032475352054461837. Test_MSE: 0.00012889175057107533\n",
      "Epoch: 664 Iteration: 63100. Train_MSE: 0.0007799041923135519. Test_MSE: 0.0001284870734044062\n",
      "Epoch: 665 Iteration: 63200. Train_MSE: 0.0005355319590307772. Test_MSE: 0.00012805922717221824\n",
      "Epoch: 666 Iteration: 63300. Train_MSE: 0.00016212023911066353. Test_MSE: 0.00012758915554691958\n",
      "Epoch: 667 Iteration: 63400. Train_MSE: 0.0001670697529334575. Test_MSE: 0.00012743959322443\n",
      "Epoch: 668 Iteration: 63500. Train_MSE: 0.0002749022387433797. Test_MSE: 0.00012752737974967891\n",
      "Epoch: 669 Iteration: 63600. Train_MSE: 0.00029490332235582173. Test_MSE: 0.000127748777303\n",
      "Epoch: 670 Iteration: 63700. Train_MSE: 0.00021175634174142033. Test_MSE: 0.00012706633385601397\n",
      "Epoch: 671 Iteration: 63800. Train_MSE: 0.00031153831514529884. Test_MSE: 0.00012702718151401735\n",
      "Epoch: 672 Iteration: 63900. Train_MSE: 0.0004821566108148545. Test_MSE: 0.00012784313976932046\n",
      "Epoch: 673 Iteration: 64000. Train_MSE: 0.00039335779729299247. Test_MSE: 0.00012843851167210497\n",
      "Epoch: 674 Iteration: 64100. Train_MSE: 0.0006656415062025189. Test_MSE: 0.00012851165111667145\n",
      "Epoch: 675 Iteration: 64200. Train_MSE: 0.006031287368386984. Test_MSE: 0.00012810570816874833\n",
      "Epoch: 676 Iteration: 64300. Train_MSE: 0.00018228126282338053. Test_MSE: 0.00012806039902013965\n",
      "Epoch: 677 Iteration: 64400. Train_MSE: 0.000351163384038955. Test_MSE: 0.00012777334280544681\n",
      "Epoch: 678 Iteration: 64500. Train_MSE: 0.0002888467279262841. Test_MSE: 0.00012778745945512518\n",
      "Epoch: 680 Iteration: 64600. Train_MSE: 0.0014976955717429519. Test_MSE: 0.0001271390942636963\n",
      "Epoch: 681 Iteration: 64700. Train_MSE: 0.0014089171309024096. Test_MSE: 0.0001265522464076037\n",
      "Epoch: 682 Iteration: 64800. Train_MSE: 0.00019396733841858804. Test_MSE: 0.0001267158539781965\n",
      "Epoch: 683 Iteration: 64900. Train_MSE: 0.00031681422842666507. Test_MSE: 0.00012681344146801176\n",
      "Epoch: 684 Iteration: 65000. Train_MSE: 0.0007775957346893847. Test_MSE: 0.0001264158769173105\n",
      "Epoch: 685 Iteration: 65100. Train_MSE: 0.0005316651659086347. Test_MSE: 0.0001259996152594355\n",
      "Epoch: 686 Iteration: 65200. Train_MSE: 0.00016014455468393862. Test_MSE: 0.00012554514769192167\n",
      "Epoch: 687 Iteration: 65300. Train_MSE: 0.00016437159501947463. Test_MSE: 0.0001254014942919627\n",
      "Epoch: 688 Iteration: 65400. Train_MSE: 0.00026879223878495395. Test_MSE: 0.0001254896737954075\n",
      "Epoch: 689 Iteration: 65500. Train_MSE: 0.0002860135573428124. Test_MSE: 0.00012570799799453767\n",
      "Epoch: 690 Iteration: 65600. Train_MSE: 0.0002071814815280959. Test_MSE: 0.00012505043943150405\n",
      "Epoch: 691 Iteration: 65700. Train_MSE: 0.0003047685604542494. Test_MSE: 0.00012501498447983937\n",
      "Epoch: 692 Iteration: 65800. Train_MSE: 0.0004729889624286443. Test_MSE: 0.0001258129156667416\n",
      "Epoch: 693 Iteration: 65900. Train_MSE: 0.0003871298104058951. Test_MSE: 0.00012639552093561332\n",
      "Epoch: 694 Iteration: 66000. Train_MSE: 0.0006608620169572532. Test_MSE: 0.00012646976996452021\n",
      "Epoch: 695 Iteration: 66100. Train_MSE: 0.006017906591296196. Test_MSE: 0.00012607523364503606\n",
      "Epoch: 696 Iteration: 66200. Train_MSE: 0.00017994019435718656. Test_MSE: 0.00012603930929374883\n",
      "Epoch: 697 Iteration: 66300. Train_MSE: 0.00034860370215028524. Test_MSE: 0.0001257602176662548\n",
      "Epoch: 698 Iteration: 66400. Train_MSE: 0.00028195674531161785. Test_MSE: 0.00012577201125853107\n",
      "Epoch: 700 Iteration: 66500. Train_MSE: 0.0014652122044935822. Test_MSE: 0.00012514096791912477\n",
      "Epoch: 701 Iteration: 66600. Train_MSE: 0.0014018819201737642. Test_MSE: 0.00012456952548878377\n",
      "Epoch: 702 Iteration: 66700. Train_MSE: 0.00018960308807436377. Test_MSE: 0.00012472836918606746\n",
      "Epoch: 703 Iteration: 66800. Train_MSE: 0.000309059105347842. Test_MSE: 0.00012482082536410108\n",
      "Epoch: 704 Iteration: 66900. Train_MSE: 0.0007754543912597001. Test_MSE: 0.00012443088560831856\n",
      "Epoch: 705 Iteration: 67000. Train_MSE: 0.0005280097830109298. Test_MSE: 0.00012402649675670558\n",
      "Epoch: 706 Iteration: 67100. Train_MSE: 0.0001582575059728697. Test_MSE: 0.0001235879004409509\n",
      "Epoch: 707 Iteration: 67200. Train_MSE: 0.00016174324264284223. Test_MSE: 0.0001234513629271165\n",
      "Epoch: 708 Iteration: 67300. Train_MSE: 0.0002629179216455668. Test_MSE: 0.0001235406337679333\n",
      "Epoch: 709 Iteration: 67400. Train_MSE: 0.0002774501626845449. Test_MSE: 0.0001237563456461551\n",
      "Epoch: 710 Iteration: 67500. Train_MSE: 0.00020280867465771735. Test_MSE: 0.00012312289384815994\n",
      "Epoch: 711 Iteration: 67600. Train_MSE: 0.00029830785933882. Test_MSE: 0.00012309144016608002\n",
      "Epoch: 712 Iteration: 67700. Train_MSE: 0.0004640718980226666. Test_MSE: 0.00012387182535564272\n",
      "Epoch: 713 Iteration: 67800. Train_MSE: 0.0003810624766629189. Test_MSE: 0.0001244427869116938\n",
      "Epoch: 714 Iteration: 67900. Train_MSE: 0.0006561827030964196. Test_MSE: 0.00012451871771635613\n",
      "Epoch: 715 Iteration: 68000. Train_MSE: 0.0060048336163163185. Test_MSE: 0.0001241368714222712\n",
      "Epoch: 716 Iteration: 68100. Train_MSE: 0.00017767770623322576. Test_MSE: 0.00012411093884039974\n",
      "Epoch: 717 Iteration: 68200. Train_MSE: 0.000346107583027333. Test_MSE: 0.00012384039052937122\n",
      "Epoch: 718 Iteration: 68300. Train_MSE: 0.0002753550070337951. Test_MSE: 0.0001238516876691619\n",
      "Epoch: 720 Iteration: 68400. Train_MSE: 0.0014341879868879914. Test_MSE: 0.00012323800158371012\n",
      "Epoch: 721 Iteration: 68500. Train_MSE: 0.0013951760483905673. Test_MSE: 0.00012268031340283245\n",
      "Epoch: 722 Iteration: 68600. Train_MSE: 0.00018549970991443843. Test_MSE: 0.00012283336918787762\n",
      "Epoch: 723 Iteration: 68700. Train_MSE: 0.0003016693808604032. Test_MSE: 0.0001229206471784226\n",
      "Epoch: 724 Iteration: 68800. Train_MSE: 0.0007734459359198809. Test_MSE: 0.00012253805168806214\n",
      "Epoch: 725 Iteration: 68900. Train_MSE: 0.00052448088536039. Test_MSE: 0.00012214427435874704\n",
      "Epoch: 726 Iteration: 69000. Train_MSE: 0.0001564762060297653. Test_MSE: 0.00012171974980481416\n",
      "Epoch: 727 Iteration: 69100. Train_MSE: 0.00015913155220914632. Test_MSE: 0.00012158840143924403\n",
      "Epoch: 728 Iteration: 69200. Train_MSE: 0.00025720580015331507. Test_MSE: 0.00012167779953095245\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 729 Iteration: 69300. Train_MSE: 0.0002692553971428424. Test_MSE: 0.00012188970098903255\n",
      "Epoch: 730 Iteration: 69400. Train_MSE: 0.00019858083396684378. Test_MSE: 0.0001212784669732555\n",
      "Epoch: 731 Iteration: 69500. Train_MSE: 0.00029217160772532225. Test_MSE: 0.0001212500040684394\n",
      "Epoch: 732 Iteration: 69600. Train_MSE: 0.00045544723980128765. Test_MSE: 0.00012201217170778903\n",
      "Epoch: 733 Iteration: 69700. Train_MSE: 0.0003751017793547362. Test_MSE: 0.00012257062437290287\n",
      "Epoch: 734 Iteration: 69800. Train_MSE: 0.0006516056600958109. Test_MSE: 0.00012264679347087987\n",
      "Epoch: 735 Iteration: 69900. Train_MSE: 0.005992185324430466. Test_MSE: 0.00012227664338219273\n",
      "Epoch: 736 Iteration: 70000. Train_MSE: 0.0001755148114170879. Test_MSE: 0.00012225926819973263\n",
      "Epoch: 737 Iteration: 70100. Train_MSE: 0.00034359045093879104. Test_MSE: 0.00012199695812045174\n",
      "Epoch: 738 Iteration: 70200. Train_MSE: 0.00026901339879259467. Test_MSE: 0.0001220070406731167\n",
      "Epoch: 740 Iteration: 70300. Train_MSE: 0.001404157024808228. Test_MSE: 0.00012141191728044311\n",
      "Epoch: 741 Iteration: 70400. Train_MSE: 0.0013886182568967342. Test_MSE: 0.00012087002750905093\n",
      "Epoch: 742 Iteration: 70500. Train_MSE: 0.0001816538133425638. Test_MSE: 0.00012101831475133072\n",
      "Epoch: 743 Iteration: 70600. Train_MSE: 0.0002945927844848484. Test_MSE: 0.00012110106801152585\n",
      "Epoch: 744 Iteration: 70700. Train_MSE: 0.0007715683314017951. Test_MSE: 0.0001207266119525856\n",
      "Epoch: 745 Iteration: 70800. Train_MSE: 0.0005210955278016627. Test_MSE: 0.00012034435936248343\n",
      "Epoch: 746 Iteration: 70900. Train_MSE: 0.00015477900160476565. Test_MSE: 0.00011993515910610569\n",
      "Epoch: 747 Iteration: 71000. Train_MSE: 0.00015661520592402667. Test_MSE: 0.0001198102236773995\n",
      "Epoch: 748 Iteration: 71100. Train_MSE: 0.00025168084539473057. Test_MSE: 0.00011990022697134087\n",
      "Epoch: 749 Iteration: 71200. Train_MSE: 0.0002613764372654259. Test_MSE: 0.00012010922567492823\n",
      "Epoch: 750 Iteration: 71300. Train_MSE: 0.00019440651522018015. Test_MSE: 0.00011952075323786011\n",
      "Epoch: 751 Iteration: 71400. Train_MSE: 0.0002862818364519626. Test_MSE: 0.00011949520790997246\n",
      "Epoch: 752 Iteration: 71500. Train_MSE: 0.0004471053835004568. Test_MSE: 0.00012023899407930707\n",
      "Epoch: 753 Iteration: 71600. Train_MSE: 0.00036929099587723613. Test_MSE: 0.00012078512683849463\n",
      "Epoch: 754 Iteration: 71700. Train_MSE: 0.0006471374072134495. Test_MSE: 0.00012086215780916506\n",
      "Epoch: 755 Iteration: 71800. Train_MSE: 0.005980006884783506. Test_MSE: 0.0001205029295441339\n",
      "Epoch: 756 Iteration: 71900. Train_MSE: 0.00017346141976304352. Test_MSE: 0.00012049291612418465\n",
      "Epoch: 757 Iteration: 72000. Train_MSE: 0.0003411776851862669. Test_MSE: 0.00012023752538244643\n",
      "Epoch: 758 Iteration: 72100. Train_MSE: 0.000262886198470369. Test_MSE: 0.00012024495693035711\n",
      "Epoch: 760 Iteration: 72200. Train_MSE: 0.00137505738530308. Test_MSE: 0.00011966437893231184\n",
      "Epoch: 761 Iteration: 72300. Train_MSE: 0.0013821498723700643. Test_MSE: 0.00011913530597081257\n",
      "Epoch: 762 Iteration: 72400. Train_MSE: 0.00017800473142415285. Test_MSE: 0.00011927790256670093\n",
      "Epoch: 763 Iteration: 72500. Train_MSE: 0.0002878331288229674. Test_MSE: 0.00011935553798540943\n",
      "Epoch: 764 Iteration: 72600. Train_MSE: 0.0007697917753830552. Test_MSE: 0.00011898751929130437\n",
      "Epoch: 765 Iteration: 72700. Train_MSE: 0.000517841603141278. Test_MSE: 0.00011861545793903171\n",
      "Epoch: 766 Iteration: 72800. Train_MSE: 0.0001531745510874316. Test_MSE: 0.00011821969958416612\n",
      "Epoch: 767 Iteration: 72900. Train_MSE: 0.00015410092601086944. Test_MSE: 0.00011809951467325605\n",
      "Epoch: 768 Iteration: 73000. Train_MSE: 0.0002463260025251657. Test_MSE: 0.00011818974978193831\n",
      "Epoch: 769 Iteration: 73100. Train_MSE: 0.0002537931432016194. Test_MSE: 0.00011839487351646307\n",
      "Epoch: 770 Iteration: 73200. Train_MSE: 0.000190252554602921. Test_MSE: 0.00011782720473706353\n",
      "Epoch: 771 Iteration: 73300. Train_MSE: 0.0002806722477544099. Test_MSE: 0.00011780433531938407\n",
      "Epoch: 772 Iteration: 73400. Train_MSE: 0.00043905634083785117. Test_MSE: 0.00011853110362314486\n",
      "Epoch: 773 Iteration: 73500. Train_MSE: 0.00036360937519930303. Test_MSE: 0.00011906543316897934\n",
      "Epoch: 774 Iteration: 73600. Train_MSE: 0.000642788945697248. Test_MSE: 0.00011914272993327825\n",
      "Epoch: 775 Iteration: 73700. Train_MSE: 0.005968229845166206. Test_MSE: 0.00011879329577870547\n",
      "Epoch: 776 Iteration: 73800. Train_MSE: 0.00017150817438960075. Test_MSE: 0.0001187907245734797\n",
      "Epoch: 777 Iteration: 73900. Train_MSE: 0.00033885915763676167. Test_MSE: 0.00011854222901884309\n",
      "Epoch: 778 Iteration: 74000. Train_MSE: 0.0002569893258623779. Test_MSE: 0.00011854776575193396\n",
      "Epoch: 780 Iteration: 74100. Train_MSE: 0.0013468357501551509. Test_MSE: 0.00011798013882387021\n",
      "Epoch: 781 Iteration: 74200. Train_MSE: 0.0013759084977209568. Test_MSE: 0.00011746221798842162\n",
      "Epoch: 782 Iteration: 74300. Train_MSE: 0.00017455287161283195. Test_MSE: 0.00011759834909562659\n",
      "Epoch: 783 Iteration: 74400. Train_MSE: 0.000281413464108482. Test_MSE: 0.00011766962767211022\n",
      "Epoch: 784 Iteration: 74500. Train_MSE: 0.0007681444403715432. Test_MSE: 0.00011730620523780736\n",
      "Epoch: 785 Iteration: 74600. Train_MSE: 0.0005147303454577923. Test_MSE: 0.00011694182838213919\n",
      "Epoch: 786 Iteration: 74700. Train_MSE: 0.00015165786317083985. Test_MSE: 0.0001165571318330075\n",
      "Epoch: 787 Iteration: 74800. Train_MSE: 0.0001515720214229077. Test_MSE: 0.00011644039909791005\n",
      "Epoch: 788 Iteration: 74900. Train_MSE: 0.00024114109692163765. Test_MSE: 0.00011652928481980588\n",
      "Epoch: 789 Iteration: 75000. Train_MSE: 0.00024650568957440555. Test_MSE: 0.00011672965841353066\n",
      "Epoch: 790 Iteration: 75100. Train_MSE: 0.00018627196550369263. Test_MSE: 0.00011617920531888928\n",
      "Epoch: 791 Iteration: 75200. Train_MSE: 0.0002752513682935387. Test_MSE: 0.00011615623328282356\n",
      "Epoch: 792 Iteration: 75300. Train_MSE: 0.000431200722232461. Test_MSE: 0.00011686644179591221\n",
      "Epoch: 793 Iteration: 75400. Train_MSE: 0.0003580240299925208. Test_MSE: 0.00011738759172866745\n",
      "Epoch: 794 Iteration: 75500. Train_MSE: 0.0006384920561686158. Test_MSE: 0.00011746264384550807\n",
      "Epoch: 795 Iteration: 75600. Train_MSE: 0.005956994369626045. Test_MSE: 0.00011712023377181715\n",
      "Epoch: 796 Iteration: 75700. Train_MSE: 0.00016962508379947394. Test_MSE: 0.00011712220748106867\n",
      "Epoch: 797 Iteration: 75800. Train_MSE: 0.00033661533962003887. Test_MSE: 0.0001168778358872589\n",
      "Epoch: 798 Iteration: 75900. Train_MSE: 0.000251326069701463. Test_MSE: 0.00011688029354504398\n",
      "Epoch: 800 Iteration: 76000. Train_MSE: 0.0013195067876949906. Test_MSE: 0.0001163236730149526\n",
      "Epoch: 801 Iteration: 76100. Train_MSE: 0.001369820791296661. Test_MSE: 0.00011581496051876202\n",
      "Epoch: 802 Iteration: 76200. Train_MSE: 0.00017124399892054498. Test_MSE: 0.00011594406384039777\n",
      "Epoch: 803 Iteration: 76300. Train_MSE: 0.0002751847496256232. Test_MSE: 0.00011600885235165688\n",
      "Epoch: 804 Iteration: 76400. Train_MSE: 0.0007666207384318113. Test_MSE: 0.00011564909767385161\n",
      "Epoch: 805 Iteration: 76500. Train_MSE: 0.0005117654800415039. Test_MSE: 0.00011529144821466404\n",
      "Epoch: 806 Iteration: 76600. Train_MSE: 0.00015018259000498801. Test_MSE: 0.00011491519613833156\n",
      "Epoch: 807 Iteration: 76700. Train_MSE: 0.00014914694475010037. Test_MSE: 0.00011480001562200303\n",
      "Epoch: 808 Iteration: 76800. Train_MSE: 0.00023611733922734857. Test_MSE: 0.00011488682579967124\n",
      "Epoch: 809 Iteration: 76900. Train_MSE: 0.00023950943432282656. Test_MSE: 0.00011508153717317689\n",
      "Epoch: 810 Iteration: 77000. Train_MSE: 0.000182426068931818. Test_MSE: 0.00011454618433966343\n",
      "Epoch: 811 Iteration: 77100. Train_MSE: 0.0002700513869058341. Test_MSE: 0.00011452518116031852\n",
      "Epoch: 812 Iteration: 77200. Train_MSE: 0.0004235244996380061. Test_MSE: 0.00011521954040220238\n",
      "Epoch: 813 Iteration: 77300. Train_MSE: 0.00035249468055553734. Test_MSE: 0.00011572944251347756\n",
      "Epoch: 814 Iteration: 77400. Train_MSE: 0.0006342074484564364. Test_MSE: 0.00011580418177685139\n",
      "Epoch: 815 Iteration: 77500. Train_MSE: 0.005946205463260412. Test_MSE: 0.00011547019809374611\n",
      "Epoch: 816 Iteration: 77600. Train_MSE: 0.0001678427361184731. Test_MSE: 0.00011547813990300305\n",
      "Epoch: 817 Iteration: 77700. Train_MSE: 0.0003344264696352184. Test_MSE: 0.00011523985142545035\n",
      "Epoch: 818 Iteration: 77800. Train_MSE: 0.00024586162180639803. Test_MSE: 0.00011524125885724914\n",
      "Epoch: 820 Iteration: 77900. Train_MSE: 0.001293074688874185. Test_MSE: 0.00011469797563194386\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 821 Iteration: 78000. Train_MSE: 0.0013638773234561086. Test_MSE: 0.00011420055154136093\n",
      "Epoch: 822 Iteration: 78100. Train_MSE: 0.00016802609025035053. Test_MSE: 0.00011432506737334776\n",
      "Epoch: 823 Iteration: 78200. Train_MSE: 0.0002691279514692724. Test_MSE: 0.00011438545896568947\n",
      "Epoch: 824 Iteration: 78300. Train_MSE: 0.0007652035565115511. Test_MSE: 0.0001140318686228766\n",
      "Epoch: 825 Iteration: 78400. Train_MSE: 0.0005089493351988494. Test_MSE: 0.00011368266545091418\n",
      "Epoch: 826 Iteration: 78500. Train_MSE: 0.00014875733177177608. Test_MSE: 0.00011331808749549495\n",
      "Epoch: 827 Iteration: 78600. Train_MSE: 0.0001467832480557263. Test_MSE: 0.00011320816447821512\n",
      "Epoch: 828 Iteration: 78700. Train_MSE: 0.00023125018924474716. Test_MSE: 0.00011329564582290545\n",
      "Epoch: 829 Iteration: 78800. Train_MSE: 0.00023268992663361132. Test_MSE: 0.00011348721026038098\n",
      "Epoch: 830 Iteration: 78900. Train_MSE: 0.00017870383453555405. Test_MSE: 0.00011296993850907672\n",
      "Epoch: 831 Iteration: 79000. Train_MSE: 0.00026507058646529913. Test_MSE: 0.00011295109814225037\n",
      "Epoch: 832 Iteration: 79100. Train_MSE: 0.0004160402459092438. Test_MSE: 0.0001136310518404701\n",
      "Epoch: 833 Iteration: 79200. Train_MSE: 0.0003470757510513067. Test_MSE: 0.00011413054631853549\n",
      "Epoch: 834 Iteration: 79300. Train_MSE: 0.0006299508386291564. Test_MSE: 0.00011420545594085564\n",
      "Epoch: 835 Iteration: 79400. Train_MSE: 0.00593586964532733. Test_MSE: 0.00011387987497472483\n",
      "Epoch: 836 Iteration: 79500. Train_MSE: 0.00016615392814856023. Test_MSE: 0.00011389364409463581\n",
      "Epoch: 837 Iteration: 79600. Train_MSE: 0.0003322034317534417. Test_MSE: 0.0001136613293324296\n",
      "Epoch: 838 Iteration: 79700. Train_MSE: 0.00024058831331785768. Test_MSE: 0.00011366175609404955\n",
      "Epoch: 840 Iteration: 79800. Train_MSE: 0.0012676538899540901. Test_MSE: 0.00011313121444140719\n",
      "Epoch: 841 Iteration: 79900. Train_MSE: 0.0013581481762230396. Test_MSE: 0.00011264375704344592\n",
      "Epoch: 842 Iteration: 80000. Train_MSE: 0.0001649440237088129. Test_MSE: 0.00011276348248613657\n",
      "Epoch: 843 Iteration: 80100. Train_MSE: 0.0002633110270835459. Test_MSE: 0.0001128193626066368\n",
      "Epoch: 844 Iteration: 80200. Train_MSE: 0.0007638582028448582. Test_MSE: 0.00011247084911817703\n",
      "Epoch: 845 Iteration: 80300. Train_MSE: 0.0005060549592599273. Test_MSE: 0.00011212926573249261\n",
      "Epoch: 846 Iteration: 80400. Train_MSE: 0.0001473847805755213. Test_MSE: 0.00011177498317482117\n",
      "Epoch: 847 Iteration: 80500. Train_MSE: 0.00014448513684328645. Test_MSE: 0.00011166910669793118\n",
      "Epoch: 848 Iteration: 80600. Train_MSE: 0.00022652461484540254. Test_MSE: 0.00011175656867626968\n",
      "Epoch: 849 Iteration: 80700. Train_MSE: 0.0002261576009914279. Test_MSE: 0.00011194498834845264\n",
      "Epoch: 850 Iteration: 80800. Train_MSE: 0.00017514204955659807. Test_MSE: 0.00011144385916300177\n",
      "Epoch: 851 Iteration: 80900. Train_MSE: 0.00026028428692370653. Test_MSE: 0.00011142719925145289\n",
      "Epoch: 852 Iteration: 81000. Train_MSE: 0.0004087350971531123. Test_MSE: 0.00011209283409505496\n",
      "Epoch: 853 Iteration: 81100. Train_MSE: 0.0003417775733396411. Test_MSE: 0.00011258226959963045\n",
      "Epoch: 854 Iteration: 81200. Train_MSE: 0.0006257901550270617. Test_MSE: 0.0001126571928568934\n",
      "Epoch: 855 Iteration: 81300. Train_MSE: 0.005925970152020454. Test_MSE: 0.00011234012237798414\n",
      "Epoch: 856 Iteration: 81400. Train_MSE: 0.00016453977150376886. Test_MSE: 0.0001123603130942496\n",
      "Epoch: 857 Iteration: 81500. Train_MSE: 0.00033000577241182327. Test_MSE: 0.0001121346257074004\n",
      "Epoch: 858 Iteration: 81600. Train_MSE: 0.00023551266349386424. Test_MSE: 0.00011213491634184425\n",
      "Epoch: 860 Iteration: 81700. Train_MSE: 0.0012429914204403758. Test_MSE: 0.00011161846665240545\n",
      "Epoch: 861 Iteration: 81800. Train_MSE: 0.0013525245012715459. Test_MSE: 0.00011114320998147625\n",
      "Epoch: 862 Iteration: 81900. Train_MSE: 0.00016199801757466048. Test_MSE: 0.00011125975447606738\n",
      "Epoch: 863 Iteration: 82000. Train_MSE: 0.00025771462242119014. Test_MSE: 0.00011131288636512216\n",
      "Epoch: 864 Iteration: 82100. Train_MSE: 0.0007625956786796451. Test_MSE: 0.00011097105449654419\n",
      "Epoch: 865 Iteration: 82200. Train_MSE: 0.0005032553453929722. Test_MSE: 0.00011063854173580329\n",
      "Epoch: 866 Iteration: 82300. Train_MSE: 0.00014605959586333483. Test_MSE: 0.00011029498755236512\n",
      "Epoch: 867 Iteration: 82400. Train_MSE: 0.0001422665809514001. Test_MSE: 0.00011019336694777027\n",
      "Epoch: 868 Iteration: 82500. Train_MSE: 0.00022197738871909678. Test_MSE: 0.00011028170722438249\n",
      "Epoch: 869 Iteration: 82600. Train_MSE: 0.00021985698549542576. Test_MSE: 0.0001104675345566442\n",
      "Epoch: 870 Iteration: 82700. Train_MSE: 0.0001717129343887791. Test_MSE: 0.00010998375937595961\n",
      "Epoch: 871 Iteration: 82800. Train_MSE: 0.0002557123953010887. Test_MSE: 0.00010996955584237118\n",
      "Epoch: 872 Iteration: 82900. Train_MSE: 0.0004016529710497707. Test_MSE: 0.00011062140769463746\n",
      "Epoch: 873 Iteration: 83000. Train_MSE: 0.00033662759233266115. Test_MSE: 0.00011110165940306436\n",
      "Epoch: 874 Iteration: 83100. Train_MSE: 0.0006217376794666052. Test_MSE: 0.00011117710325687889\n",
      "Epoch: 875 Iteration: 83200. Train_MSE: 0.00591634027659893. Test_MSE: 0.00011086866925206014\n",
      "Epoch: 876 Iteration: 83300. Train_MSE: 0.00016297452384606004. Test_MSE: 0.00011089524150962449\n",
      "Epoch: 877 Iteration: 83400. Train_MSE: 0.0003278343065176159. Test_MSE: 0.00011067637315193861\n",
      "Epoch: 878 Iteration: 83500. Train_MSE: 0.00023063014668878168. Test_MSE: 0.00011067657174387786\n",
      "Epoch: 880 Iteration: 83600. Train_MSE: 0.0012191085843369365. Test_MSE: 0.00011017312307352348\n",
      "Epoch: 881 Iteration: 83700. Train_MSE: 0.0013470243429765105. Test_MSE: 0.00010970799937320722\n",
      "Epoch: 882 Iteration: 83800. Train_MSE: 0.0001592078187968582. Test_MSE: 0.00010982019740840093\n",
      "Epoch: 883 Iteration: 83900. Train_MSE: 0.0002523800649214536. Test_MSE: 0.00010986963820499309\n",
      "Epoch: 884 Iteration: 84000. Train_MSE: 0.0007614266942255199. Test_MSE: 0.00010953387186785975\n",
      "Epoch: 885 Iteration: 84100. Train_MSE: 0.0005005019484087825. Test_MSE: 0.00010920942003885983\n",
      "Epoch: 886 Iteration: 84200. Train_MSE: 0.00014483771519735456. Test_MSE: 0.00010887638262802928\n",
      "Epoch: 887 Iteration: 84300. Train_MSE: 0.00014011692837812006. Test_MSE: 0.00010877919893591124\n",
      "Epoch: 888 Iteration: 84400. Train_MSE: 0.00021759924129582942. Test_MSE: 0.00010886799403699406\n",
      "Epoch: 889 Iteration: 84500. Train_MSE: 0.0002137771953130141. Test_MSE: 0.00010905095880116071\n",
      "Epoch: 890 Iteration: 84600. Train_MSE: 0.0001684288145042956. Test_MSE: 0.00010858305246794359\n",
      "Epoch: 891 Iteration: 84700. Train_MSE: 0.000251306890277192. Test_MSE: 0.00010857094494226259\n",
      "Epoch: 892 Iteration: 84800. Train_MSE: 0.00039477378595620394. Test_MSE: 0.00010920880237802884\n",
      "Epoch: 893 Iteration: 84900. Train_MSE: 0.0003315838403068483. Test_MSE: 0.00010967928064099028\n",
      "Epoch: 894 Iteration: 85000. Train_MSE: 0.0006177722243592143. Test_MSE: 0.00010975439707624582\n",
      "Epoch: 895 Iteration: 85100. Train_MSE: 0.005907074082642794. Test_MSE: 0.00010945389208312936\n",
      "Epoch: 896 Iteration: 85200. Train_MSE: 0.0001614164502825588. Test_MSE: 0.00010948582662913591\n",
      "Epoch: 897 Iteration: 85300. Train_MSE: 0.00032571106567047536. Test_MSE: 0.00010927268401093804\n",
      "Epoch: 898 Iteration: 85400. Train_MSE: 0.0002259414759464562. Test_MSE: 0.00010927225051184257\n",
      "Epoch: 900 Iteration: 85500. Train_MSE: 0.0011961035197600722. Test_MSE: 0.00010878134836909182\n",
      "Epoch: 901 Iteration: 85600. Train_MSE: 0.0013416511937975883. Test_MSE: 0.00010832665268641313\n",
      "Epoch: 902 Iteration: 85700. Train_MSE: 0.0001565567945363. Test_MSE: 0.000108435139256928\n",
      "Epoch: 903 Iteration: 85800. Train_MSE: 0.00024714236496947706. Test_MSE: 0.00010848104735271286\n",
      "Epoch: 904 Iteration: 85900. Train_MSE: 0.0007603858248330653. Test_MSE: 0.00010815077069710394\n",
      "Epoch: 905 Iteration: 86000. Train_MSE: 0.0004978503566235304. Test_MSE: 0.00010783389175136285\n",
      "Epoch: 906 Iteration: 86100. Train_MSE: 0.00014368513075169176. Test_MSE: 0.00010751036175383203\n",
      "Epoch: 907 Iteration: 86200. Train_MSE: 0.00013804079208057374. Test_MSE: 0.00010741646500848806\n",
      "Epoch: 908 Iteration: 86300. Train_MSE: 0.00021338161604944617. Test_MSE: 0.00010750430591930628\n",
      "Epoch: 909 Iteration: 86400. Train_MSE: 0.00020794277952518314. Test_MSE: 0.00010768348555447674\n",
      "Epoch: 910 Iteration: 86500. Train_MSE: 0.0001652854698477313. Test_MSE: 0.00010723052408599399\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 911 Iteration: 86600. Train_MSE: 0.00024692912120372057. Test_MSE: 0.00010721961750793964\n",
      "Epoch: 912 Iteration: 86700. Train_MSE: 0.00038788988604210317. Test_MSE: 0.00010784354147229772\n",
      "Epoch: 913 Iteration: 86800. Train_MSE: 0.00032666223705746233. Test_MSE: 0.00010830400950173181\n",
      "Epoch: 914 Iteration: 86900. Train_MSE: 0.000613922777120024. Test_MSE: 0.00010837857209622186\n",
      "Epoch: 915 Iteration: 87000. Train_MSE: 0.005897979252040386. Test_MSE: 0.0001080849743192495\n",
      "Epoch: 916 Iteration: 87100. Train_MSE: 0.0001599030802026391. Test_MSE: 0.0001081215822638339\n",
      "Epoch: 917 Iteration: 87200. Train_MSE: 0.00032354274298995733. Test_MSE: 0.00010791396601971785\n",
      "Epoch: 918 Iteration: 87300. Train_MSE: 0.00022155293845571578. Test_MSE: 0.00010791315482541842\n",
      "Epoch: 920 Iteration: 87400. Train_MSE: 0.001173978322185576. Test_MSE: 0.00010743393382162387\n",
      "Epoch: 921 Iteration: 87500. Train_MSE: 0.0013363836333155632. Test_MSE: 0.00010698838860871923\n",
      "Epoch: 922 Iteration: 87600. Train_MSE: 0.0001536690106149763. Test_MSE: 0.00010709263290878172\n",
      "Epoch: 923 Iteration: 87700. Train_MSE: 0.0002417174109723419. Test_MSE: 0.00010713500612951409\n",
      "Epoch: 924 Iteration: 87800. Train_MSE: 0.0007596202194690704. Test_MSE: 0.00010681006879043055\n",
      "Epoch: 925 Iteration: 87900. Train_MSE: 0.0004952326416969299. Test_MSE: 0.00010650088987717283\n",
      "Epoch: 926 Iteration: 88000. Train_MSE: 0.00014257025031838566. Test_MSE: 0.00010618685666064442\n",
      "Epoch: 927 Iteration: 88100. Train_MSE: 0.00013600468810182065. Test_MSE: 0.00010609701063488932\n",
      "Epoch: 928 Iteration: 88200. Train_MSE: 0.00020928034791722894. Test_MSE: 0.00010618562899777792\n",
      "Epoch: 929 Iteration: 88300. Train_MSE: 0.00020239427976775914. Test_MSE: 0.00010636222023859625\n",
      "Epoch: 930 Iteration: 88400. Train_MSE: 0.0001622614508960396. Test_MSE: 0.00010592326514316392\n",
      "Epoch: 931 Iteration: 88500. Train_MSE: 0.00024258214398287237. Test_MSE: 0.00010591422404157773\n",
      "Epoch: 932 Iteration: 88600. Train_MSE: 0.0003810750204138458. Test_MSE: 0.00010652510453134158\n",
      "Epoch: 933 Iteration: 88700. Train_MSE: 0.000321897299727425. Test_MSE: 0.0001069764017063171\n",
      "Epoch: 934 Iteration: 88800. Train_MSE: 0.0006101977778598666. Test_MSE: 0.00010705080931155714\n",
      "Epoch: 935 Iteration: 88900. Train_MSE: 0.005888945423066616. Test_MSE: 0.00010676495425261734\n",
      "Epoch: 936 Iteration: 89000. Train_MSE: 0.00015844026347622275. Test_MSE: 0.00010680660948916651\n",
      "Epoch: 937 Iteration: 89100. Train_MSE: 0.000321318773785606. Test_MSE: 0.00010660507766535666\n",
      "Epoch: 938 Iteration: 89200. Train_MSE: 0.00021741431555710733. Test_MSE: 0.00010660427428517062\n",
      "Epoch: 940 Iteration: 89300. Train_MSE: 0.001152767683379352. Test_MSE: 0.00010613651440944186\n",
      "Epoch: 941 Iteration: 89400. Train_MSE: 0.0013312411028891802. Test_MSE: 0.00010570043309378789\n",
      "Epoch: 942 Iteration: 89500. Train_MSE: 0.0001509066642029211. Test_MSE: 0.00010580056662540785\n",
      "Epoch: 943 Iteration: 89600. Train_MSE: 0.00023653409152757376. Test_MSE: 0.00010583961175068256\n",
      "Epoch: 944 Iteration: 89700. Train_MSE: 0.0007589654414914548. Test_MSE: 0.00010551966741191735\n",
      "Epoch: 945 Iteration: 89800. Train_MSE: 0.0004926773253828287. Test_MSE: 0.00010521728575834952\n",
      "Epoch: 946 Iteration: 89900. Train_MSE: 0.00014150358038023114. Test_MSE: 0.00010491189783740493\n",
      "Epoch: 947 Iteration: 90000. Train_MSE: 0.00013402049080468714. Test_MSE: 0.00010482552860407396\n",
      "Epoch: 948 Iteration: 90100. Train_MSE: 0.0002052694617304951. Test_MSE: 0.00010491447687137197\n",
      "Epoch: 949 Iteration: 90200. Train_MSE: 0.0001971015881281346. Test_MSE: 0.00010508799342655744\n",
      "Epoch: 950 Iteration: 90300. Train_MSE: 0.00015934614930301905. Test_MSE: 0.00010466270760953033\n",
      "Epoch: 951 Iteration: 90400. Train_MSE: 0.00023838963534217328. Test_MSE: 0.00010465496618777524\n",
      "Epoch: 952 Iteration: 90500. Train_MSE: 0.0003744487767107785. Test_MSE: 0.00010525319171129045\n",
      "Epoch: 953 Iteration: 90600. Train_MSE: 0.0003172301221638918. Test_MSE: 0.00010569569695181601\n",
      "Epoch: 954 Iteration: 90700. Train_MSE: 0.0006065685884095728. Test_MSE: 0.00010576968344822337\n",
      "Epoch: 955 Iteration: 90800. Train_MSE: 0.005880198907107115. Test_MSE: 0.0001054906490530889\n",
      "Epoch: 956 Iteration: 90900. Train_MSE: 0.00015703096869401634. Test_MSE: 0.00010553750404891494\n",
      "Epoch: 957 Iteration: 91000. Train_MSE: 0.0003190482093486935. Test_MSE: 0.00010534194075265131\n",
      "Epoch: 958 Iteration: 91100. Train_MSE: 0.00021340265811886638. Test_MSE: 0.00010534178966213046\n",
      "Epoch: 960 Iteration: 91200. Train_MSE: 0.0011321738129481673. Test_MSE: 0.0001048859052933077\n",
      "Epoch: 961 Iteration: 91300. Train_MSE: 0.001326271565631032. Test_MSE: 0.00010446020303018148\n",
      "Epoch: 962 Iteration: 91400. Train_MSE: 0.00014825872494839132. Test_MSE: 0.00010455763177019496\n",
      "Epoch: 963 Iteration: 91500. Train_MSE: 0.00023153384972829372. Test_MSE: 0.00010459457575226114\n",
      "Epoch: 964 Iteration: 91600. Train_MSE: 0.0007584022241644561. Test_MSE: 0.00010428132484834209\n",
      "Epoch: 965 Iteration: 91700. Train_MSE: 0.000490225909743458. Test_MSE: 0.00010398715361699741\n",
      "Epoch: 966 Iteration: 91800. Train_MSE: 0.00014048929733689874. Test_MSE: 0.00010369135477775385\n",
      "Epoch: 967 Iteration: 91900. Train_MSE: 0.00013207034498918802. Test_MSE: 0.00010360867544752758\n",
      "Epoch: 968 Iteration: 92000. Train_MSE: 0.00020137870160397142. Test_MSE: 0.00010369763693935512\n",
      "Epoch: 969 Iteration: 92100. Train_MSE: 0.000192014776985161. Test_MSE: 0.0001038680602751371\n",
      "Epoch: 970 Iteration: 92200. Train_MSE: 0.00015654051094315946. Test_MSE: 0.000103455936067725\n",
      "Epoch: 971 Iteration: 92300. Train_MSE: 0.00023432236048392951. Test_MSE: 0.00010344928756116579\n",
      "Epoch: 972 Iteration: 92400. Train_MSE: 0.0003679218061733991. Test_MSE: 0.0001040341502944914\n",
      "Epoch: 973 Iteration: 92500. Train_MSE: 0.0003126277879346162. Test_MSE: 0.00010446727691481729\n",
      "Epoch: 974 Iteration: 92600. Train_MSE: 0.000603007385507226. Test_MSE: 0.00010454064294172391\n",
      "Epoch: 975 Iteration: 92700. Train_MSE: 0.005871818866580725. Test_MSE: 0.00010426829622039434\n",
      "Epoch: 976 Iteration: 92800. Train_MSE: 0.00015567451191600412. Test_MSE: 0.00010431866456360473\n",
      "Epoch: 977 Iteration: 92900. Train_MSE: 0.0003168150142300874. Test_MSE: 0.00010412744677090994\n",
      "Epoch: 978 Iteration: 93000. Train_MSE: 0.00020948862947989255. Test_MSE: 0.00010412619118791354\n",
      "Epoch: 980 Iteration: 93100. Train_MSE: 0.0011122761061415076. Test_MSE: 0.000103679162131911\n",
      "Epoch: 981 Iteration: 93200. Train_MSE: 0.0013214659411460161. Test_MSE: 0.00010326052246851146\n",
      "Epoch: 982 Iteration: 93300. Train_MSE: 0.00014573140651918948. Test_MSE: 0.00010335350531172326\n",
      "Epoch: 983 Iteration: 93400. Train_MSE: 0.00022672400518786162. Test_MSE: 0.0001033865104557672\n",
      "Epoch: 984 Iteration: 93500. Train_MSE: 0.0007578927907161415. Test_MSE: 0.00010307723897614709\n",
      "Epoch: 985 Iteration: 93600. Train_MSE: 0.0004878790059592575. Test_MSE: 0.0001027890120717668\n",
      "Epoch: 986 Iteration: 93700. Train_MSE: 0.00013951575965620577. Test_MSE: 0.00010250085090201692\n",
      "Epoch: 987 Iteration: 93800. Train_MSE: 0.0001301764277741313. Test_MSE: 0.00010241989675974666\n",
      "Epoch: 988 Iteration: 93900. Train_MSE: 0.00019759299175348133. Test_MSE: 0.00010250724299248472\n",
      "Epoch: 989 Iteration: 94000. Train_MSE: 0.0001871085405582562. Test_MSE: 0.00010267237124610547\n",
      "Epoch: 990 Iteration: 94100. Train_MSE: 0.00015380042896140367. Test_MSE: 0.00010227126252607992\n",
      "Epoch: 991 Iteration: 94200. Train_MSE: 0.00023040935047902167. Test_MSE: 0.0001022639811522327\n",
      "Epoch: 992 Iteration: 94300. Train_MSE: 0.0003615535097196698. Test_MSE: 0.00010283451109195811\n",
      "Epoch: 993 Iteration: 94400. Train_MSE: 0.0003081322065554559. Test_MSE: 0.00010325690021048351\n",
      "Epoch: 994 Iteration: 94500. Train_MSE: 0.0005995241808705032. Test_MSE: 0.00010332793126826165\n",
      "Epoch: 995 Iteration: 94600. Train_MSE: 0.005863672588020563. Test_MSE: 0.00010306034807850423\n",
      "Epoch: 996 Iteration: 94700. Train_MSE: 0.00015436230751220137. Test_MSE: 0.0001031131293782182\n",
      "Epoch: 997 Iteration: 94800. Train_MSE: 0.00031453921110369265. Test_MSE: 0.00010292552611157163\n",
      "Epoch: 998 Iteration: 94900. Train_MSE: 0.0002057260717265308. Test_MSE: 0.00010292256218138651\n"
     ]
    }
   ],
   "source": [
    "# Training the model\n",
    "seq_dim = 1\n",
    "\n",
    "n_iter =0\n",
    "num_samples = len(X_train)\n",
    "test_samples = len(X_test)\n",
    "batch_size = 100\n",
    "num_epochs = 1000\n",
    "feat_dim = X_train.shape[1]\n",
    "\n",
    "X_train = X_train.type(torch.FloatTensor)\n",
    "y_train = y_train.type(torch.FloatTensor)\n",
    "X_test = X_test.type(torch.FloatTensor)\n",
    "y_test = y_test.type(torch.FloatTensor)\n",
    "\n",
    "for epoch in range(num_epochs):\n",
    "    for i in range(0, int(num_samples/batch_size -1)):\n",
    "        \n",
    "        \n",
    "        features = Variable(X_train[i*batch_size:(i+1)*batch_size, :]).view(-1, seq_dim, feat_dim)\n",
    "        Kt_value = Variable(y_train[i*batch_size:(i+1)*batch_size])\n",
    "        \n",
    "        #print(\"Kt_value={}\".format(Kt_value))\n",
    "        \n",
    "        optimizer.zero_grad()\n",
    "        \n",
    "        outputs = model(features)\n",
    "        #print(\"outputs ={}\".format(outputs))\n",
    "        \n",
    "        loss = criterion(outputs, Kt_value)\n",
    "        \n",
    "        train_loss.append(loss.data[0])\n",
    "        train_iter.append(n_iter)\n",
    "\n",
    "        #print(\"loss = {}\".format(loss))\n",
    "        loss.backward()\n",
    "        \n",
    "        optimizer.step()\n",
    "        \n",
    "        \n",
    "            \n",
    "        if n_iter%100 == 0:\n",
    "            for i in range(0,int(test_samples/batch_size -1)):\n",
    "                features = Variable(X_test[i*batch_size:(i+1)*batch_size, :]).view(-1, seq_dim, feat_dim)\n",
    "                Kt_test = Variable(y_test[i*batch_size:(i+1)*batch_size])\n",
    "                \n",
    "                outputs = model(features)\n",
    "                \n",
    "                mse = np.sqrt(np.mean((Kt_test.data.numpy() - outputs.data.numpy().squeeze())**2)/num_samples)\n",
    "                \n",
    "                test_iter.append(n_iter)\n",
    "                test_loss.append(mse)\n",
    "                \n",
    "            print('Epoch: {} Iteration: {}. Train_MSE: {}. Test_MSE: {}'.format(epoch, n_iter, loss.data[0], mse))\n",
    "        n_iter += 1         "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "38000\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x4bafbac8>]"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXcAAAD3CAYAAADmBxSSAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nRK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(len(test_loss))\n",
    "#plt.plot(test_loss)\n",
    "plt.plot(train_loss,'-')\n",
    "#plt.ylim([0.000,0.99])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0xd3c9e80>]"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYIAAAD3CAYAAAAQYlNPAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDIuMi4yLCBo\ndHRwOi8vbWF0cGxvdGxpYi5vcmcvhp/UCwAAGvdJREFUeJzt3X+UXOV93/H3zI7QrqzVekEDuLZb\nH5PDtzppUGtkGUWSfwQISASD6+aUQlNbOWABThVE2kYUAgcf1bgnxTQKgbQijnBU5aSkVqGKhaEH\nB0srFAWscMSx+Yo1PsGUXyu02l1ptT9m5vaPubu6Ws3O3t2Z3Rn5+bzO0fHM8zz3me+9Huaz996Z\nezNRFCEiIuHKNroAERFpLAWBiEjgFAQiIoFTEIiIBE5BICISuFyjC5iJnp6BGX/VqbNzAb29g/Us\np65UX21UX21UX22aub58vj0zWV9wewS5XEujS6hK9dVG9dVG9dWm2eubTHBBICIip1MQiIgETkEg\nIhI4BYGISOAUBCIigVMQiIgETkEgIhK4KX9QZmZZ4BFgKTAM3Ozu3Yn+W4D1QAHY7O67zGwxsANo\nA94C1rn7oJl9FfgyEAFfi8dmgDeB1+IpX3D3u+q1gkkDL/4thWiE3CdXzsb0IiJnpTS/LL4eaHX3\nFWZ2GfAgcB2AmV0IbACWAa3AXjN7FrgX2OHu28xsE7DezP4MuB34p/HYH5nZXwEXAT9092vrvG6n\nKQ2d5O0//iMALlYQiIiMSxMEq4CnAdx9v5ktS/QtB7rcfRgYNrNu4JJ4ma/HY3YDX3f3h8xsqbsX\nzOxjwDF3j8zsUuDDZvZ94CSw0d29WkGdnQum/Qu+wTf7xx/n8+3TWnauqb7aqL7aqL7aNHt9laQJ\ngkVAX+J50cxy7l6o0DcAdExoH2sjDoHfAu4HtsT9bwMPuPsTZrYK2A58slpBM7mWx8jR4+OPe3oG\npr38XMnn21VfDVRfbVRfbZq5vmoBleZkcT+QnCEbh0Clvnbg2IT2sTYA3P1h4EPAp83sc8CLwJNx\n317KeweTXhxJRETqK00QdAFrAeJzBIcSfQeA1WbWamYdwBLgleQywBpgj5V9J/6QH6V84rkE3Afc\nEc+/FHjD3XUjZRGROZImCHYCQ2a2D3gI2Ghmd5rZ5939HcqHePYAzwF3u/sQsBm4wcy6gBXAw/Fx\n/5eBF4B9wH53fx74BvAZM3se+CblbxXVXbatbTamFRE56015jsDdS8CtE5pfTfRvBbZOWOZd4OoK\nc91P+fxAsq0XuCZ9yTOTyc2b7ZcQETkr6QdlIiKBUxCIiAQunCDI6ItIIiKVhBMEIiJSkYJARCRw\n4QSBjgyJiFQUThCIiEhFCgIRkcApCEREAhdQEOgkgYhIJQEFgYiIVKIgEBEJnIJARCRw4QSBLjEh\nIlJROEEgIiIVKQhERAKnIBARCVwwQaBTBCIilQUTBCIiUpmCQEQkcFPevN7MssAjwFJgGLjZ3bsT\n/bcA64ECsNndd5nZYmAH0Aa8Baxz90Ez+yrwZSACvhaPbQO2A+cDA8CX3L2njusY07EhEZFK0uwR\nXA+0uvsKYBPw4FiHmV0IbABWAlcBD5jZfOBeYIe7rwYOAuvjcLgd+GXgcuBRM8sAtwGH4rHfBu6p\n18qJiMjU0gTBKuBpAHffDyxL9C0Hutx92N37gG7gkuQywG7gCnc/Aix191HgQuCYu0eVxta2SiIi\nMh1THhoCFgF9iedFM8u5e6FC3wDQMaF9rA13L5jZbwH3A1sqzD8+tprOzgXkci0pSk8UfTLH2PGs\nfL59WsvONdVXG9VXG9VXm2avr5I0QdAPJNcsG4dApb524Fii/WSiDQB3f9jM/juw28w+N2GO08ZO\nprd3MEXZpysND48/7ukZmPbycyWfb1d9NVB9tVF9tWnm+qoFVJpDQ13AWgAzuww4lOg7AKw2s1Yz\n6wCWAK8klwHWAHus7DvxeYFRyieeS5XGplwvERGpgzRBsBMYMrN9wEPARjO708w+7+7vUD7Eswd4\nDrjb3YeAzcANZtYFrAAedncHXgZeAPYB+939eeBR4BfNbC/wFcqHjUREZI5MeWjI3UvArROaX030\nbwW2TljmXeDqCnPdz4QPencfBH49fckiIlJP+kGZiEjgFAQiIoFTEIiIBE5BICISuHCCQNehFhGp\nKJwgEBGRisIJAu0QiIhUFE4QiIhIRQEFgXYJREQqCSgIRESkEgWBiEjgggmCjL4+KiJSUTBBICIi\nlSkIREQCpyAQEQlcOEGQOEcQFYsNLEREpLmEEwRRNP7w2F8/18BCRESaSzhBkDDy/95sdAkiIk0j\nyCCIEnsHIiKhCycIsuGsqojIdATz6ZhJBoF2CERExuWmGmBmWeARYCkwDNzs7t2J/luA9UAB2Ozu\nu8xsMbADaAPeAta5+6CZbQRuiBf9rrvfb2YZ4E3gtbj9BXe/qz6rNwkdGhIRGTdlEADXA63uvsLM\nLgMeBK4DMLMLgQ3AMqAV2GtmzwL3AjvcfZuZbQLWm9mTwE3Apyj/Tb7HzHYCg8AP3f3aOq+biIik\nkObQ0CrgaQB330/5Q3/McqDL3YfdvQ/oBi5JLgPsBq4AfgZc7e5Fdy8B84Ah4FLgw2b2fTP7rplZ\nHdarqtLQydl+CRGRs0aaPYJFQF/iedHMcu5eqNA3AHRMaB8AOtx9FDgSHwr6feCgux+O9yoecPcn\nzGwVsB34ZLWCOjsXkMu1pCj9dIfj/x1+vZt8vn3ay8+VZq4NVF+tVF9tVF/9pQmCfiC5Ztk4BCr1\ntQPHEu0nE22YWSvwLcrhcHu8zIuUzy/g7nvN7MNmlnH3SQ/k9/YOpih7cqVCiZ6egZrmmC35fHvT\n1gaqr1aqrzaqb+aqBVSaQ0NdwFqA+BzBoUTfAWC1mbWaWQewBHgluQywhvL5gAzwJPCyu69397Hr\nPNwH3BHPvxR4o1oI1EMUlWZzehGRs0qaPYKdwJVmto/y/R7XmdmdQLe7P2VmW4A9lEPlbncfMrPN\nwOPxN4qOADdSPun8GWC+ma2J574L+Aaw3cyuobxn8OX6rV5l0cjIbL+EiMhZY8ogiE/s3jqh+dVE\n/1Zg64Rl3gWunrDMTsrfLKrkmikrFRGRWRHMD8pOU9KhIRGRMUEGga41JCJySpBBoD0CEZFTwgwC\n7RGIiIwLMwhERGScgkBEJHAKAhGRwCkIREQCpyAQEQmcgkBEJHAKAhGRwCkIREQCpyAQEQmcgkBE\nJHAKAhGRwCkIREQCpyAQEQmcgkBEJHAKAhGRwCkIREQCpyAQEQlcbqoBZpYFHgGWAsPAze7enei/\nBVgPFIDN7r7LzBYDO4A24C1gnbsPmtlG4IZ40e+6+/1m1gZsB84HBoAvuXtP3dZQRESqSrNHcD3Q\n6u4rgE3Ag2MdZnYhsAFYCVwFPGBm84F7gR3uvho4CKw3s48DNwG/DKwAftXMLgFuAw7FY78N3FOv\nlRMRkamlCYJVwNMA7r4fWJboWw50ufuwu/cB3cAlyWWA3cAVwM+Aq9296O4lYB4wNMlYERGZI1Me\nGgIWAX2J50Uzy7l7oULfANAxoX0A6HD3UeCImWWA3wcOuvthMztj7FQFdXYuIJdrSVH66Q4nHufz\n7dNefq40c22g+mql+mqj+uovTRD0A8k1y8YhUKmvHTiWaD+ZaMPMWoFvUf7Av73CHONjq+ntHUxR\ndnU9PQM1zzEb8vn2pq0NVF+tVF9tVN/MVQuoNIeGuoC1AGZ2GXAo0XcAWG1mrWbWASwBXkkuA6wB\n9sR7Ak8CL7v7encvTpx/bGyalRIRkfpIs0ewE7jSzPYBGWCdmd0JdLv7U2a2hfKHdxa4292HzGwz\n8Hj8jaIjwI2UTzp/BphvZmviue8CHo3H7gVG4rEiIjJHpgyC+MTurROaX030bwW2TljmXeDqCcvs\nBFoneZlfn7JSERGZFfpBmYhI4BQEIiKBUxCIiAROQSAiEjgFgYhI4BQEIiKBUxCIiAROQSAiEjgF\ngYhI4BQEIiKBUxCIiAROQSAiEjgFgYhI4BQEIiKBUxCIiAROQSAiEjgFgYhI4BQEIiKBUxCIiARO\nQSAiEjgFgYhI4HJTDTCzLPAIsBQYBm529+5E/y3AeqAAbHb3XWa2GNgBtAFvAevcfTAenwf2Ab/k\n7kNmlgHeBF6Lp3zB3e+q1wqKiEh1afYIrgda3X0FsAl4cKzDzC4ENgArgauAB8xsPnAvsMPdVwMH\nKQcFZnYV8AxwQWL+i4Afuvtn438KARGROTTlHgGwCngawN33m9myRN9yoMvdh4FhM+sGLomX+Xo8\nZnf8+CGgBFwBvJSY41Lgw2b2feAksNHdvVpBnZ0LyOVaUpR+usOJx/l8+7SXnyvNXBuovlqpvtqo\nvvpLEwSLgL7E86KZ5dy9UKFvAOiY0D7Whrs/C2BmyfnfBh5w9yfMbBWwHfhktYJ6ewdTlF1dT89A\nzXPMhny+vWlrA9VXK9VXG9U3c9UCKs2hoX4gOUM2DoFKfe3AsQntY22TeRF4EsDd91LeO8ikqEtE\nROogTRB0AWsBzOwy4FCi7wCw2sxazawDWAK8klwGWAPsqTL/fcAd8fxLgTfcPZrOSoiIyMylOTS0\nE7jSzPYBGWCdmd0JdLv7U2a2hfIHfRa4O/4m0Gbg8fgbRUeAG6vM/w1gu5ldQ/mbR1+e+eqIiMh0\nTRkE7l4Cbp3Q/GqifyuwdcIy7wJXV5nzY4nHvcA16coVEZF60w/KREQCpyAQEQmcgkBEJHAKAhGR\nwCkIREQCpyAQEQmcgkBEJHAKAhGRwCkIREQCF1QQZObNG38cRbqckYgIBBYE59/0G+OPT752uMpI\nEZFwBBUEuc5zxx/37v6rBlYiItI8ggqCpOKJ440uQUSkKQQVBLlFHeOPh15/vYGViIg0j7CCoLOz\n0SWIiDSdoIIAfVNIROQMYQWBiIicQUEgIhI4BYGISOCCCoIInSMQEZloypvXm1kWeARYCgwDN7t7\nd6L/FmA9UAA2u/suM1sM7ADagLeAde4+GI/PA/uAX3L3ITNrA7YD5wMDwJfcvaeO63iKckBE5Axp\n9giuB1rdfQWwCXhwrMPMLgQ2ACuBq4AHzGw+cC+ww91XAwcpBwVmdhXwDHBBYv7bgEPx2G8D99S6\nUiIikl6aIFgFPA3g7vuBZYm+5UCXuw+7ex/QDVySXAbYDVwRPy7Fj49Wmn/CWBERmQNTHhoCFgF9\niedFM8u5e6FC3wDQMaF9rA13fxbAzCabf3xsNZ2dC8jlWlKUfrqReUWSvyfO59unPcdcaNa6xqi+\n2qi+2ixevLByR/J3QhN+MxRV6ouiylchLpVOtU9YLiqNPY/i5YGoRFQqQQTDR95nEREQEZVKRMXi\n+CHp8pwRlEqUCsV4jkQdUURULJbnSvaVSkSFAmSzdPyTXySbuIpyvaQJgn4g+c7IxiFQqa8dOJZo\nP5loSzP/VGMB6O0dTFH2mQp9p19f6L33+slkMjOaqxZRFJHJZE5/E5ZKEEXkz19Ez3v98RsrIioW\noFiCTKb8JikWoVQkKhTKj6HcXihAsUhpdLT8uFSK30CjRKMFotERotHR8rxj48b+FUbjeUtEo6On\n+sZeo1SM5xglExUpDI2U64rfoNHoqH6sJzJHLn5s24yWqxbwaYKgC7gW+J9mdhlwKNF3APhPZtYK\nzAeWAK/Ey6wFtgFrgD1TzL82nmuqsXX12i3r5uqlUnut0QWISHDSBMFO4Eoz2wdkgHVmdifQ7e5P\nmdkWyh/eWeDu+JtAm4HH428UHQFurDL/o/HYvcDIFGNFRKTOMmfjnbp6egZmVHSh7xiv/84d9S5H\nRGTO1HBoaNLj4EH9oExERM6kIBARCVxYQXD2HQUTEZl1YQVBS1irKyKSRlCfjLn2RY0uQUSk6QQV\nBCIiciYFgYhI4BQEIiKBUxCIiAROQSAiEjgFgYhI4BQEIiKBUxCIiAROQSAiEjgFgYhI4BQEIiKB\nUxCIiAROQSAiEjgFgYhI4BQEIiKBUxCIiAQuN9UAM8sCjwBLgWHgZnfvTvTfAqwHCsBmd99lZouB\nHUAb8Bawzt0HJxl7LnAYeCWecqe7/0Hd1lBERKqaMgiA64FWd19hZpcBDwLXAZjZhcAGYBnQCuw1\ns2eBe4Ed7r7NzDYB683szycZ+wngz93939Z53UREJIU0h4ZWAU8DuPt+yh/kY5YDXe4+7O59QDdw\nSXIZYDdwRZWxlwKfMLPnzewJM/tQHdZLRERSSrNHsAjoSzwvmlnO3QsV+gaAjgntldqS7a8CL7n7\n/zWzm4A/BP5FtYI6OxeQy7WkKP1Mh2e0lIhIc8jn2+s+Z5og6AeSr5yNQ6BSXztwLNF+skLbxLF/\nAwzGbTuBr01VUG/v4FRDRER+LvX0DMxouWoBkubQUBewFiA+R3Ao0XcAWG1mrWbWASyhfNJ3fBlg\nDbCnytjHgC/GYy8HXkq3WjPzoWuvmc3pRUTOOmmCYCcwZGb7gIeAjWZ2p5l93t3fAbZQ/qB/Drjb\n3YeAzcANZtYFrAAerjJ2E3Cbmf01cCvw23VdwwkuuPxXZnN6EZGzTiaKokbXMG09PQMzLnrBYC9/\nt2FjPcsREZkzFz+2bUbL5fPtmcn6gvtB2YKPfqTRJYiINJXggiCTDW6VRUSq0qeiiEjgFAQiIoFT\nEIiIBE5BICISuCCDYNGKlY0uQUSkaQQZBPl/+a8aXYKISNMIMghaFi7k3LW/1ugyRESaQpBBAHDe\nF75Ix+d0uQkRkWCDIJPJcMFN/4aLH9tG/oYbG12OiEjDBBsESZ1X/CoXP7aNj/y73210KSIicy7N\n/QiCseAfLzntgk6jR9/n6K6n6PvB840rSkRklgV39dF8vn3GN3YYM/r++/Tv20vvs9+jNKib5IjI\n3JmNq49qj2AG5p13Huddex3nXXvdGX1RqUQ0OsrR7+7i+MGXyHV8kMEf/6gBVYqIpKMgqLNMNktm\n/nwWf+GLLP7CF6ccP7ZHNvTT1xl+4+9ZuHA+7734MsdfPDDbpYqIAAqChstkyntrbR+/iLaPX0Q+\n305u2Urg9qrLRYUCpeFhomKRqDDK0E9/yvCbP+PEy3/H8Bt/PweVi8jPCwXBWSqTy9GSO/V/37xz\nz6P90mUsvu4LdZk/KpWICgWiQoHsOedQGhqiePw4i1ozvPfjbkpDw7S0L6RwtJfRI+9R6D3G8Jtv\nUDx5ktzCdgr9/ZQGT9SlFhE5JSqV6n5fFQWBVJTJZsmccw6ccw5Q/jV2y8KFtOfbGfrgBQ2ubnL1\n+DJAFEXje2pRqVTe6xoeJiqVyLa1EY2OUBocpDQ0xOjRo1AqkftgJ8XBExSPHaMw0M/I228TjY4y\nL7+Y0uBJRo/0UOjrY/TttyiNjJA791yi0VGKA7XVKgHKTHrOd8YUBCITZBL/oWWy2fJfX/PmnRow\nbx4tCz4AwPyPfHRac9cjqGZT2vpOC8sogigiKhaBiExLrvy8UCAqFiidHAIisq1tpw5pjo5QHBgg\nKpVoWbiw3D54gtLJIQp9x4hGC7S0t0NUojgwQPHECQq9vZzTEjGam08mm6XYP0DxxHEKvUcpjYzQ\n8oEPkGlpoTAwQOn4caJCYXY3VgO0tLef9v6sFwWBiEzbaWGZyUAmc8bhikxLCzB/PDTr4eclSJvN\nlEFgZlngEWApMAzc7O7dif5bgPVAAdjs7rvMbDGwA2gD3gLWufvgdMbWcyVFRGRyac44XA+0uvsK\nYBPw4FiHmV0IbABWAlcBD5jZfOBeYIe7rwYOAuunM7ZeKyciIlNLc2hoFfA0gLvvN7Nlib7lQJe7\nDwPDZtYNXBIv8/V4zO748U+mMfahagV1di4gl2tJUXpl+Xz7jJedC6qvNqqvNqqvNs1eXyVpgmAR\n0Jd4XjSznLsXKvQNAB0T2iu1TTW2qt7emR85avZjeKqvNqqvNqqvNs1cX7WASnNoqB9IzpCNQ6BS\nXztwbEJ7pbapxoqIyBxJEwRdwFoAM7sMOJToOwCsNrNWM+sAlgCvJJcB1gB7pjlWRETmSJog2AkM\nmdk+ysfuN5rZnWb2eXd/B9hC+cP7OeBudx8CNgM3mFkXsAJ4eDpj67uKIiJSjS5D3WRUX21UX21U\nX22aub5ql6E+K4NARETqR7eqFBEJnIJARCRwCgIRkcApCEREAqcgEBEJnIJARCRwCgIRkcAFcWOa\nqe6pMEc1HOTUxfV+Cvw34A8o35vhGXe/f7I640t7nDa2jnV9CvjP7v5ZM/sFYBsQUb78x1fdvWRm\n9wHXxK9/h7sfmM7YOtb3CeD/AK/F3Y+6+180oj4zmwd8C/gYMJ/yL+R/lPY152L7TVLjmzTPNmwB\ntgIGFIF1QCbtazaovg6aZPvVUyh7BJPeU2EumFkrgLt/Nv63Dvhj4EbKl+H+VPwhN1mdlcbWo67/\nADwGtMZN3wTuie8NkQGui1/rM8CngBuAP5rB2HrV9wngm4nt+BcNrO9fA+/H86+hfGmUptp+k9TY\nTNvwWgB3X0n5viTfnOZrNqK+Ztp+dRNKEJx2TwVgWfXhdbcUWGBmz5jZc2b2aWC+u//E3SPge8Dl\nleo0s0WTjK2HnwD/PPH8UuD5+PFu4Iq4pmfcPXL3N4CcmeWnObae9V1jZj8wsz8xs/YG1vcE8HuJ\n54VpvuZcbL/JamyKbeju/xv4Svz0HwHvTvM1G1VfU2y/egolCCreU2EOX38Q+C+U78x2K/CncduY\nye7ZUIzb+iuMrZm7/y9gNNGUicOmWk1j7dMZW6/6DgD/3t0/DbwO3Neo+tz9uLsPxB8EfwncM83X\nnIvtV6nGptmGcY0FM3sc+MO4xmbbhhPra6rtVy+hBEG1eyrMhcPA9vivgMOU3wjnJvonu2dDtkLb\nbN6zoZSiprH26Yytl53u/tLYY+CfNbI+M/so8H3gz9x9xzRfc062X4Uam2obArj7l4CLKR+Pb5vG\nazaivmeabfvVQyhBUO2eCnPhN4mP95vZPwAWACfM7CIzy1DeU9hTqU537wdGKoydDQfN7LPx47F7\nQ3QBV5lZ1sz+IeUQPTLNsfXyPTNbHj++HHipUfWZ2QXAM8Dvuvu34uam2n6T1NhM2/A3zOyu+Okg\n5Q/OF5tlG05S33eaZfvVUxDfGqKc3Fda+Z4KGcpn/+fSnwDbzGwv5W8Q/CblN9X/AFoo/5XxN2b2\nt5PUeevEsbNU5+8AW83sHODHwF+6e9HM9gAvUP7D4aszGFsvtwEPm9kI8A7wFXfvb1B9/xHoBH7P\nzMaOw/82sKWJtl+lGu8E/muTbMPvAH9qZj8A5gF3xK/TLO/BSvX9jOZ5D9aNLkMtIhK4UA4NiYjI\nJBQEIiKBUxCIiAROQSAiEjgFgYhI4BQEIiKBUxCIiATu/wMz84LAluKrbQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(test_loss,'r')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "#### Demornamization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [],
   "source": [
    "rmse = np.sqrt(mse)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [],
   "source": [
    "rmse_denorm = (rmse * (df_new_test['Kt'].max() - df_new_test['Kt'].min()))+ df_new_test['Kt'].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "31.74081898174663"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rmse_denorm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    4075.000000\n",
       "mean        7.569057\n",
       "std        56.069218\n",
       "min        -0.032646\n",
       "25%        -0.000888\n",
       "50%         0.187680\n",
       "75%         1.290727\n",
       "max      2382.577671\n",
       "Name: Kt, dtype: float64"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_new_test['Kt'].describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "### Saving train and test losses to a csv"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_trainLoss = pd.DataFrame(data={'Train Loss':train_loss, 'iteration':train_iter}, columns=['Train Loss','iteration'])\n",
    "df_trainLoss.to_csv('RNN Paper Results/Exp1_SiouxFalls_TrainLoss.csv')\n",
    "df_testLoss = pd.DataFrame(data={'Test Loss':test_loss, 'iteration':test_iter}, columns=['Test Loss','iteration'])\n",
    "df_testLoss.to_csv('RNN Paper Results/Exp1_SiouxFalls_TestLoss.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "anaconda-cloud": {},
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}