Predict Future Price - Binary Option of USDCAD - V6.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Predicting Financial Time Series Data with Machine Learning\n",
    "\n",
    "This is a sample that predicts future price behaviour from past price movements.\n",
    "Here we implement it with USD/CAD rate as an example, but you can convert it to other market prices by getting other csv files.\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "import datetime\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import mplfinance as mpf\n",
    "from sklearn.ensemble import GradientBoostingClassifier\n",
    "from sklearn import ensemble\n",
    "from sklearn.model_selection import cross_val_score"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Estruturando e Visualizando os Dados"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# transformando dataframes de USDCAD e do PETRÓLEO BRENT\n",
    "\n",
    "h = pd.read_csv('USDCAD_Candlestick_1_Hour_BID_31.12.2015-31.05.2018.csv')\n",
    "o = pd.read_csv('BRENT.CMDUSD_Candlestick_1_Hour_BID_31.12.2014-31.05.2018.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "scrolled": true,
    "tags": []
   },
   "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>Gmt time</th>\n",
       "      <th>Open</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Close</th>\n",
       "      <th>Volume</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>01.01.2016 00:00:00.000</td>\n",
       "      <td>1.38326</td>\n",
       "      <td>1.38326</td>\n",
       "      <td>1.38326</td>\n",
       "      <td>1.38326</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>01.01.2016 01:00:00.000</td>\n",
       "      <td>1.38326</td>\n",
       "      <td>1.38326</td>\n",
       "      <td>1.38326</td>\n",
       "      <td>1.38326</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>01.01.2016 02:00:00.000</td>\n",
       "      <td>1.38326</td>\n",
       "      <td>1.38326</td>\n",
       "      <td>1.38326</td>\n",
       "      <td>1.38326</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>01.01.2016 03:00:00.000</td>\n",
       "      <td>1.38326</td>\n",
       "      <td>1.38326</td>\n",
       "      <td>1.38326</td>\n",
       "      <td>1.38326</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>01.01.2016 04:00:00.000</td>\n",
       "      <td>1.38326</td>\n",
       "      <td>1.38326</td>\n",
       "      <td>1.38326</td>\n",
       "      <td>1.38326</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  Gmt time     Open     High      Low    Close  Volume\n",
       "0  01.01.2016 00:00:00.000  1.38326  1.38326  1.38326  1.38326     0.0\n",
       "1  01.01.2016 01:00:00.000  1.38326  1.38326  1.38326  1.38326     0.0\n",
       "2  01.01.2016 02:00:00.000  1.38326  1.38326  1.38326  1.38326     0.0\n",
       "3  01.01.2016 03:00:00.000  1.38326  1.38326  1.38326  1.38326     0.0\n",
       "4  01.01.2016 04:00:00.000  1.38326  1.38326  1.38326  1.38326     0.0"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "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>Gmt time</th>\n",
       "      <th>Open</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Close</th>\n",
       "      <th>Volume</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>15116</th>\n",
       "      <td>31.05.2018 19:00:00.000</td>\n",
       "      <td>1.29566</td>\n",
       "      <td>1.29671</td>\n",
       "      <td>1.29541</td>\n",
       "      <td>1.29666</td>\n",
       "      <td>5.330320e+09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15117</th>\n",
       "      <td>31.05.2018 20:00:00.000</td>\n",
       "      <td>1.29668</td>\n",
       "      <td>1.29682</td>\n",
       "      <td>1.29502</td>\n",
       "      <td>1.29563</td>\n",
       "      <td>3.116830e+09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15118</th>\n",
       "      <td>31.05.2018 21:00:00.000</td>\n",
       "      <td>1.29563</td>\n",
       "      <td>1.29577</td>\n",
       "      <td>1.29498</td>\n",
       "      <td>1.29564</td>\n",
       "      <td>1.456650e+09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15119</th>\n",
       "      <td>31.05.2018 22:00:00.000</td>\n",
       "      <td>1.29563</td>\n",
       "      <td>1.29563</td>\n",
       "      <td>1.29407</td>\n",
       "      <td>1.29409</td>\n",
       "      <td>1.370330e+09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15120</th>\n",
       "      <td>31.05.2018 23:00:00.000</td>\n",
       "      <td>1.29409</td>\n",
       "      <td>1.29469</td>\n",
       "      <td>1.29406</td>\n",
       "      <td>1.29436</td>\n",
       "      <td>1.259850e+09</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                      Gmt time     Open     High      Low    Close  \\\n",
       "15116  31.05.2018 19:00:00.000  1.29566  1.29671  1.29541  1.29666   \n",
       "15117  31.05.2018 20:00:00.000  1.29668  1.29682  1.29502  1.29563   \n",
       "15118  31.05.2018 21:00:00.000  1.29563  1.29577  1.29498  1.29564   \n",
       "15119  31.05.2018 22:00:00.000  1.29563  1.29563  1.29407  1.29409   \n",
       "15120  31.05.2018 23:00:00.000  1.29409  1.29469  1.29406  1.29436   \n",
       "\n",
       "             Volume  \n",
       "15116  5.330320e+09  \n",
       "15117  3.116830e+09  \n",
       "15118  1.456650e+09  \n",
       "15119  1.370330e+09  \n",
       "15120  1.259850e+09  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Put USD/CAD hour data into Pandas Dataframe\n",
    "\n",
    "df = pd.DataFrame(h)\n",
    "display(df.head())\n",
    "display(df.tail())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 862.5x575 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "usd_cad = df.copy()\n",
    "usd_cad.index = pd.to_datetime(usd_cad['Gmt time'])\n",
    "\n",
    "# Renomeia as colunas para atender aos requisitos do mplfinance\n",
    "usd_cad = usd_cad.rename(columns={'Open':'open', 'High':'high', 'Low':'low', 'Close':'close', 'Volume':'volume'})\n",
    "\n",
    "# Define os parâmetros para o gráfico de candlestick\n",
    "mc = mpf.make_marketcolors(up='g', down='r', wick='inherit', edge='inherit')\n",
    "s = mpf.make_mpf_style(marketcolors=mc)\n",
    "kwargs = dict(type='candle', volume=True, figratio=(12,8), figscale=1.0, style=s, title='USDCAD', warn_too_much_data=100000)\n",
    "\n",
    "# Cria o gráfico de candlestick com um gráfico de barras de volume\n",
    "mpf.plot(usd_cad, **kwargs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "tags": []
   },
   "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>Gmt time</th>\n",
       "      <th>Open</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Close</th>\n",
       "      <th>Volume</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>01.01.2015 00:00:00.000</td>\n",
       "      <td>56.83</td>\n",
       "      <td>56.83</td>\n",
       "      <td>56.83</td>\n",
       "      <td>56.83</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>01.01.2015 01:00:00.000</td>\n",
       "      <td>56.83</td>\n",
       "      <td>56.83</td>\n",
       "      <td>56.83</td>\n",
       "      <td>56.83</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>01.01.2015 02:00:00.000</td>\n",
       "      <td>56.83</td>\n",
       "      <td>56.83</td>\n",
       "      <td>56.83</td>\n",
       "      <td>56.83</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>01.01.2015 03:00:00.000</td>\n",
       "      <td>56.83</td>\n",
       "      <td>56.83</td>\n",
       "      <td>56.83</td>\n",
       "      <td>56.83</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>01.01.2015 04:00:00.000</td>\n",
       "      <td>56.83</td>\n",
       "      <td>56.83</td>\n",
       "      <td>56.83</td>\n",
       "      <td>56.83</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  Gmt time   Open   High    Low  Close  Volume\n",
       "0  01.01.2015 00:00:00.000  56.83  56.83  56.83  56.83     0.0\n",
       "1  01.01.2015 01:00:00.000  56.83  56.83  56.83  56.83     0.0\n",
       "2  01.01.2015 02:00:00.000  56.83  56.83  56.83  56.83     0.0\n",
       "3  01.01.2015 03:00:00.000  56.83  56.83  56.83  56.83     0.0\n",
       "4  01.01.2015 04:00:00.000  56.83  56.83  56.83  56.83     0.0"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "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>Gmt time</th>\n",
       "      <th>Open</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Close</th>\n",
       "      <th>Volume</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>21380</th>\n",
       "      <td>31.05.2018 19:00:00.000</td>\n",
       "      <td>77.835</td>\n",
       "      <td>77.865</td>\n",
       "      <td>77.625</td>\n",
       "      <td>77.655</td>\n",
       "      <td>102294.9964</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21381</th>\n",
       "      <td>31.05.2018 20:00:00.000</td>\n",
       "      <td>77.655</td>\n",
       "      <td>77.788</td>\n",
       "      <td>77.535</td>\n",
       "      <td>77.725</td>\n",
       "      <td>125650.0036</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21382</th>\n",
       "      <td>31.05.2018 21:00:00.000</td>\n",
       "      <td>77.725</td>\n",
       "      <td>77.725</td>\n",
       "      <td>77.725</td>\n",
       "      <td>77.725</td>\n",
       "      <td>0.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21383</th>\n",
       "      <td>31.05.2018 22:00:00.000</td>\n",
       "      <td>77.725</td>\n",
       "      <td>77.725</td>\n",
       "      <td>77.725</td>\n",
       "      <td>77.725</td>\n",
       "      <td>0.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21384</th>\n",
       "      <td>31.05.2018 23:00:00.000</td>\n",
       "      <td>77.725</td>\n",
       "      <td>77.725</td>\n",
       "      <td>77.725</td>\n",
       "      <td>77.725</td>\n",
       "      <td>0.0000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                      Gmt time    Open    High     Low   Close       Volume\n",
       "21380  31.05.2018 19:00:00.000  77.835  77.865  77.625  77.655  102294.9964\n",
       "21381  31.05.2018 20:00:00.000  77.655  77.788  77.535  77.725  125650.0036\n",
       "21382  31.05.2018 21:00:00.000  77.725  77.725  77.725  77.725       0.0000\n",
       "21383  31.05.2018 22:00:00.000  77.725  77.725  77.725  77.725       0.0000\n",
       "21384  31.05.2018 23:00:00.000  77.725  77.725  77.725  77.725       0.0000"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Oil data\n",
    "\n",
    "df_oil = pd.DataFrame(o)\n",
    "display(df_oil.head())\n",
    "display(df_oil.tail())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "ename": "KeyError",
     "evalue": "'Gmt time'",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
      "File \u001b[1;32m~\\anaconda3\\lib\\site-packages\\pandas\\core\\indexes\\base.py:3802\u001b[0m, in \u001b[0;36mIndex.get_loc\u001b[1;34m(self, key, method, tolerance)\u001b[0m\n\u001b[0;32m   3801\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m-> 3802\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_engine\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget_loc\u001b[49m\u001b[43m(\u001b[49m\u001b[43mcasted_key\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m   3803\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mKeyError\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m err:\n",
      "File \u001b[1;32m~\\anaconda3\\lib\\site-packages\\pandas\\_libs\\index.pyx:138\u001b[0m, in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[1;34m()\u001b[0m\n",
      "File \u001b[1;32m~\\anaconda3\\lib\\site-packages\\pandas\\_libs\\index.pyx:165\u001b[0m, in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[1;34m()\u001b[0m\n",
      "File \u001b[1;32mpandas\\_libs\\hashtable_class_helper.pxi:5745\u001b[0m, in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[1;34m()\u001b[0m\n",
      "File \u001b[1;32mpandas\\_libs\\hashtable_class_helper.pxi:5753\u001b[0m, in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[1;34m()\u001b[0m\n",
      "\u001b[1;31mKeyError\u001b[0m: 'Gmt time'",
      "\nThe above exception was the direct cause of the following exception:\n",
      "\u001b[1;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
      "Cell \u001b[1;32mIn[36], line 2\u001b[0m\n\u001b[0;32m      1\u001b[0m oil \u001b[38;5;241m=\u001b[39m df_oil\u001b[38;5;241m.\u001b[39mcopy()\n\u001b[1;32m----> 2\u001b[0m oil\u001b[38;5;241m.\u001b[39mindex \u001b[38;5;241m=\u001b[39m pd\u001b[38;5;241m.\u001b[39mto_datetime(\u001b[43moil\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mGmt time\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m]\u001b[49m)\n\u001b[0;32m      4\u001b[0m \u001b[38;5;66;03m# Renomeia as colunas para atender aos requisitos do mplfinance\u001b[39;00m\n\u001b[0;32m      5\u001b[0m oil \u001b[38;5;241m=\u001b[39m oil\u001b[38;5;241m.\u001b[39mrename(columns\u001b[38;5;241m=\u001b[39m{\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mOpen\u001b[39m\u001b[38;5;124m'\u001b[39m:\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mopen\u001b[39m\u001b[38;5;124m'\u001b[39m, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mHigh\u001b[39m\u001b[38;5;124m'\u001b[39m:\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mhigh\u001b[39m\u001b[38;5;124m'\u001b[39m, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mLow\u001b[39m\u001b[38;5;124m'\u001b[39m:\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mlow\u001b[39m\u001b[38;5;124m'\u001b[39m, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mClose\u001b[39m\u001b[38;5;124m'\u001b[39m:\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mclose\u001b[39m\u001b[38;5;124m'\u001b[39m, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mVolume\u001b[39m\u001b[38;5;124m'\u001b[39m:\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mvolume\u001b[39m\u001b[38;5;124m'\u001b[39m})\n",
      "File \u001b[1;32m~\\anaconda3\\lib\\site-packages\\pandas\\core\\frame.py:3807\u001b[0m, in \u001b[0;36mDataFrame.__getitem__\u001b[1;34m(self, key)\u001b[0m\n\u001b[0;32m   3805\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mcolumns\u001b[38;5;241m.\u001b[39mnlevels \u001b[38;5;241m>\u001b[39m \u001b[38;5;241m1\u001b[39m:\n\u001b[0;32m   3806\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_getitem_multilevel(key)\n\u001b[1;32m-> 3807\u001b[0m indexer \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcolumns\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget_loc\u001b[49m\u001b[43m(\u001b[49m\u001b[43mkey\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m   3808\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m is_integer(indexer):\n\u001b[0;32m   3809\u001b[0m     indexer \u001b[38;5;241m=\u001b[39m [indexer]\n",
      "File \u001b[1;32m~\\anaconda3\\lib\\site-packages\\pandas\\core\\indexes\\base.py:3804\u001b[0m, in \u001b[0;36mIndex.get_loc\u001b[1;34m(self, key, method, tolerance)\u001b[0m\n\u001b[0;32m   3802\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_engine\u001b[38;5;241m.\u001b[39mget_loc(casted_key)\n\u001b[0;32m   3803\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mKeyError\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m err:\n\u001b[1;32m-> 3804\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mKeyError\u001b[39;00m(key) \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01merr\u001b[39;00m\n\u001b[0;32m   3805\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mTypeError\u001b[39;00m:\n\u001b[0;32m   3806\u001b[0m     \u001b[38;5;66;03m# If we have a listlike key, _check_indexing_error will raise\u001b[39;00m\n\u001b[0;32m   3807\u001b[0m     \u001b[38;5;66;03m#  InvalidIndexError. Otherwise we fall through and re-raise\u001b[39;00m\n\u001b[0;32m   3808\u001b[0m     \u001b[38;5;66;03m#  the TypeError.\u001b[39;00m\n\u001b[0;32m   3809\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_check_indexing_error(key)\n",
      "\u001b[1;31mKeyError\u001b[0m: 'Gmt time'"
     ]
    }
   ],
   "source": [
    "oil = df_oil.copy()\n",
    "oil.index = pd.to_datetime(oil['Gmt time'])\n",
    "\n",
    "# Renomeia as colunas para atender aos requisitos do mplfinance\n",
    "oil = oil.rename(columns={'Open':'open', 'High':'high', 'Low':'low', 'Close':'close', 'Volume':'volume'})\n",
    "\n",
    "# Define os parâmetros para o gráfico de candlestick\n",
    "mc = mpf.make_marketcolors(up='g', down='r', wick='inherit', edge='inherit')\n",
    "s = mpf.make_mpf_style(marketcolors=mc)\n",
    "kwargs = dict(type='candle', volume=True, figratio=(12,8), figscale=1.0, style=s, title='Oil',warn_too_much_data=100000)\n",
    "\n",
    "# Cria o gráfico de candlestick com um gráfico de barras de volume\n",
    "mpf.plot(oil, **kwargs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Set the date as datetime\n",
    "df['datetime'] = pd.to_datetime(df['Gmt time'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "tags": []
   },
   "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>Gmt time</th>\n",
       "      <th>Open</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Close</th>\n",
       "      <th>Volume</th>\n",
       "      <th>datetime</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2016-01-01 00:00:00</th>\n",
       "      <td>01.01.2016 00:00:00.000</td>\n",
       "      <td>1.38326</td>\n",
       "      <td>1.38326</td>\n",
       "      <td>1.38326</td>\n",
       "      <td>1.38326</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>2016-01-01 00:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-01 01:00:00</th>\n",
       "      <td>01.01.2016 01:00:00.000</td>\n",
       "      <td>1.38326</td>\n",
       "      <td>1.38326</td>\n",
       "      <td>1.38326</td>\n",
       "      <td>1.38326</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>2016-01-01 01:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-01 02:00:00</th>\n",
       "      <td>01.01.2016 02:00:00.000</td>\n",
       "      <td>1.38326</td>\n",
       "      <td>1.38326</td>\n",
       "      <td>1.38326</td>\n",
       "      <td>1.38326</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>2016-01-01 02:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-01 03:00:00</th>\n",
       "      <td>01.01.2016 03:00:00.000</td>\n",
       "      <td>1.38326</td>\n",
       "      <td>1.38326</td>\n",
       "      <td>1.38326</td>\n",
       "      <td>1.38326</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>2016-01-01 03:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-01 04:00:00</th>\n",
       "      <td>01.01.2016 04:00:00.000</td>\n",
       "      <td>1.38326</td>\n",
       "      <td>1.38326</td>\n",
       "      <td>1.38326</td>\n",
       "      <td>1.38326</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>2016-01-01 04:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-05-31 19:00:00</th>\n",
       "      <td>31.05.2018 19:00:00.000</td>\n",
       "      <td>1.29566</td>\n",
       "      <td>1.29671</td>\n",
       "      <td>1.29541</td>\n",
       "      <td>1.29666</td>\n",
       "      <td>5.330320e+09</td>\n",
       "      <td>2018-05-31 19:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-05-31 20:00:00</th>\n",
       "      <td>31.05.2018 20:00:00.000</td>\n",
       "      <td>1.29668</td>\n",
       "      <td>1.29682</td>\n",
       "      <td>1.29502</td>\n",
       "      <td>1.29563</td>\n",
       "      <td>3.116830e+09</td>\n",
       "      <td>2018-05-31 20:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-05-31 21:00:00</th>\n",
       "      <td>31.05.2018 21:00:00.000</td>\n",
       "      <td>1.29563</td>\n",
       "      <td>1.29577</td>\n",
       "      <td>1.29498</td>\n",
       "      <td>1.29564</td>\n",
       "      <td>1.456650e+09</td>\n",
       "      <td>2018-05-31 21:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-05-31 22:00:00</th>\n",
       "      <td>31.05.2018 22:00:00.000</td>\n",
       "      <td>1.29563</td>\n",
       "      <td>1.29563</td>\n",
       "      <td>1.29407</td>\n",
       "      <td>1.29409</td>\n",
       "      <td>1.370330e+09</td>\n",
       "      <td>2018-05-31 22:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-05-31 23:00:00</th>\n",
       "      <td>31.05.2018 23:00:00.000</td>\n",
       "      <td>1.29409</td>\n",
       "      <td>1.29469</td>\n",
       "      <td>1.29406</td>\n",
       "      <td>1.29436</td>\n",
       "      <td>1.259850e+09</td>\n",
       "      <td>2018-05-31 23:00:00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>15121 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                    Gmt time     Open     High      Low  \\\n",
       "2016-01-01 00:00:00  01.01.2016 00:00:00.000  1.38326  1.38326  1.38326   \n",
       "2016-01-01 01:00:00  01.01.2016 01:00:00.000  1.38326  1.38326  1.38326   \n",
       "2016-01-01 02:00:00  01.01.2016 02:00:00.000  1.38326  1.38326  1.38326   \n",
       "2016-01-01 03:00:00  01.01.2016 03:00:00.000  1.38326  1.38326  1.38326   \n",
       "2016-01-01 04:00:00  01.01.2016 04:00:00.000  1.38326  1.38326  1.38326   \n",
       "...                                      ...      ...      ...      ...   \n",
       "2018-05-31 19:00:00  31.05.2018 19:00:00.000  1.29566  1.29671  1.29541   \n",
       "2018-05-31 20:00:00  31.05.2018 20:00:00.000  1.29668  1.29682  1.29502   \n",
       "2018-05-31 21:00:00  31.05.2018 21:00:00.000  1.29563  1.29577  1.29498   \n",
       "2018-05-31 22:00:00  31.05.2018 22:00:00.000  1.29563  1.29563  1.29407   \n",
       "2018-05-31 23:00:00  31.05.2018 23:00:00.000  1.29409  1.29469  1.29406   \n",
       "\n",
       "                       Close        Volume            datetime  \n",
       "2016-01-01 00:00:00  1.38326  0.000000e+00 2016-01-01 00:00:00  \n",
       "2016-01-01 01:00:00  1.38326  0.000000e+00 2016-01-01 01:00:00  \n",
       "2016-01-01 02:00:00  1.38326  0.000000e+00 2016-01-01 02:00:00  \n",
       "2016-01-01 03:00:00  1.38326  0.000000e+00 2016-01-01 03:00:00  \n",
       "2016-01-01 04:00:00  1.38326  0.000000e+00 2016-01-01 04:00:00  \n",
       "...                      ...           ...                 ...  \n",
       "2018-05-31 19:00:00  1.29666  5.330320e+09 2018-05-31 19:00:00  \n",
       "2018-05-31 20:00:00  1.29563  3.116830e+09 2018-05-31 20:00:00  \n",
       "2018-05-31 21:00:00  1.29564  1.456650e+09 2018-05-31 21:00:00  \n",
       "2018-05-31 22:00:00  1.29409  1.370330e+09 2018-05-31 22:00:00  \n",
       "2018-05-31 23:00:00  1.29436  1.259850e+09 2018-05-31 23:00:00  \n",
       "\n",
       "[15121 rows x 7 columns]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Colocando a data como index\n",
    "df = df.set_index(df['datetime'])\n",
    "df.index.names = [None]\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "tags": []
   },
   "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>Gmt time</th>\n",
       "      <th>Open</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Close</th>\n",
       "      <th>Volume</th>\n",
       "      <th>datetime</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2015-01-01 00:00:00</th>\n",
       "      <td>01.01.2015 00:00:00.000</td>\n",
       "      <td>56.830</td>\n",
       "      <td>56.830</td>\n",
       "      <td>56.830</td>\n",
       "      <td>56.830</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>2015-01-01 00:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-01-01 01:00:00</th>\n",
       "      <td>01.01.2015 01:00:00.000</td>\n",
       "      <td>56.830</td>\n",
       "      <td>56.830</td>\n",
       "      <td>56.830</td>\n",
       "      <td>56.830</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>2015-01-01 01:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-01-01 02:00:00</th>\n",
       "      <td>01.01.2015 02:00:00.000</td>\n",
       "      <td>56.830</td>\n",
       "      <td>56.830</td>\n",
       "      <td>56.830</td>\n",
       "      <td>56.830</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>2015-01-01 02:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-01-01 03:00:00</th>\n",
       "      <td>01.01.2015 03:00:00.000</td>\n",
       "      <td>56.830</td>\n",
       "      <td>56.830</td>\n",
       "      <td>56.830</td>\n",
       "      <td>56.830</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>2015-01-01 03:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-01-01 04:00:00</th>\n",
       "      <td>01.01.2015 04:00:00.000</td>\n",
       "      <td>56.830</td>\n",
       "      <td>56.830</td>\n",
       "      <td>56.830</td>\n",
       "      <td>56.830</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>2015-01-01 04:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-05-31 19:00:00</th>\n",
       "      <td>31.05.2018 19:00:00.000</td>\n",
       "      <td>77.835</td>\n",
       "      <td>77.865</td>\n",
       "      <td>77.625</td>\n",
       "      <td>77.655</td>\n",
       "      <td>102294.9964</td>\n",
       "      <td>2018-05-31 19:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-05-31 20:00:00</th>\n",
       "      <td>31.05.2018 20:00:00.000</td>\n",
       "      <td>77.655</td>\n",
       "      <td>77.788</td>\n",
       "      <td>77.535</td>\n",
       "      <td>77.725</td>\n",
       "      <td>125650.0036</td>\n",
       "      <td>2018-05-31 20:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-05-31 21:00:00</th>\n",
       "      <td>31.05.2018 21:00:00.000</td>\n",
       "      <td>77.725</td>\n",
       "      <td>77.725</td>\n",
       "      <td>77.725</td>\n",
       "      <td>77.725</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>2018-05-31 21:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-05-31 22:00:00</th>\n",
       "      <td>31.05.2018 22:00:00.000</td>\n",
       "      <td>77.725</td>\n",
       "      <td>77.725</td>\n",
       "      <td>77.725</td>\n",
       "      <td>77.725</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>2018-05-31 22:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-05-31 23:00:00</th>\n",
       "      <td>31.05.2018 23:00:00.000</td>\n",
       "      <td>77.725</td>\n",
       "      <td>77.725</td>\n",
       "      <td>77.725</td>\n",
       "      <td>77.725</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>2018-05-31 23:00:00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>21385 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                    Gmt time    Open    High     Low   Close  \\\n",
       "2015-01-01 00:00:00  01.01.2015 00:00:00.000  56.830  56.830  56.830  56.830   \n",
       "2015-01-01 01:00:00  01.01.2015 01:00:00.000  56.830  56.830  56.830  56.830   \n",
       "2015-01-01 02:00:00  01.01.2015 02:00:00.000  56.830  56.830  56.830  56.830   \n",
       "2015-01-01 03:00:00  01.01.2015 03:00:00.000  56.830  56.830  56.830  56.830   \n",
       "2015-01-01 04:00:00  01.01.2015 04:00:00.000  56.830  56.830  56.830  56.830   \n",
       "...                                      ...     ...     ...     ...     ...   \n",
       "2018-05-31 19:00:00  31.05.2018 19:00:00.000  77.835  77.865  77.625  77.655   \n",
       "2018-05-31 20:00:00  31.05.2018 20:00:00.000  77.655  77.788  77.535  77.725   \n",
       "2018-05-31 21:00:00  31.05.2018 21:00:00.000  77.725  77.725  77.725  77.725   \n",
       "2018-05-31 22:00:00  31.05.2018 22:00:00.000  77.725  77.725  77.725  77.725   \n",
       "2018-05-31 23:00:00  31.05.2018 23:00:00.000  77.725  77.725  77.725  77.725   \n",
       "\n",
       "                          Volume            datetime  \n",
       "2015-01-01 00:00:00       0.0000 2015-01-01 00:00:00  \n",
       "2015-01-01 01:00:00       0.0000 2015-01-01 01:00:00  \n",
       "2015-01-01 02:00:00       0.0000 2015-01-01 02:00:00  \n",
       "2015-01-01 03:00:00       0.0000 2015-01-01 03:00:00  \n",
       "2015-01-01 04:00:00       0.0000 2015-01-01 04:00:00  \n",
       "...                          ...                 ...  \n",
       "2018-05-31 19:00:00  102294.9964 2018-05-31 19:00:00  \n",
       "2018-05-31 20:00:00  125650.0036 2018-05-31 20:00:00  \n",
       "2018-05-31 21:00:00       0.0000 2018-05-31 21:00:00  \n",
       "2018-05-31 22:00:00       0.0000 2018-05-31 22:00:00  \n",
       "2018-05-31 23:00:00       0.0000 2018-05-31 23:00:00  \n",
       "\n",
       "[21385 rows x 7 columns]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Set datetime for oil\n",
    "\n",
    "df_oil['datetime'] = pd.to_datetime(df_oil['Gmt time'])\n",
    "df_oil = df_oil.set_index(df_oil['datetime'])\n",
    "df_oil.index.names = [None]\n",
    "df_oil"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "tags": []
   },
   "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>Open_oil</th>\n",
       "      <th>High_oil</th>\n",
       "      <th>Low_oil</th>\n",
       "      <th>Close_oil</th>\n",
       "      <th>Volume_oil</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2018-05-31 19:00:00</th>\n",
       "      <td>77.835</td>\n",
       "      <td>77.865</td>\n",
       "      <td>77.625</td>\n",
       "      <td>77.655</td>\n",
       "      <td>102294.9964</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-05-31 20:00:00</th>\n",
       "      <td>77.655</td>\n",
       "      <td>77.788</td>\n",
       "      <td>77.535</td>\n",
       "      <td>77.725</td>\n",
       "      <td>125650.0036</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-05-31 21:00:00</th>\n",
       "      <td>77.725</td>\n",
       "      <td>77.725</td>\n",
       "      <td>77.725</td>\n",
       "      <td>77.725</td>\n",
       "      <td>0.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-05-31 22:00:00</th>\n",
       "      <td>77.725</td>\n",
       "      <td>77.725</td>\n",
       "      <td>77.725</td>\n",
       "      <td>77.725</td>\n",
       "      <td>0.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-05-31 23:00:00</th>\n",
       "      <td>77.725</td>\n",
       "      <td>77.725</td>\n",
       "      <td>77.725</td>\n",
       "      <td>77.725</td>\n",
       "      <td>0.0000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     Open_oil  High_oil  Low_oil  Close_oil   Volume_oil\n",
       "2018-05-31 19:00:00    77.835    77.865   77.625     77.655  102294.9964\n",
       "2018-05-31 20:00:00    77.655    77.788   77.535     77.725  125650.0036\n",
       "2018-05-31 21:00:00    77.725    77.725   77.725     77.725       0.0000\n",
       "2018-05-31 22:00:00    77.725    77.725   77.725     77.725       0.0000\n",
       "2018-05-31 23:00:00    77.725    77.725   77.725     77.725       0.0000"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_oil = df_oil.loc[:,['Open', 'High', 'Low', 'Close', 'Volume']]\n",
    "\n",
    "df_oil = df_oil.rename(columns={'Open': 'Open_oil', 'High': 'High_oil', 'Low':'Low_oil', 'Close':'Close_oil', 'Volume':'Volume_oil'})\n",
    "\n",
    "df_oil.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "scrolled": true,
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: >"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Just some exploration \n",
    "\n",
    "df['Close'].plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: >"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_oil['Close_oil'].plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1000x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create return (close-open) normalized\n",
    "\n",
    "df['return_next'] = df['Close'].shift(-1) - df['Open'].shift(-1)\n",
    "return_range = df['return_next'].max() - df['return_next'].min()\n",
    "df['return_next'] = df['return_next'] / return_range\n",
    "\n",
    "df['return'] = df['Close'] - df['Open']\n",
    "return_range = df['return'].max() - df['return'].min()\n",
    "df['return'] = (df['return'] - df['return'].min()) / return_range\n",
    "\n",
    "df.plot(y='return', figsize=(10,4))\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "tags": []
   },
   "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>Gmt time</th>\n",
       "      <th>Open</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Close</th>\n",
       "      <th>Volume</th>\n",
       "      <th>datetime</th>\n",
       "      <th>return_next</th>\n",
       "      <th>return</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2016-01-01 00:00:00</th>\n",
       "      <td>01.01.2016 01:00:00.000</td>\n",
       "      <td>1.38326</td>\n",
       "      <td>1.38326</td>\n",
       "      <td>1.38326</td>\n",
       "      <td>1.38326</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2016-01-01 01:00:00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.57152</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-01 01:00:00</th>\n",
       "      <td>01.01.2016 02:00:00.000</td>\n",
       "      <td>1.38326</td>\n",
       "      <td>1.38326</td>\n",
       "      <td>1.38326</td>\n",
       "      <td>1.38326</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2016-01-01 02:00:00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.57152</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-01 02:00:00</th>\n",
       "      <td>01.01.2016 03:00:00.000</td>\n",
       "      <td>1.38326</td>\n",
       "      <td>1.38326</td>\n",
       "      <td>1.38326</td>\n",
       "      <td>1.38326</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2016-01-01 03:00:00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.57152</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-01 03:00:00</th>\n",
       "      <td>01.01.2016 04:00:00.000</td>\n",
       "      <td>1.38326</td>\n",
       "      <td>1.38326</td>\n",
       "      <td>1.38326</td>\n",
       "      <td>1.38326</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2016-01-01 04:00:00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.57152</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-01 04:00:00</th>\n",
       "      <td>01.01.2016 05:00:00.000</td>\n",
       "      <td>1.38326</td>\n",
       "      <td>1.38326</td>\n",
       "      <td>1.38326</td>\n",
       "      <td>1.38326</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2016-01-01 05:00:00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.57152</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                    Gmt time     Open     High      Low  \\\n",
       "2016-01-01 00:00:00  01.01.2016 01:00:00.000  1.38326  1.38326  1.38326   \n",
       "2016-01-01 01:00:00  01.01.2016 02:00:00.000  1.38326  1.38326  1.38326   \n",
       "2016-01-01 02:00:00  01.01.2016 03:00:00.000  1.38326  1.38326  1.38326   \n",
       "2016-01-01 03:00:00  01.01.2016 04:00:00.000  1.38326  1.38326  1.38326   \n",
       "2016-01-01 04:00:00  01.01.2016 05:00:00.000  1.38326  1.38326  1.38326   \n",
       "\n",
       "                       Close  Volume            datetime  return_next   return  \n",
       "2016-01-01 00:00:00  1.38326     0.0 2016-01-01 01:00:00          0.0  0.57152  \n",
       "2016-01-01 01:00:00  1.38326     0.0 2016-01-01 02:00:00          0.0  0.57152  \n",
       "2016-01-01 02:00:00  1.38326     0.0 2016-01-01 03:00:00          0.0  0.57152  \n",
       "2016-01-01 03:00:00  1.38326     0.0 2016-01-01 04:00:00          0.0  0.57152  \n",
       "2016-01-01 04:00:00  1.38326     0.0 2016-01-01 05:00:00          0.0  0.57152  "
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shift(-1).head()"
   ]
  },
  {
   "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>Gmt time</th>\n",
       "      <th>Open</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Close</th>\n",
       "      <th>Volume</th>\n",
       "      <th>datetime</th>\n",
       "      <th>return_next</th>\n",
       "      <th>return</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2018-05-31 19:00:00</th>\n",
       "      <td>31.05.2018 20:00:00.000</td>\n",
       "      <td>1.29668</td>\n",
       "      <td>1.29682</td>\n",
       "      <td>1.29502</td>\n",
       "      <td>1.29563</td>\n",
       "      <td>3.116830e+09</td>\n",
       "      <td>2018-05-31 20:00:00</td>\n",
       "      <td>0.000321</td>\n",
       "      <td>0.537769</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-05-31 20:00:00</th>\n",
       "      <td>31.05.2018 21:00:00.000</td>\n",
       "      <td>1.29563</td>\n",
       "      <td>1.29577</td>\n",
       "      <td>1.29498</td>\n",
       "      <td>1.29564</td>\n",
       "      <td>1.456650e+09</td>\n",
       "      <td>2018-05-31 21:00:00</td>\n",
       "      <td>-0.049502</td>\n",
       "      <td>0.571842</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-05-31 21:00:00</th>\n",
       "      <td>31.05.2018 22:00:00.000</td>\n",
       "      <td>1.29563</td>\n",
       "      <td>1.29563</td>\n",
       "      <td>1.29407</td>\n",
       "      <td>1.29409</td>\n",
       "      <td>1.370330e+09</td>\n",
       "      <td>2018-05-31 22:00:00</td>\n",
       "      <td>0.008679</td>\n",
       "      <td>0.522019</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-05-31 22:00:00</th>\n",
       "      <td>31.05.2018 23:00:00.000</td>\n",
       "      <td>1.29409</td>\n",
       "      <td>1.29469</td>\n",
       "      <td>1.29406</td>\n",
       "      <td>1.29436</td>\n",
       "      <td>1.259850e+09</td>\n",
       "      <td>2018-05-31 23:00:00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.580199</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-05-31 23:00:00</th>\n",
       "      <td>None</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaT</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                    Gmt time     Open     High      Low  \\\n",
       "2018-05-31 19:00:00  31.05.2018 20:00:00.000  1.29668  1.29682  1.29502   \n",
       "2018-05-31 20:00:00  31.05.2018 21:00:00.000  1.29563  1.29577  1.29498   \n",
       "2018-05-31 21:00:00  31.05.2018 22:00:00.000  1.29563  1.29563  1.29407   \n",
       "2018-05-31 22:00:00  31.05.2018 23:00:00.000  1.29409  1.29469  1.29406   \n",
       "2018-05-31 23:00:00                     None      NaN      NaN      NaN   \n",
       "\n",
       "                       Close        Volume            datetime  return_next  \\\n",
       "2018-05-31 19:00:00  1.29563  3.116830e+09 2018-05-31 20:00:00     0.000321   \n",
       "2018-05-31 20:00:00  1.29564  1.456650e+09 2018-05-31 21:00:00    -0.049502   \n",
       "2018-05-31 21:00:00  1.29409  1.370330e+09 2018-05-31 22:00:00     0.008679   \n",
       "2018-05-31 22:00:00  1.29436  1.259850e+09 2018-05-31 23:00:00          NaN   \n",
       "2018-05-31 23:00:00      NaN           NaN                 NaT          NaN   \n",
       "\n",
       "                       return  \n",
       "2018-05-31 19:00:00  0.537769  \n",
       "2018-05-31 20:00:00  0.571842  \n",
       "2018-05-31 21:00:00  0.522019  \n",
       "2018-05-31 22:00:00  0.580199  \n",
       "2018-05-31 23:00:00       NaN  "
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shift(-1).tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "tags": []
   },
   "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>Gmt time</th>\n",
       "      <th>Open</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Close</th>\n",
       "      <th>Volume</th>\n",
       "      <th>datetime</th>\n",
       "      <th>return_next</th>\n",
       "      <th>return</th>\n",
       "      <th>label</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2018-05-31 14:00:00</th>\n",
       "      <td>31.05.2018 14:00:00.000</td>\n",
       "      <td>1.29518</td>\n",
       "      <td>1.29770</td>\n",
       "      <td>1.29430</td>\n",
       "      <td>1.29718</td>\n",
       "      <td>9.841480e+09</td>\n",
       "      <td>2018-05-31 14:00:00</td>\n",
       "      <td>-0.005464</td>\n",
       "      <td>0.635808</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-05-31 15:00:00</th>\n",
       "      <td>31.05.2018 15:00:00.000</td>\n",
       "      <td>1.29716</td>\n",
       "      <td>1.29903</td>\n",
       "      <td>1.29586</td>\n",
       "      <td>1.29699</td>\n",
       "      <td>1.169860e+10</td>\n",
       "      <td>2018-05-31 15:00:00</td>\n",
       "      <td>-0.045002</td>\n",
       "      <td>0.566056</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-05-31 16:00:00</th>\n",
       "      <td>31.05.2018 16:00:00.000</td>\n",
       "      <td>1.29699</td>\n",
       "      <td>1.29750</td>\n",
       "      <td>1.29555</td>\n",
       "      <td>1.29559</td>\n",
       "      <td>7.277670e+09</td>\n",
       "      <td>2018-05-31 16:00:00</td>\n",
       "      <td>0.072324</td>\n",
       "      <td>0.526519</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-05-31 17:00:00</th>\n",
       "      <td>31.05.2018 17:00:00.000</td>\n",
       "      <td>1.29560</td>\n",
       "      <td>1.29803</td>\n",
       "      <td>1.29424</td>\n",
       "      <td>1.29785</td>\n",
       "      <td>7.262840e+09</td>\n",
       "      <td>2018-05-31 17:00:00</td>\n",
       "      <td>-0.071038</td>\n",
       "      <td>0.643844</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-05-31 18:00:00</th>\n",
       "      <td>31.05.2018 18:00:00.000</td>\n",
       "      <td>1.29785</td>\n",
       "      <td>1.29884</td>\n",
       "      <td>1.29538</td>\n",
       "      <td>1.29564</td>\n",
       "      <td>6.044530e+09</td>\n",
       "      <td>2018-05-31 18:00:00</td>\n",
       "      <td>0.032144</td>\n",
       "      <td>0.500482</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-05-31 19:00:00</th>\n",
       "      <td>31.05.2018 19:00:00.000</td>\n",
       "      <td>1.29566</td>\n",
       "      <td>1.29671</td>\n",
       "      <td>1.29541</td>\n",
       "      <td>1.29666</td>\n",
       "      <td>5.330320e+09</td>\n",
       "      <td>2018-05-31 19:00:00</td>\n",
       "      <td>-0.033751</td>\n",
       "      <td>0.603664</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-05-31 20:00:00</th>\n",
       "      <td>31.05.2018 20:00:00.000</td>\n",
       "      <td>1.29668</td>\n",
       "      <td>1.29682</td>\n",
       "      <td>1.29502</td>\n",
       "      <td>1.29563</td>\n",
       "      <td>3.116830e+09</td>\n",
       "      <td>2018-05-31 20:00:00</td>\n",
       "      <td>0.000321</td>\n",
       "      <td>0.537769</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-05-31 21:00:00</th>\n",
       "      <td>31.05.2018 21:00:00.000</td>\n",
       "      <td>1.29563</td>\n",
       "      <td>1.29577</td>\n",
       "      <td>1.29498</td>\n",
       "      <td>1.29564</td>\n",
       "      <td>1.456650e+09</td>\n",
       "      <td>2018-05-31 21:00:00</td>\n",
       "      <td>-0.049502</td>\n",
       "      <td>0.571842</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-05-31 22:00:00</th>\n",
       "      <td>31.05.2018 22:00:00.000</td>\n",
       "      <td>1.29563</td>\n",
       "      <td>1.29563</td>\n",
       "      <td>1.29407</td>\n",
       "      <td>1.29409</td>\n",
       "      <td>1.370330e+09</td>\n",
       "      <td>2018-05-31 22:00:00</td>\n",
       "      <td>0.008679</td>\n",
       "      <td>0.522019</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-05-31 23:00:00</th>\n",
       "      <td>31.05.2018 23:00:00.000</td>\n",
       "      <td>1.29409</td>\n",
       "      <td>1.29469</td>\n",
       "      <td>1.29406</td>\n",
       "      <td>1.29436</td>\n",
       "      <td>1.259850e+09</td>\n",
       "      <td>2018-05-31 23:00:00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.580199</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                    Gmt time     Open     High      Low  \\\n",
       "2018-05-31 14:00:00  31.05.2018 14:00:00.000  1.29518  1.29770  1.29430   \n",
       "2018-05-31 15:00:00  31.05.2018 15:00:00.000  1.29716  1.29903  1.29586   \n",
       "2018-05-31 16:00:00  31.05.2018 16:00:00.000  1.29699  1.29750  1.29555   \n",
       "2018-05-31 17:00:00  31.05.2018 17:00:00.000  1.29560  1.29803  1.29424   \n",
       "2018-05-31 18:00:00  31.05.2018 18:00:00.000  1.29785  1.29884  1.29538   \n",
       "2018-05-31 19:00:00  31.05.2018 19:00:00.000  1.29566  1.29671  1.29541   \n",
       "2018-05-31 20:00:00  31.05.2018 20:00:00.000  1.29668  1.29682  1.29502   \n",
       "2018-05-31 21:00:00  31.05.2018 21:00:00.000  1.29563  1.29577  1.29498   \n",
       "2018-05-31 22:00:00  31.05.2018 22:00:00.000  1.29563  1.29563  1.29407   \n",
       "2018-05-31 23:00:00  31.05.2018 23:00:00.000  1.29409  1.29469  1.29406   \n",
       "\n",
       "                       Close        Volume            datetime  return_next  \\\n",
       "2018-05-31 14:00:00  1.29718  9.841480e+09 2018-05-31 14:00:00    -0.005464   \n",
       "2018-05-31 15:00:00  1.29699  1.169860e+10 2018-05-31 15:00:00    -0.045002   \n",
       "2018-05-31 16:00:00  1.29559  7.277670e+09 2018-05-31 16:00:00     0.072324   \n",
       "2018-05-31 17:00:00  1.29785  7.262840e+09 2018-05-31 17:00:00    -0.071038   \n",
       "2018-05-31 18:00:00  1.29564  6.044530e+09 2018-05-31 18:00:00     0.032144   \n",
       "2018-05-31 19:00:00  1.29666  5.330320e+09 2018-05-31 19:00:00    -0.033751   \n",
       "2018-05-31 20:00:00  1.29563  3.116830e+09 2018-05-31 20:00:00     0.000321   \n",
       "2018-05-31 21:00:00  1.29564  1.456650e+09 2018-05-31 21:00:00    -0.049502   \n",
       "2018-05-31 22:00:00  1.29409  1.370330e+09 2018-05-31 22:00:00     0.008679   \n",
       "2018-05-31 23:00:00  1.29436  1.259850e+09 2018-05-31 23:00:00          NaN   \n",
       "\n",
       "                       return  label  \n",
       "2018-05-31 14:00:00  0.635808      0  \n",
       "2018-05-31 15:00:00  0.566056      0  \n",
       "2018-05-31 16:00:00  0.526519      1  \n",
       "2018-05-31 17:00:00  0.643844      0  \n",
       "2018-05-31 18:00:00  0.500482      1  \n",
       "2018-05-31 19:00:00  0.603664      0  \n",
       "2018-05-31 20:00:00  0.537769      1  \n",
       "2018-05-31 21:00:00  0.571842      0  \n",
       "2018-05-31 22:00:00  0.522019      1  \n",
       "2018-05-31 23:00:00  0.580199      0  "
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Make label, 1 as rising price, 0 as falling price - prediction for the following timestamp\n",
    "# ****************** consider when return ==0 next version\n",
    "\n",
    "df['label'] = df['return_next'].apply(lambda x: 1 if x>0.0 else 0)\n",
    "df.tail(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "tags": []
   },
   "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>Gmt time</th>\n",
       "      <th>Open</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Close</th>\n",
       "      <th>Volume</th>\n",
       "      <th>datetime</th>\n",
       "      <th>return_next</th>\n",
       "      <th>return</th>\n",
       "      <th>label</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2018-05-31 18:00:00</th>\n",
       "      <td>31.05.2018 18:00:00.000</td>\n",
       "      <td>1.29785</td>\n",
       "      <td>1.29884</td>\n",
       "      <td>1.29538</td>\n",
       "      <td>1.29564</td>\n",
       "      <td>6.044530e+09</td>\n",
       "      <td>2018-05-31 18:00:00</td>\n",
       "      <td>0.032144</td>\n",
       "      <td>0.500482</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-05-31 19:00:00</th>\n",
       "      <td>31.05.2018 19:00:00.000</td>\n",
       "      <td>1.29566</td>\n",
       "      <td>1.29671</td>\n",
       "      <td>1.29541</td>\n",
       "      <td>1.29666</td>\n",
       "      <td>5.330320e+09</td>\n",
       "      <td>2018-05-31 19:00:00</td>\n",
       "      <td>-0.033751</td>\n",
       "      <td>0.603664</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-05-31 20:00:00</th>\n",
       "      <td>31.05.2018 20:00:00.000</td>\n",
       "      <td>1.29668</td>\n",
       "      <td>1.29682</td>\n",
       "      <td>1.29502</td>\n",
       "      <td>1.29563</td>\n",
       "      <td>3.116830e+09</td>\n",
       "      <td>2018-05-31 20:00:00</td>\n",
       "      <td>0.000321</td>\n",
       "      <td>0.537769</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-05-31 21:00:00</th>\n",
       "      <td>31.05.2018 21:00:00.000</td>\n",
       "      <td>1.29563</td>\n",
       "      <td>1.29577</td>\n",
       "      <td>1.29498</td>\n",
       "      <td>1.29564</td>\n",
       "      <td>1.456650e+09</td>\n",
       "      <td>2018-05-31 21:00:00</td>\n",
       "      <td>-0.049502</td>\n",
       "      <td>0.571842</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-05-31 22:00:00</th>\n",
       "      <td>31.05.2018 22:00:00.000</td>\n",
       "      <td>1.29563</td>\n",
       "      <td>1.29563</td>\n",
       "      <td>1.29407</td>\n",
       "      <td>1.29409</td>\n",
       "      <td>1.370330e+09</td>\n",
       "      <td>2018-05-31 22:00:00</td>\n",
       "      <td>0.008679</td>\n",
       "      <td>0.522019</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                    Gmt time     Open     High      Low  \\\n",
       "2018-05-31 18:00:00  31.05.2018 18:00:00.000  1.29785  1.29884  1.29538   \n",
       "2018-05-31 19:00:00  31.05.2018 19:00:00.000  1.29566  1.29671  1.29541   \n",
       "2018-05-31 20:00:00  31.05.2018 20:00:00.000  1.29668  1.29682  1.29502   \n",
       "2018-05-31 21:00:00  31.05.2018 21:00:00.000  1.29563  1.29577  1.29498   \n",
       "2018-05-31 22:00:00  31.05.2018 22:00:00.000  1.29563  1.29563  1.29407   \n",
       "\n",
       "                       Close        Volume            datetime  return_next  \\\n",
       "2018-05-31 18:00:00  1.29564  6.044530e+09 2018-05-31 18:00:00     0.032144   \n",
       "2018-05-31 19:00:00  1.29666  5.330320e+09 2018-05-31 19:00:00    -0.033751   \n",
       "2018-05-31 20:00:00  1.29563  3.116830e+09 2018-05-31 20:00:00     0.000321   \n",
       "2018-05-31 21:00:00  1.29564  1.456650e+09 2018-05-31 21:00:00    -0.049502   \n",
       "2018-05-31 22:00:00  1.29409  1.370330e+09 2018-05-31 22:00:00     0.008679   \n",
       "\n",
       "                       return  label  \n",
       "2018-05-31 18:00:00  0.500482      1  \n",
       "2018-05-31 19:00:00  0.603664      0  \n",
       "2018-05-31 20:00:00  0.537769      1  \n",
       "2018-05-31 21:00:00  0.571842      0  \n",
       "2018-05-31 22:00:00  0.522019      1  "
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# The last register shows a NaN\n",
    "\n",
    "df.dropna(inplace=True)\n",
    "df.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "tags": []
   },
   "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>Open</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Close</th>\n",
       "      <th>Volume</th>\n",
       "      <th>return</th>\n",
       "      <th>label</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2018-05-31 18:00:00</th>\n",
       "      <td>1.29785</td>\n",
       "      <td>1.29884</td>\n",
       "      <td>1.29538</td>\n",
       "      <td>1.29564</td>\n",
       "      <td>6.044530e+09</td>\n",
       "      <td>0.500482</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-05-31 19:00:00</th>\n",
       "      <td>1.29566</td>\n",
       "      <td>1.29671</td>\n",
       "      <td>1.29541</td>\n",
       "      <td>1.29666</td>\n",
       "      <td>5.330320e+09</td>\n",
       "      <td>0.603664</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-05-31 20:00:00</th>\n",
       "      <td>1.29668</td>\n",
       "      <td>1.29682</td>\n",
       "      <td>1.29502</td>\n",
       "      <td>1.29563</td>\n",
       "      <td>3.116830e+09</td>\n",
       "      <td>0.537769</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-05-31 21:00:00</th>\n",
       "      <td>1.29563</td>\n",
       "      <td>1.29577</td>\n",
       "      <td>1.29498</td>\n",
       "      <td>1.29564</td>\n",
       "      <td>1.456650e+09</td>\n",
       "      <td>0.571842</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-05-31 22:00:00</th>\n",
       "      <td>1.29563</td>\n",
       "      <td>1.29563</td>\n",
       "      <td>1.29407</td>\n",
       "      <td>1.29409</td>\n",
       "      <td>1.370330e+09</td>\n",
       "      <td>0.522019</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                        Open     High      Low    Close        Volume  \\\n",
       "2018-05-31 18:00:00  1.29785  1.29884  1.29538  1.29564  6.044530e+09   \n",
       "2018-05-31 19:00:00  1.29566  1.29671  1.29541  1.29666  5.330320e+09   \n",
       "2018-05-31 20:00:00  1.29668  1.29682  1.29502  1.29563  3.116830e+09   \n",
       "2018-05-31 21:00:00  1.29563  1.29577  1.29498  1.29564  1.456650e+09   \n",
       "2018-05-31 22:00:00  1.29563  1.29563  1.29407  1.29409  1.370330e+09   \n",
       "\n",
       "                       return  label  \n",
       "2018-05-31 18:00:00  0.500482      1  \n",
       "2018-05-31 19:00:00  0.603664      0  \n",
       "2018-05-31 20:00:00  0.537769      1  \n",
       "2018-05-31 21:00:00  0.571842      0  \n",
       "2018-05-31 22:00:00  0.522019      1  "
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2 = df.copy()\n",
    "\n",
    "df = df.loc[:,['Open', 'High', 'Low', 'Close', 'Volume', 'return', 'label']]\n",
    "df.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "tags": []
   },
   "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>Open</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Close</th>\n",
       "      <th>Volume</th>\n",
       "      <th>return</th>\n",
       "      <th>label</th>\n",
       "      <th>Open_oil</th>\n",
       "      <th>High_oil</th>\n",
       "      <th>Low_oil</th>\n",
       "      <th>Close_oil</th>\n",
       "      <th>Volume_oil</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2016-01-01 00:00:00</th>\n",
       "      <td>1.38326</td>\n",
       "      <td>1.38326</td>\n",
       "      <td>1.38326</td>\n",
       "      <td>1.38326</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.57152</td>\n",
       "      <td>0</td>\n",
       "      <td>37.704</td>\n",
       "      <td>37.704</td>\n",
       "      <td>37.704</td>\n",
       "      <td>37.704</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-01 01:00:00</th>\n",
       "      <td>1.38326</td>\n",
       "      <td>1.38326</td>\n",
       "      <td>1.38326</td>\n",
       "      <td>1.38326</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.57152</td>\n",
       "      <td>0</td>\n",
       "      <td>37.704</td>\n",
       "      <td>37.704</td>\n",
       "      <td>37.704</td>\n",
       "      <td>37.704</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-01 02:00:00</th>\n",
       "      <td>1.38326</td>\n",
       "      <td>1.38326</td>\n",
       "      <td>1.38326</td>\n",
       "      <td>1.38326</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.57152</td>\n",
       "      <td>0</td>\n",
       "      <td>37.704</td>\n",
       "      <td>37.704</td>\n",
       "      <td>37.704</td>\n",
       "      <td>37.704</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-01 03:00:00</th>\n",
       "      <td>1.38326</td>\n",
       "      <td>1.38326</td>\n",
       "      <td>1.38326</td>\n",
       "      <td>1.38326</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.57152</td>\n",
       "      <td>0</td>\n",
       "      <td>37.704</td>\n",
       "      <td>37.704</td>\n",
       "      <td>37.704</td>\n",
       "      <td>37.704</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-01 04:00:00</th>\n",
       "      <td>1.38326</td>\n",
       "      <td>1.38326</td>\n",
       "      <td>1.38326</td>\n",
       "      <td>1.38326</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.57152</td>\n",
       "      <td>0</td>\n",
       "      <td>37.704</td>\n",
       "      <td>37.704</td>\n",
       "      <td>37.704</td>\n",
       "      <td>37.704</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                        Open     High      Low    Close  Volume   return  \\\n",
       "2016-01-01 00:00:00  1.38326  1.38326  1.38326  1.38326     0.0  0.57152   \n",
       "2016-01-01 01:00:00  1.38326  1.38326  1.38326  1.38326     0.0  0.57152   \n",
       "2016-01-01 02:00:00  1.38326  1.38326  1.38326  1.38326     0.0  0.57152   \n",
       "2016-01-01 03:00:00  1.38326  1.38326  1.38326  1.38326     0.0  0.57152   \n",
       "2016-01-01 04:00:00  1.38326  1.38326  1.38326  1.38326     0.0  0.57152   \n",
       "\n",
       "                     label  Open_oil  High_oil  Low_oil  Close_oil  Volume_oil  \n",
       "2016-01-01 00:00:00      0    37.704    37.704   37.704     37.704         0.0  \n",
       "2016-01-01 01:00:00      0    37.704    37.704   37.704     37.704         0.0  \n",
       "2016-01-01 02:00:00      0    37.704    37.704   37.704     37.704         0.0  \n",
       "2016-01-01 03:00:00      0    37.704    37.704   37.704     37.704         0.0  \n",
       "2016-01-01 04:00:00      0    37.704    37.704   37.704     37.704         0.0  "
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.concat([df,df_oil],axis=1, join='inner')\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "tags": []
   },
   "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>Open</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Close</th>\n",
       "      <th>Volume</th>\n",
       "      <th>return</th>\n",
       "      <th>label</th>\n",
       "      <th>Open_oil</th>\n",
       "      <th>High_oil</th>\n",
       "      <th>Low_oil</th>\n",
       "      <th>Close_oil</th>\n",
       "      <th>Volume_oil</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2018-05-31 18:00:00</th>\n",
       "      <td>1.29785</td>\n",
       "      <td>1.29884</td>\n",
       "      <td>1.29538</td>\n",
       "      <td>1.29564</td>\n",
       "      <td>6.044530e+09</td>\n",
       "      <td>0.500482</td>\n",
       "      <td>1</td>\n",
       "      <td>77.156</td>\n",
       "      <td>77.891</td>\n",
       "      <td>76.935</td>\n",
       "      <td>77.835</td>\n",
       "      <td>405591.9945</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-05-31 19:00:00</th>\n",
       "      <td>1.29566</td>\n",
       "      <td>1.29671</td>\n",
       "      <td>1.29541</td>\n",
       "      <td>1.29666</td>\n",
       "      <td>5.330320e+09</td>\n",
       "      <td>0.603664</td>\n",
       "      <td>0</td>\n",
       "      <td>77.835</td>\n",
       "      <td>77.865</td>\n",
       "      <td>77.625</td>\n",
       "      <td>77.655</td>\n",
       "      <td>102294.9964</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-05-31 20:00:00</th>\n",
       "      <td>1.29668</td>\n",
       "      <td>1.29682</td>\n",
       "      <td>1.29502</td>\n",
       "      <td>1.29563</td>\n",
       "      <td>3.116830e+09</td>\n",
       "      <td>0.537769</td>\n",
       "      <td>1</td>\n",
       "      <td>77.655</td>\n",
       "      <td>77.788</td>\n",
       "      <td>77.535</td>\n",
       "      <td>77.725</td>\n",
       "      <td>125650.0036</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-05-31 21:00:00</th>\n",
       "      <td>1.29563</td>\n",
       "      <td>1.29577</td>\n",
       "      <td>1.29498</td>\n",
       "      <td>1.29564</td>\n",
       "      <td>1.456650e+09</td>\n",
       "      <td>0.571842</td>\n",
       "      <td>0</td>\n",
       "      <td>77.725</td>\n",
       "      <td>77.725</td>\n",
       "      <td>77.725</td>\n",
       "      <td>77.725</td>\n",
       "      <td>0.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-05-31 22:00:00</th>\n",
       "      <td>1.29563</td>\n",
       "      <td>1.29563</td>\n",
       "      <td>1.29407</td>\n",
       "      <td>1.29409</td>\n",
       "      <td>1.370330e+09</td>\n",
       "      <td>0.522019</td>\n",
       "      <td>1</td>\n",
       "      <td>77.725</td>\n",
       "      <td>77.725</td>\n",
       "      <td>77.725</td>\n",
       "      <td>77.725</td>\n",
       "      <td>0.0000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                        Open     High      Low    Close        Volume  \\\n",
       "2018-05-31 18:00:00  1.29785  1.29884  1.29538  1.29564  6.044530e+09   \n",
       "2018-05-31 19:00:00  1.29566  1.29671  1.29541  1.29666  5.330320e+09   \n",
       "2018-05-31 20:00:00  1.29668  1.29682  1.29502  1.29563  3.116830e+09   \n",
       "2018-05-31 21:00:00  1.29563  1.29577  1.29498  1.29564  1.456650e+09   \n",
       "2018-05-31 22:00:00  1.29563  1.29563  1.29407  1.29409  1.370330e+09   \n",
       "\n",
       "                       return  label  Open_oil  High_oil  Low_oil  Close_oil  \\\n",
       "2018-05-31 18:00:00  0.500482      1    77.156    77.891   76.935     77.835   \n",
       "2018-05-31 19:00:00  0.603664      0    77.835    77.865   77.625     77.655   \n",
       "2018-05-31 20:00:00  0.537769      1    77.655    77.788   77.535     77.725   \n",
       "2018-05-31 21:00:00  0.571842      0    77.725    77.725   77.725     77.725   \n",
       "2018-05-31 22:00:00  0.522019      1    77.725    77.725   77.725     77.725   \n",
       "\n",
       "                      Volume_oil  \n",
       "2018-05-31 18:00:00  405591.9945  \n",
       "2018-05-31 19:00:00  102294.9964  \n",
       "2018-05-31 20:00:00  125650.0036  \n",
       "2018-05-31 21:00:00       0.0000  \n",
       "2018-05-31 22:00:00       0.0000  "
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "scrolled": true,
    "tags": []
   },
   "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>Open</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Close</th>\n",
       "      <th>Volume</th>\n",
       "      <th>return</th>\n",
       "      <th>label</th>\n",
       "      <th>Open_oil</th>\n",
       "      <th>High_oil</th>\n",
       "      <th>Low_oil</th>\n",
       "      <th>Close_oil</th>\n",
       "      <th>Volume_oil</th>\n",
       "      <th>year</th>\n",
       "      <th>month</th>\n",
       "      <th>day</th>\n",
       "      <th>hour</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2016-01-01 00:00:00</th>\n",
       "      <td>1.38326</td>\n",
       "      <td>1.38326</td>\n",
       "      <td>1.38326</td>\n",
       "      <td>1.38326</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.57152</td>\n",
       "      <td>0</td>\n",
       "      <td>37.704</td>\n",
       "      <td>37.704</td>\n",
       "      <td>37.704</td>\n",
       "      <td>37.704</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2016</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-01 01:00:00</th>\n",
       "      <td>1.38326</td>\n",
       "      <td>1.38326</td>\n",
       "      <td>1.38326</td>\n",
       "      <td>1.38326</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.57152</td>\n",
       "      <td>0</td>\n",
       "      <td>37.704</td>\n",
       "      <td>37.704</td>\n",
       "      <td>37.704</td>\n",
       "      <td>37.704</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2016</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-01 02:00:00</th>\n",
       "      <td>1.38326</td>\n",
       "      <td>1.38326</td>\n",
       "      <td>1.38326</td>\n",
       "      <td>1.38326</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.57152</td>\n",
       "      <td>0</td>\n",
       "      <td>37.704</td>\n",
       "      <td>37.704</td>\n",
       "      <td>37.704</td>\n",
       "      <td>37.704</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2016</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-01 03:00:00</th>\n",
       "      <td>1.38326</td>\n",
       "      <td>1.38326</td>\n",
       "      <td>1.38326</td>\n",
       "      <td>1.38326</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.57152</td>\n",
       "      <td>0</td>\n",
       "      <td>37.704</td>\n",
       "      <td>37.704</td>\n",
       "      <td>37.704</td>\n",
       "      <td>37.704</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2016</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-01 04:00:00</th>\n",
       "      <td>1.38326</td>\n",
       "      <td>1.38326</td>\n",
       "      <td>1.38326</td>\n",
       "      <td>1.38326</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.57152</td>\n",
       "      <td>0</td>\n",
       "      <td>37.704</td>\n",
       "      <td>37.704</td>\n",
       "      <td>37.704</td>\n",
       "      <td>37.704</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2016</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                        Open     High      Low    Close  Volume   return  \\\n",
       "2016-01-01 00:00:00  1.38326  1.38326  1.38326  1.38326     0.0  0.57152   \n",
       "2016-01-01 01:00:00  1.38326  1.38326  1.38326  1.38326     0.0  0.57152   \n",
       "2016-01-01 02:00:00  1.38326  1.38326  1.38326  1.38326     0.0  0.57152   \n",
       "2016-01-01 03:00:00  1.38326  1.38326  1.38326  1.38326     0.0  0.57152   \n",
       "2016-01-01 04:00:00  1.38326  1.38326  1.38326  1.38326     0.0  0.57152   \n",
       "\n",
       "                     label  Open_oil  High_oil  Low_oil  Close_oil  \\\n",
       "2016-01-01 00:00:00      0    37.704    37.704   37.704     37.704   \n",
       "2016-01-01 01:00:00      0    37.704    37.704   37.704     37.704   \n",
       "2016-01-01 02:00:00      0    37.704    37.704   37.704     37.704   \n",
       "2016-01-01 03:00:00      0    37.704    37.704   37.704     37.704   \n",
       "2016-01-01 04:00:00      0    37.704    37.704   37.704     37.704   \n",
       "\n",
       "                     Volume_oil  year  month  day  hour  \n",
       "2016-01-01 00:00:00         0.0  2016      1    1     0  \n",
       "2016-01-01 01:00:00         0.0  2016      1    1     1  \n",
       "2016-01-01 02:00:00         0.0  2016      1    1     2  \n",
       "2016-01-01 03:00:00         0.0  2016      1    1     3  \n",
       "2016-01-01 04:00:00         0.0  2016      1    1     4  "
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Creating datetime features\n",
    "\n",
    "\n",
    "df['year'] = df.index.year\n",
    "df['month'] = df.index.month\n",
    "df['day'] = df.index.day\n",
    "\n",
    "df['hour'] = df.index.hour\n",
    "\n",
    "\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "scrolled": true,
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Open          float64\n",
       "High          float64\n",
       "Low           float64\n",
       "Close         float64\n",
       "Volume        float64\n",
       "return        float64\n",
       "label           int64\n",
       "Open_oil      float64\n",
       "High_oil      float64\n",
       "Low_oil       float64\n",
       "Close_oil     float64\n",
       "Volume_oil    float64\n",
       "year            int64\n",
       "month           int64\n",
       "day             int64\n",
       "hour            int64\n",
       "dtype: object"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.dtypes\n",
    "\n",
    "# we have to change the volume type later"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Escolhendo um DataFrame para o Trade"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\arthu\\AppData\\Local\\Temp\\ipykernel_1136\\1837619197.py:3: FutureWarning: `include_start` and `include_end` are deprecated in favour of `inclusive`.\n",
      "  df = df.between_time(include_start=True,include_end=True, start_time='03:00:00', end_time='12:00:00')\n"
     ]
    },
    {
     "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>Open</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Close</th>\n",
       "      <th>Volume</th>\n",
       "      <th>return</th>\n",
       "      <th>label</th>\n",
       "      <th>Open_oil</th>\n",
       "      <th>High_oil</th>\n",
       "      <th>Low_oil</th>\n",
       "      <th>Close_oil</th>\n",
       "      <th>Volume_oil</th>\n",
       "      <th>year</th>\n",
       "      <th>month</th>\n",
       "      <th>day</th>\n",
       "      <th>hour</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2018-05-30 03:00:00</th>\n",
       "      <td>1.30247</td>\n",
       "      <td>1.30350</td>\n",
       "      <td>1.30238</td>\n",
       "      <td>1.30303</td>\n",
       "      <td>2.623410e+09</td>\n",
       "      <td>0.589521</td>\n",
       "      <td>0</td>\n",
       "      <td>74.916</td>\n",
       "      <td>74.971</td>\n",
       "      <td>74.845</td>\n",
       "      <td>74.911</td>\n",
       "      <td>22219.0004</td>\n",
       "      <td>2018</td>\n",
       "      <td>5</td>\n",
       "      <td>30</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-05-30 04:00:00</th>\n",
       "      <td>1.30306</td>\n",
       "      <td>1.30314</td>\n",
       "      <td>1.30230</td>\n",
       "      <td>1.30260</td>\n",
       "      <td>1.692350e+09</td>\n",
       "      <td>0.556734</td>\n",
       "      <td>1</td>\n",
       "      <td>74.906</td>\n",
       "      <td>75.256</td>\n",
       "      <td>74.875</td>\n",
       "      <td>75.210</td>\n",
       "      <td>23346.9997</td>\n",
       "      <td>2018</td>\n",
       "      <td>5</td>\n",
       "      <td>30</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-05-30 05:00:00</th>\n",
       "      <td>1.30259</td>\n",
       "      <td>1.30398</td>\n",
       "      <td>1.30234</td>\n",
       "      <td>1.30262</td>\n",
       "      <td>3.642960e+09</td>\n",
       "      <td>0.572485</td>\n",
       "      <td>0</td>\n",
       "      <td>75.201</td>\n",
       "      <td>75.231</td>\n",
       "      <td>74.956</td>\n",
       "      <td>75.071</td>\n",
       "      <td>29354.0005</td>\n",
       "      <td>2018</td>\n",
       "      <td>5</td>\n",
       "      <td>30</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-05-30 06:00:00</th>\n",
       "      <td>1.30266</td>\n",
       "      <td>1.30302</td>\n",
       "      <td>1.30143</td>\n",
       "      <td>1.30222</td>\n",
       "      <td>7.352290e+09</td>\n",
       "      <td>0.557377</td>\n",
       "      <td>0</td>\n",
       "      <td>75.076</td>\n",
       "      <td>75.411</td>\n",
       "      <td>75.051</td>\n",
       "      <td>75.331</td>\n",
       "      <td>48622.9993</td>\n",
       "      <td>2018</td>\n",
       "      <td>5</td>\n",
       "      <td>30</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-05-30 07:00:00</th>\n",
       "      <td>1.30218</td>\n",
       "      <td>1.30279</td>\n",
       "      <td>1.30109</td>\n",
       "      <td>1.30148</td>\n",
       "      <td>7.621840e+09</td>\n",
       "      <td>0.549020</td>\n",
       "      <td>0</td>\n",
       "      <td>75.341</td>\n",
       "      <td>75.421</td>\n",
       "      <td>75.165</td>\n",
       "      <td>75.411</td>\n",
       "      <td>223709.0021</td>\n",
       "      <td>2018</td>\n",
       "      <td>5</td>\n",
       "      <td>30</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-05-30 08:00:00</th>\n",
       "      <td>1.30152</td>\n",
       "      <td>1.30161</td>\n",
       "      <td>1.30047</td>\n",
       "      <td>1.30047</td>\n",
       "      <td>7.426750e+09</td>\n",
       "      <td>0.537769</td>\n",
       "      <td>0</td>\n",
       "      <td>75.395</td>\n",
       "      <td>75.495</td>\n",
       "      <td>75.195</td>\n",
       "      <td>75.475</td>\n",
       "      <td>232400.9985</td>\n",
       "      <td>2018</td>\n",
       "      <td>5</td>\n",
       "      <td>30</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-05-30 09:00:00</th>\n",
       "      <td>1.30047</td>\n",
       "      <td>1.30049</td>\n",
       "      <td>1.29931</td>\n",
       "      <td>1.29958</td>\n",
       "      <td>7.103510e+09</td>\n",
       "      <td>0.542912</td>\n",
       "      <td>0</td>\n",
       "      <td>75.475</td>\n",
       "      <td>75.915</td>\n",
       "      <td>75.430</td>\n",
       "      <td>75.820</td>\n",
       "      <td>386076.0033</td>\n",
       "      <td>2018</td>\n",
       "      <td>5</td>\n",
       "      <td>30</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-05-30 10:00:00</th>\n",
       "      <td>1.29959</td>\n",
       "      <td>1.29999</td>\n",
       "      <td>1.29813</td>\n",
       "      <td>1.29861</td>\n",
       "      <td>8.318910e+09</td>\n",
       "      <td>0.540019</td>\n",
       "      <td>0</td>\n",
       "      <td>75.815</td>\n",
       "      <td>75.835</td>\n",
       "      <td>75.505</td>\n",
       "      <td>75.635</td>\n",
       "      <td>319040.0004</td>\n",
       "      <td>2018</td>\n",
       "      <td>5</td>\n",
       "      <td>30</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-05-30 11:00:00</th>\n",
       "      <td>1.29863</td>\n",
       "      <td>1.29885</td>\n",
       "      <td>1.29705</td>\n",
       "      <td>1.29715</td>\n",
       "      <td>6.624660e+09</td>\n",
       "      <td>0.523947</td>\n",
       "      <td>1</td>\n",
       "      <td>75.630</td>\n",
       "      <td>76.040</td>\n",
       "      <td>75.570</td>\n",
       "      <td>76.035</td>\n",
       "      <td>354658.0076</td>\n",
       "      <td>2018</td>\n",
       "      <td>5</td>\n",
       "      <td>30</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-05-30 12:00:00</th>\n",
       "      <td>1.29717</td>\n",
       "      <td>1.29863</td>\n",
       "      <td>1.29527</td>\n",
       "      <td>1.29781</td>\n",
       "      <td>1.067015e+10</td>\n",
       "      <td>0.592093</td>\n",
       "      <td>1</td>\n",
       "      <td>76.035</td>\n",
       "      <td>76.295</td>\n",
       "      <td>75.935</td>\n",
       "      <td>76.015</td>\n",
       "      <td>572080.0161</td>\n",
       "      <td>2018</td>\n",
       "      <td>5</td>\n",
       "      <td>30</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-05-31 03:00:00</th>\n",
       "      <td>1.28779</td>\n",
       "      <td>1.28788</td>\n",
       "      <td>1.28669</td>\n",
       "      <td>1.28719</td>\n",
       "      <td>2.857670e+09</td>\n",
       "      <td>0.552234</td>\n",
       "      <td>0</td>\n",
       "      <td>77.575</td>\n",
       "      <td>77.580</td>\n",
       "      <td>77.375</td>\n",
       "      <td>77.410</td>\n",
       "      <td>61315.9984</td>\n",
       "      <td>2018</td>\n",
       "      <td>5</td>\n",
       "      <td>31</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-05-31 04:00:00</th>\n",
       "      <td>1.28719</td>\n",
       "      <td>1.28725</td>\n",
       "      <td>1.28647</td>\n",
       "      <td>1.28677</td>\n",
       "      <td>1.907510e+09</td>\n",
       "      <td>0.558020</td>\n",
       "      <td>0</td>\n",
       "      <td>77.420</td>\n",
       "      <td>77.490</td>\n",
       "      <td>77.405</td>\n",
       "      <td>77.440</td>\n",
       "      <td>32935.9993</td>\n",
       "      <td>2018</td>\n",
       "      <td>5</td>\n",
       "      <td>31</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-05-31 05:00:00</th>\n",
       "      <td>1.28677</td>\n",
       "      <td>1.28707</td>\n",
       "      <td>1.28598</td>\n",
       "      <td>1.28673</td>\n",
       "      <td>3.887940e+09</td>\n",
       "      <td>0.570235</td>\n",
       "      <td>1</td>\n",
       "      <td>77.440</td>\n",
       "      <td>77.600</td>\n",
       "      <td>77.425</td>\n",
       "      <td>77.565</td>\n",
       "      <td>57546.0009</td>\n",
       "      <td>2018</td>\n",
       "      <td>5</td>\n",
       "      <td>31</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-05-31 06:00:00</th>\n",
       "      <td>1.28678</td>\n",
       "      <td>1.28758</td>\n",
       "      <td>1.28654</td>\n",
       "      <td>1.28734</td>\n",
       "      <td>6.998760e+09</td>\n",
       "      <td>0.589521</td>\n",
       "      <td>0</td>\n",
       "      <td>77.560</td>\n",
       "      <td>77.560</td>\n",
       "      <td>77.210</td>\n",
       "      <td>77.240</td>\n",
       "      <td>122455.9993</td>\n",
       "      <td>2018</td>\n",
       "      <td>5</td>\n",
       "      <td>31</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-05-31 07:00:00</th>\n",
       "      <td>1.28735</td>\n",
       "      <td>1.28747</td>\n",
       "      <td>1.28452</td>\n",
       "      <td>1.28482</td>\n",
       "      <td>7.630360e+09</td>\n",
       "      <td>0.490196</td>\n",
       "      <td>0</td>\n",
       "      <td>77.235</td>\n",
       "      <td>77.515</td>\n",
       "      <td>77.195</td>\n",
       "      <td>77.510</td>\n",
       "      <td>447019.0108</td>\n",
       "      <td>2018</td>\n",
       "      <td>5</td>\n",
       "      <td>31</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-05-31 08:00:00</th>\n",
       "      <td>1.28484</td>\n",
       "      <td>1.28538</td>\n",
       "      <td>1.28356</td>\n",
       "      <td>1.28400</td>\n",
       "      <td>5.385360e+09</td>\n",
       "      <td>0.544519</td>\n",
       "      <td>1</td>\n",
       "      <td>77.505</td>\n",
       "      <td>77.580</td>\n",
       "      <td>77.285</td>\n",
       "      <td>77.315</td>\n",
       "      <td>260250.0021</td>\n",
       "      <td>2018</td>\n",
       "      <td>5</td>\n",
       "      <td>31</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-05-31 09:00:00</th>\n",
       "      <td>1.28400</td>\n",
       "      <td>1.28539</td>\n",
       "      <td>1.28394</td>\n",
       "      <td>1.28470</td>\n",
       "      <td>5.552970e+09</td>\n",
       "      <td>0.594021</td>\n",
       "      <td>0</td>\n",
       "      <td>77.346</td>\n",
       "      <td>77.446</td>\n",
       "      <td>77.185</td>\n",
       "      <td>77.215</td>\n",
       "      <td>167003.0057</td>\n",
       "      <td>2018</td>\n",
       "      <td>5</td>\n",
       "      <td>31</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-05-31 10:00:00</th>\n",
       "      <td>1.28472</td>\n",
       "      <td>1.28583</td>\n",
       "      <td>1.28180</td>\n",
       "      <td>1.28259</td>\n",
       "      <td>5.881380e+09</td>\n",
       "      <td>0.503054</td>\n",
       "      <td>1</td>\n",
       "      <td>77.221</td>\n",
       "      <td>77.496</td>\n",
       "      <td>77.165</td>\n",
       "      <td>77.395</td>\n",
       "      <td>133186.9960</td>\n",
       "      <td>2018</td>\n",
       "      <td>5</td>\n",
       "      <td>31</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-05-31 11:00:00</th>\n",
       "      <td>1.28264</td>\n",
       "      <td>1.28514</td>\n",
       "      <td>1.28251</td>\n",
       "      <td>1.28474</td>\n",
       "      <td>6.608530e+09</td>\n",
       "      <td>0.639023</td>\n",
       "      <td>1</td>\n",
       "      <td>77.416</td>\n",
       "      <td>77.891</td>\n",
       "      <td>77.365</td>\n",
       "      <td>77.826</td>\n",
       "      <td>255546.9871</td>\n",
       "      <td>2018</td>\n",
       "      <td>5</td>\n",
       "      <td>31</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-05-31 12:00:00</th>\n",
       "      <td>1.28474</td>\n",
       "      <td>1.28987</td>\n",
       "      <td>1.28472</td>\n",
       "      <td>1.28985</td>\n",
       "      <td>1.079586e+10</td>\n",
       "      <td>0.735776</td>\n",
       "      <td>1</td>\n",
       "      <td>77.821</td>\n",
       "      <td>78.201</td>\n",
       "      <td>77.615</td>\n",
       "      <td>78.121</td>\n",
       "      <td>309336.0066</td>\n",
       "      <td>2018</td>\n",
       "      <td>5</td>\n",
       "      <td>31</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                        Open     High      Low    Close        Volume  \\\n",
       "2018-05-30 03:00:00  1.30247  1.30350  1.30238  1.30303  2.623410e+09   \n",
       "2018-05-30 04:00:00  1.30306  1.30314  1.30230  1.30260  1.692350e+09   \n",
       "2018-05-30 05:00:00  1.30259  1.30398  1.30234  1.30262  3.642960e+09   \n",
       "2018-05-30 06:00:00  1.30266  1.30302  1.30143  1.30222  7.352290e+09   \n",
       "2018-05-30 07:00:00  1.30218  1.30279  1.30109  1.30148  7.621840e+09   \n",
       "2018-05-30 08:00:00  1.30152  1.30161  1.30047  1.30047  7.426750e+09   \n",
       "2018-05-30 09:00:00  1.30047  1.30049  1.29931  1.29958  7.103510e+09   \n",
       "2018-05-30 10:00:00  1.29959  1.29999  1.29813  1.29861  8.318910e+09   \n",
       "2018-05-30 11:00:00  1.29863  1.29885  1.29705  1.29715  6.624660e+09   \n",
       "2018-05-30 12:00:00  1.29717  1.29863  1.29527  1.29781  1.067015e+10   \n",
       "2018-05-31 03:00:00  1.28779  1.28788  1.28669  1.28719  2.857670e+09   \n",
       "2018-05-31 04:00:00  1.28719  1.28725  1.28647  1.28677  1.907510e+09   \n",
       "2018-05-31 05:00:00  1.28677  1.28707  1.28598  1.28673  3.887940e+09   \n",
       "2018-05-31 06:00:00  1.28678  1.28758  1.28654  1.28734  6.998760e+09   \n",
       "2018-05-31 07:00:00  1.28735  1.28747  1.28452  1.28482  7.630360e+09   \n",
       "2018-05-31 08:00:00  1.28484  1.28538  1.28356  1.28400  5.385360e+09   \n",
       "2018-05-31 09:00:00  1.28400  1.28539  1.28394  1.28470  5.552970e+09   \n",
       "2018-05-31 10:00:00  1.28472  1.28583  1.28180  1.28259  5.881380e+09   \n",
       "2018-05-31 11:00:00  1.28264  1.28514  1.28251  1.28474  6.608530e+09   \n",
       "2018-05-31 12:00:00  1.28474  1.28987  1.28472  1.28985  1.079586e+10   \n",
       "\n",
       "                       return  label  Open_oil  High_oil  Low_oil  Close_oil  \\\n",
       "2018-05-30 03:00:00  0.589521      0    74.916    74.971   74.845     74.911   \n",
       "2018-05-30 04:00:00  0.556734      1    74.906    75.256   74.875     75.210   \n",
       "2018-05-30 05:00:00  0.572485      0    75.201    75.231   74.956     75.071   \n",
       "2018-05-30 06:00:00  0.557377      0    75.076    75.411   75.051     75.331   \n",
       "2018-05-30 07:00:00  0.549020      0    75.341    75.421   75.165     75.411   \n",
       "2018-05-30 08:00:00  0.537769      0    75.395    75.495   75.195     75.475   \n",
       "2018-05-30 09:00:00  0.542912      0    75.475    75.915   75.430     75.820   \n",
       "2018-05-30 10:00:00  0.540019      0    75.815    75.835   75.505     75.635   \n",
       "2018-05-30 11:00:00  0.523947      1    75.630    76.040   75.570     76.035   \n",
       "2018-05-30 12:00:00  0.592093      1    76.035    76.295   75.935     76.015   \n",
       "2018-05-31 03:00:00  0.552234      0    77.575    77.580   77.375     77.410   \n",
       "2018-05-31 04:00:00  0.558020      0    77.420    77.490   77.405     77.440   \n",
       "2018-05-31 05:00:00  0.570235      1    77.440    77.600   77.425     77.565   \n",
       "2018-05-31 06:00:00  0.589521      0    77.560    77.560   77.210     77.240   \n",
       "2018-05-31 07:00:00  0.490196      0    77.235    77.515   77.195     77.510   \n",
       "2018-05-31 08:00:00  0.544519      1    77.505    77.580   77.285     77.315   \n",
       "2018-05-31 09:00:00  0.594021      0    77.346    77.446   77.185     77.215   \n",
       "2018-05-31 10:00:00  0.503054      1    77.221    77.496   77.165     77.395   \n",
       "2018-05-31 11:00:00  0.639023      1    77.416    77.891   77.365     77.826   \n",
       "2018-05-31 12:00:00  0.735776      1    77.821    78.201   77.615     78.121   \n",
       "\n",
       "                      Volume_oil  year  month  day  hour  \n",
       "2018-05-30 03:00:00   22219.0004  2018      5   30     3  \n",
       "2018-05-30 04:00:00   23346.9997  2018      5   30     4  \n",
       "2018-05-30 05:00:00   29354.0005  2018      5   30     5  \n",
       "2018-05-30 06:00:00   48622.9993  2018      5   30     6  \n",
       "2018-05-30 07:00:00  223709.0021  2018      5   30     7  \n",
       "2018-05-30 08:00:00  232400.9985  2018      5   30     8  \n",
       "2018-05-30 09:00:00  386076.0033  2018      5   30     9  \n",
       "2018-05-30 10:00:00  319040.0004  2018      5   30    10  \n",
       "2018-05-30 11:00:00  354658.0076  2018      5   30    11  \n",
       "2018-05-30 12:00:00  572080.0161  2018      5   30    12  \n",
       "2018-05-31 03:00:00   61315.9984  2018      5   31     3  \n",
       "2018-05-31 04:00:00   32935.9993  2018      5   31     4  \n",
       "2018-05-31 05:00:00   57546.0009  2018      5   31     5  \n",
       "2018-05-31 06:00:00  122455.9993  2018      5   31     6  \n",
       "2018-05-31 07:00:00  447019.0108  2018      5   31     7  \n",
       "2018-05-31 08:00:00  260250.0021  2018      5   31     8  \n",
       "2018-05-31 09:00:00  167003.0057  2018      5   31     9  \n",
       "2018-05-31 10:00:00  133186.9960  2018      5   31    10  \n",
       "2018-05-31 11:00:00  255546.9871  2018      5   31    11  \n",
       "2018-05-31 12:00:00  309336.0066  2018      5   31    12  "
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Limiting the dataframe: from 3:00 to 12:00 (10 timestamps) \n",
    "\n",
    "df = df.between_time(include_start=True,include_end=True, start_time='03:00:00', end_time='12:00:00')\n",
    "df.tail(20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "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>Open</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Close</th>\n",
       "      <th>Volume</th>\n",
       "      <th>return</th>\n",
       "      <th>label</th>\n",
       "      <th>Open_oil</th>\n",
       "      <th>High_oil</th>\n",
       "      <th>Low_oil</th>\n",
       "      <th>Close_oil</th>\n",
       "      <th>Volume_oil</th>\n",
       "      <th>year</th>\n",
       "      <th>month</th>\n",
       "      <th>day</th>\n",
       "      <th>hour</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2016-01-01 03:00:00</th>\n",
       "      <td>1.38326</td>\n",
       "      <td>1.38326</td>\n",
       "      <td>1.38326</td>\n",
       "      <td>1.38326</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.57152</td>\n",
       "      <td>0</td>\n",
       "      <td>37.704</td>\n",
       "      <td>37.704</td>\n",
       "      <td>37.704</td>\n",
       "      <td>37.704</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2016</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-01 04:00:00</th>\n",
       "      <td>1.38326</td>\n",
       "      <td>1.38326</td>\n",
       "      <td>1.38326</td>\n",
       "      <td>1.38326</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.57152</td>\n",
       "      <td>0</td>\n",
       "      <td>37.704</td>\n",
       "      <td>37.704</td>\n",
       "      <td>37.704</td>\n",
       "      <td>37.704</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2016</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-01 05:00:00</th>\n",
       "      <td>1.38326</td>\n",
       "      <td>1.38326</td>\n",
       "      <td>1.38326</td>\n",
       "      <td>1.38326</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.57152</td>\n",
       "      <td>0</td>\n",
       "      <td>37.704</td>\n",
       "      <td>37.704</td>\n",
       "      <td>37.704</td>\n",
       "      <td>37.704</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2016</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-01 06:00:00</th>\n",
       "      <td>1.38326</td>\n",
       "      <td>1.38326</td>\n",
       "      <td>1.38326</td>\n",
       "      <td>1.38326</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.57152</td>\n",
       "      <td>0</td>\n",
       "      <td>37.704</td>\n",
       "      <td>37.704</td>\n",
       "      <td>37.704</td>\n",
       "      <td>37.704</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2016</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-01 07:00:00</th>\n",
       "      <td>1.38326</td>\n",
       "      <td>1.38326</td>\n",
       "      <td>1.38326</td>\n",
       "      <td>1.38326</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.57152</td>\n",
       "      <td>0</td>\n",
       "      <td>37.704</td>\n",
       "      <td>37.704</td>\n",
       "      <td>37.704</td>\n",
       "      <td>37.704</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2016</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-01 08:00:00</th>\n",
       "      <td>1.38326</td>\n",
       "      <td>1.38326</td>\n",
       "      <td>1.38326</td>\n",
       "      <td>1.38326</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.57152</td>\n",
       "      <td>0</td>\n",
       "      <td>37.704</td>\n",
       "      <td>37.704</td>\n",
       "      <td>37.704</td>\n",
       "      <td>37.704</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2016</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-01 09:00:00</th>\n",
       "      <td>1.38326</td>\n",
       "      <td>1.38326</td>\n",
       "      <td>1.38326</td>\n",
       "      <td>1.38326</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.57152</td>\n",
       "      <td>0</td>\n",
       "      <td>37.704</td>\n",
       "      <td>37.704</td>\n",
       "      <td>37.704</td>\n",
       "      <td>37.704</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2016</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-01 10:00:00</th>\n",
       "      <td>1.38326</td>\n",
       "      <td>1.38326</td>\n",
       "      <td>1.38326</td>\n",
       "      <td>1.38326</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.57152</td>\n",
       "      <td>0</td>\n",
       "      <td>37.704</td>\n",
       "      <td>37.704</td>\n",
       "      <td>37.704</td>\n",
       "      <td>37.704</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2016</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-01 11:00:00</th>\n",
       "      <td>1.38326</td>\n",
       "      <td>1.38326</td>\n",
       "      <td>1.38326</td>\n",
       "      <td>1.38326</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.57152</td>\n",
       "      <td>0</td>\n",
       "      <td>37.704</td>\n",
       "      <td>37.704</td>\n",
       "      <td>37.704</td>\n",
       "      <td>37.704</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2016</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-01 12:00:00</th>\n",
       "      <td>1.38326</td>\n",
       "      <td>1.38326</td>\n",
       "      <td>1.38326</td>\n",
       "      <td>1.38326</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.57152</td>\n",
       "      <td>0</td>\n",
       "      <td>37.704</td>\n",
       "      <td>37.704</td>\n",
       "      <td>37.704</td>\n",
       "      <td>37.704</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2016</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                        Open     High      Low    Close  Volume   return  \\\n",
       "2016-01-01 03:00:00  1.38326  1.38326  1.38326  1.38326     0.0  0.57152   \n",
       "2016-01-01 04:00:00  1.38326  1.38326  1.38326  1.38326     0.0  0.57152   \n",
       "2016-01-01 05:00:00  1.38326  1.38326  1.38326  1.38326     0.0  0.57152   \n",
       "2016-01-01 06:00:00  1.38326  1.38326  1.38326  1.38326     0.0  0.57152   \n",
       "2016-01-01 07:00:00  1.38326  1.38326  1.38326  1.38326     0.0  0.57152   \n",
       "2016-01-01 08:00:00  1.38326  1.38326  1.38326  1.38326     0.0  0.57152   \n",
       "2016-01-01 09:00:00  1.38326  1.38326  1.38326  1.38326     0.0  0.57152   \n",
       "2016-01-01 10:00:00  1.38326  1.38326  1.38326  1.38326     0.0  0.57152   \n",
       "2016-01-01 11:00:00  1.38326  1.38326  1.38326  1.38326     0.0  0.57152   \n",
       "2016-01-01 12:00:00  1.38326  1.38326  1.38326  1.38326     0.0  0.57152   \n",
       "\n",
       "                     label  Open_oil  High_oil  Low_oil  Close_oil  \\\n",
       "2016-01-01 03:00:00      0    37.704    37.704   37.704     37.704   \n",
       "2016-01-01 04:00:00      0    37.704    37.704   37.704     37.704   \n",
       "2016-01-01 05:00:00      0    37.704    37.704   37.704     37.704   \n",
       "2016-01-01 06:00:00      0    37.704    37.704   37.704     37.704   \n",
       "2016-01-01 07:00:00      0    37.704    37.704   37.704     37.704   \n",
       "2016-01-01 08:00:00      0    37.704    37.704   37.704     37.704   \n",
       "2016-01-01 09:00:00      0    37.704    37.704   37.704     37.704   \n",
       "2016-01-01 10:00:00      0    37.704    37.704   37.704     37.704   \n",
       "2016-01-01 11:00:00      0    37.704    37.704   37.704     37.704   \n",
       "2016-01-01 12:00:00      0    37.704    37.704   37.704     37.704   \n",
       "\n",
       "                     Volume_oil  year  month  day  hour  \n",
       "2016-01-01 03:00:00         0.0  2016      1    1     3  \n",
       "2016-01-01 04:00:00         0.0  2016      1    1     4  \n",
       "2016-01-01 05:00:00         0.0  2016      1    1     5  \n",
       "2016-01-01 06:00:00         0.0  2016      1    1     6  \n",
       "2016-01-01 07:00:00         0.0  2016      1    1     7  \n",
       "2016-01-01 08:00:00         0.0  2016      1    1     8  \n",
       "2016-01-01 09:00:00         0.0  2016      1    1     9  \n",
       "2016-01-01 10:00:00         0.0  2016      1    1    10  \n",
       "2016-01-01 11:00:00         0.0  2016      1    1    11  \n",
       "2016-01-01 12:00:00         0.0  2016      1    1    12  "
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\arthu\\AppData\\Local\\Temp\\ipykernel_1136\\1148158352.py:3: FutureWarning: Value based partial slicing on non-monotonic DatetimeIndexes with non-existing keys is deprecated and will raise a KeyError in a future Version.\n",
      "  df_test = df.loc['2018-05-01':'2018-05-31' ,:]\n"
     ]
    },
    {
     "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>Open</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Close</th>\n",
       "      <th>Volume</th>\n",
       "      <th>return</th>\n",
       "      <th>label</th>\n",
       "      <th>Open_oil</th>\n",
       "      <th>High_oil</th>\n",
       "      <th>Low_oil</th>\n",
       "      <th>Close_oil</th>\n",
       "      <th>Volume_oil</th>\n",
       "      <th>year</th>\n",
       "      <th>month</th>\n",
       "      <th>day</th>\n",
       "      <th>hour</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2018-05-01 03:00:00</th>\n",
       "      <td>1.24915</td>\n",
       "      <td>1.24925</td>\n",
       "      <td>1.24836</td>\n",
       "      <td>1.24875</td>\n",
       "      <td>2.587190e+09</td>\n",
       "      <td>0.558663</td>\n",
       "      <td>0</td>\n",
       "      <td>67.95</td>\n",
       "      <td>67.975</td>\n",
       "      <td>67.905</td>\n",
       "      <td>67.945</td>\n",
       "      <td>16400.0001</td>\n",
       "      <td>2018</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-05-01 04:00:00</th>\n",
       "      <td>1.24875</td>\n",
       "      <td>1.24889</td>\n",
       "      <td>1.24844</td>\n",
       "      <td>1.24845</td>\n",
       "      <td>2.972600e+09</td>\n",
       "      <td>0.561877</td>\n",
       "      <td>1</td>\n",
       "      <td>67.95</td>\n",
       "      <td>68.010</td>\n",
       "      <td>67.945</td>\n",
       "      <td>67.995</td>\n",
       "      <td>11629.9996</td>\n",
       "      <td>2018</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-05-01 05:00:00</th>\n",
       "      <td>1.24844</td>\n",
       "      <td>1.24938</td>\n",
       "      <td>1.24844</td>\n",
       "      <td>1.24922</td>\n",
       "      <td>2.884640e+09</td>\n",
       "      <td>0.596593</td>\n",
       "      <td>1</td>\n",
       "      <td>67.99</td>\n",
       "      <td>68.040</td>\n",
       "      <td>67.965</td>\n",
       "      <td>67.980</td>\n",
       "      <td>27775.9992</td>\n",
       "      <td>2018</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-05-01 06:00:00</th>\n",
       "      <td>1.24923</td>\n",
       "      <td>1.25064</td>\n",
       "      <td>1.24898</td>\n",
       "      <td>1.25047</td>\n",
       "      <td>4.153540e+09</td>\n",
       "      <td>0.611379</td>\n",
       "      <td>1</td>\n",
       "      <td>67.98</td>\n",
       "      <td>68.010</td>\n",
       "      <td>67.875</td>\n",
       "      <td>67.905</td>\n",
       "      <td>29696.9991</td>\n",
       "      <td>2018</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-05-01 07:00:00</th>\n",
       "      <td>1.25047</td>\n",
       "      <td>1.25075</td>\n",
       "      <td>1.24961</td>\n",
       "      <td>1.25074</td>\n",
       "      <td>5.663920e+09</td>\n",
       "      <td>0.580199</td>\n",
       "      <td>0</td>\n",
       "      <td>67.91</td>\n",
       "      <td>67.910</td>\n",
       "      <td>67.750</td>\n",
       "      <td>67.775</td>\n",
       "      <td>75609.9969</td>\n",
       "      <td>2018</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                        Open     High      Low    Close        Volume  \\\n",
       "2018-05-01 03:00:00  1.24915  1.24925  1.24836  1.24875  2.587190e+09   \n",
       "2018-05-01 04:00:00  1.24875  1.24889  1.24844  1.24845  2.972600e+09   \n",
       "2018-05-01 05:00:00  1.24844  1.24938  1.24844  1.24922  2.884640e+09   \n",
       "2018-05-01 06:00:00  1.24923  1.25064  1.24898  1.25047  4.153540e+09   \n",
       "2018-05-01 07:00:00  1.25047  1.25075  1.24961  1.25074  5.663920e+09   \n",
       "\n",
       "                       return  label  Open_oil  High_oil  Low_oil  Close_oil  \\\n",
       "2018-05-01 03:00:00  0.558663      0     67.95    67.975   67.905     67.945   \n",
       "2018-05-01 04:00:00  0.561877      1     67.95    68.010   67.945     67.995   \n",
       "2018-05-01 05:00:00  0.596593      1     67.99    68.040   67.965     67.980   \n",
       "2018-05-01 06:00:00  0.611379      1     67.98    68.010   67.875     67.905   \n",
       "2018-05-01 07:00:00  0.580199      0     67.91    67.910   67.750     67.775   \n",
       "\n",
       "                     Volume_oil  year  month  day  hour  \n",
       "2018-05-01 03:00:00  16400.0001  2018      5    1     3  \n",
       "2018-05-01 04:00:00  11629.9996  2018      5    1     4  \n",
       "2018-05-01 05:00:00  27775.9992  2018      5    1     5  \n",
       "2018-05-01 06:00:00  29696.9991  2018      5    1     6  \n",
       "2018-05-01 07:00:00  75609.9969  2018      5    1     7  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "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>Open</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Close</th>\n",
       "      <th>Volume</th>\n",
       "      <th>return</th>\n",
       "      <th>label</th>\n",
       "      <th>Open_oil</th>\n",
       "      <th>High_oil</th>\n",
       "      <th>Low_oil</th>\n",
       "      <th>Close_oil</th>\n",
       "      <th>Volume_oil</th>\n",
       "      <th>year</th>\n",
       "      <th>month</th>\n",
       "      <th>day</th>\n",
       "      <th>hour</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2018-05-31 08:00:00</th>\n",
       "      <td>1.28484</td>\n",
       "      <td>1.28538</td>\n",
       "      <td>1.28356</td>\n",
       "      <td>1.28400</td>\n",
       "      <td>5.385360e+09</td>\n",
       "      <td>0.544519</td>\n",
       "      <td>1</td>\n",
       "      <td>77.505</td>\n",
       "      <td>77.580</td>\n",
       "      <td>77.285</td>\n",
       "      <td>77.315</td>\n",
       "      <td>260250.0021</td>\n",
       "      <td>2018</td>\n",
       "      <td>5</td>\n",
       "      <td>31</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-05-31 09:00:00</th>\n",
       "      <td>1.28400</td>\n",
       "      <td>1.28539</td>\n",
       "      <td>1.28394</td>\n",
       "      <td>1.28470</td>\n",
       "      <td>5.552970e+09</td>\n",
       "      <td>0.594021</td>\n",
       "      <td>0</td>\n",
       "      <td>77.346</td>\n",
       "      <td>77.446</td>\n",
       "      <td>77.185</td>\n",
       "      <td>77.215</td>\n",
       "      <td>167003.0057</td>\n",
       "      <td>2018</td>\n",
       "      <td>5</td>\n",
       "      <td>31</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-05-31 10:00:00</th>\n",
       "      <td>1.28472</td>\n",
       "      <td>1.28583</td>\n",
       "      <td>1.28180</td>\n",
       "      <td>1.28259</td>\n",
       "      <td>5.881380e+09</td>\n",
       "      <td>0.503054</td>\n",
       "      <td>1</td>\n",
       "      <td>77.221</td>\n",
       "      <td>77.496</td>\n",
       "      <td>77.165</td>\n",
       "      <td>77.395</td>\n",
       "      <td>133186.9960</td>\n",
       "      <td>2018</td>\n",
       "      <td>5</td>\n",
       "      <td>31</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-05-31 11:00:00</th>\n",
       "      <td>1.28264</td>\n",
       "      <td>1.28514</td>\n",
       "      <td>1.28251</td>\n",
       "      <td>1.28474</td>\n",
       "      <td>6.608530e+09</td>\n",
       "      <td>0.639023</td>\n",
       "      <td>1</td>\n",
       "      <td>77.416</td>\n",
       "      <td>77.891</td>\n",
       "      <td>77.365</td>\n",
       "      <td>77.826</td>\n",
       "      <td>255546.9871</td>\n",
       "      <td>2018</td>\n",
       "      <td>5</td>\n",
       "      <td>31</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-05-31 12:00:00</th>\n",
       "      <td>1.28474</td>\n",
       "      <td>1.28987</td>\n",
       "      <td>1.28472</td>\n",
       "      <td>1.28985</td>\n",
       "      <td>1.079586e+10</td>\n",
       "      <td>0.735776</td>\n",
       "      <td>1</td>\n",
       "      <td>77.821</td>\n",
       "      <td>78.201</td>\n",
       "      <td>77.615</td>\n",
       "      <td>78.121</td>\n",
       "      <td>309336.0066</td>\n",
       "      <td>2018</td>\n",
       "      <td>5</td>\n",
       "      <td>31</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                        Open     High      Low    Close        Volume  \\\n",
       "2018-05-31 08:00:00  1.28484  1.28538  1.28356  1.28400  5.385360e+09   \n",
       "2018-05-31 09:00:00  1.28400  1.28539  1.28394  1.28470  5.552970e+09   \n",
       "2018-05-31 10:00:00  1.28472  1.28583  1.28180  1.28259  5.881380e+09   \n",
       "2018-05-31 11:00:00  1.28264  1.28514  1.28251  1.28474  6.608530e+09   \n",
       "2018-05-31 12:00:00  1.28474  1.28987  1.28472  1.28985  1.079586e+10   \n",
       "\n",
       "                       return  label  Open_oil  High_oil  Low_oil  Close_oil  \\\n",
       "2018-05-31 08:00:00  0.544519      1    77.505    77.580   77.285     77.315   \n",
       "2018-05-31 09:00:00  0.594021      0    77.346    77.446   77.185     77.215   \n",
       "2018-05-31 10:00:00  0.503054      1    77.221    77.496   77.165     77.395   \n",
       "2018-05-31 11:00:00  0.639023      1    77.416    77.891   77.365     77.826   \n",
       "2018-05-31 12:00:00  0.735776      1    77.821    78.201   77.615     78.121   \n",
       "\n",
       "                      Volume_oil  year  month  day  hour  \n",
       "2018-05-31 08:00:00  260250.0021  2018      5   31     8  \n",
       "2018-05-31 09:00:00  167003.0057  2018      5   31     9  \n",
       "2018-05-31 10:00:00  133186.9960  2018      5   31    10  \n",
       "2018-05-31 11:00:00  255546.9871  2018      5   31    11  \n",
       "2018-05-31 12:00:00  309336.0066  2018      5   31    12  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "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>Open</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Close</th>\n",
       "      <th>Volume</th>\n",
       "      <th>return</th>\n",
       "      <th>label</th>\n",
       "      <th>Open_oil</th>\n",
       "      <th>High_oil</th>\n",
       "      <th>Low_oil</th>\n",
       "      <th>Close_oil</th>\n",
       "      <th>Volume_oil</th>\n",
       "      <th>year</th>\n",
       "      <th>month</th>\n",
       "      <th>day</th>\n",
       "      <th>hour</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2016-01-01 03:00:00</th>\n",
       "      <td>1.38326</td>\n",
       "      <td>1.38326</td>\n",
       "      <td>1.38326</td>\n",
       "      <td>1.38326</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.57152</td>\n",
       "      <td>0</td>\n",
       "      <td>37.704</td>\n",
       "      <td>37.704</td>\n",
       "      <td>37.704</td>\n",
       "      <td>37.704</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2016</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-01 04:00:00</th>\n",
       "      <td>1.38326</td>\n",
       "      <td>1.38326</td>\n",
       "      <td>1.38326</td>\n",
       "      <td>1.38326</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.57152</td>\n",
       "      <td>0</td>\n",
       "      <td>37.704</td>\n",
       "      <td>37.704</td>\n",
       "      <td>37.704</td>\n",
       "      <td>37.704</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2016</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-01 05:00:00</th>\n",
       "      <td>1.38326</td>\n",
       "      <td>1.38326</td>\n",
       "      <td>1.38326</td>\n",
       "      <td>1.38326</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.57152</td>\n",
       "      <td>0</td>\n",
       "      <td>37.704</td>\n",
       "      <td>37.704</td>\n",
       "      <td>37.704</td>\n",
       "      <td>37.704</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2016</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-01 06:00:00</th>\n",
       "      <td>1.38326</td>\n",
       "      <td>1.38326</td>\n",
       "      <td>1.38326</td>\n",
       "      <td>1.38326</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.57152</td>\n",
       "      <td>0</td>\n",
       "      <td>37.704</td>\n",
       "      <td>37.704</td>\n",
       "      <td>37.704</td>\n",
       "      <td>37.704</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2016</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-01 07:00:00</th>\n",
       "      <td>1.38326</td>\n",
       "      <td>1.38326</td>\n",
       "      <td>1.38326</td>\n",
       "      <td>1.38326</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.57152</td>\n",
       "      <td>0</td>\n",
       "      <td>37.704</td>\n",
       "      <td>37.704</td>\n",
       "      <td>37.704</td>\n",
       "      <td>37.704</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2016</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                        Open     High      Low    Close  Volume   return  \\\n",
       "2016-01-01 03:00:00  1.38326  1.38326  1.38326  1.38326     0.0  0.57152   \n",
       "2016-01-01 04:00:00  1.38326  1.38326  1.38326  1.38326     0.0  0.57152   \n",
       "2016-01-01 05:00:00  1.38326  1.38326  1.38326  1.38326     0.0  0.57152   \n",
       "2016-01-01 06:00:00  1.38326  1.38326  1.38326  1.38326     0.0  0.57152   \n",
       "2016-01-01 07:00:00  1.38326  1.38326  1.38326  1.38326     0.0  0.57152   \n",
       "\n",
       "                     label  Open_oil  High_oil  Low_oil  Close_oil  \\\n",
       "2016-01-01 03:00:00      0    37.704    37.704   37.704     37.704   \n",
       "2016-01-01 04:00:00      0    37.704    37.704   37.704     37.704   \n",
       "2016-01-01 05:00:00      0    37.704    37.704   37.704     37.704   \n",
       "2016-01-01 06:00:00      0    37.704    37.704   37.704     37.704   \n",
       "2016-01-01 07:00:00      0    37.704    37.704   37.704     37.704   \n",
       "\n",
       "                     Volume_oil  year  month  day  hour  \n",
       "2016-01-01 03:00:00         0.0  2016      1    1     3  \n",
       "2016-01-01 04:00:00         0.0  2016      1    1     4  \n",
       "2016-01-01 05:00:00         0.0  2016      1    1     5  \n",
       "2016-01-01 06:00:00         0.0  2016      1    1     6  \n",
       "2016-01-01 07:00:00         0.0  2016      1    1     7  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "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>Open</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Close</th>\n",
       "      <th>Volume</th>\n",
       "      <th>return</th>\n",
       "      <th>label</th>\n",
       "      <th>Open_oil</th>\n",
       "      <th>High_oil</th>\n",
       "      <th>Low_oil</th>\n",
       "      <th>Close_oil</th>\n",
       "      <th>Volume_oil</th>\n",
       "      <th>year</th>\n",
       "      <th>month</th>\n",
       "      <th>day</th>\n",
       "      <th>hour</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2018-04-05 08:00:00</th>\n",
       "      <td>1.28627</td>\n",
       "      <td>1.28750</td>\n",
       "      <td>1.28572</td>\n",
       "      <td>1.28749</td>\n",
       "      <td>3.296140e+09</td>\n",
       "      <td>0.610736</td>\n",
       "      <td>0</td>\n",
       "      <td>73.500</td>\n",
       "      <td>73.515</td>\n",
       "      <td>73.285</td>\n",
       "      <td>73.310</td>\n",
       "      <td>116010.0028</td>\n",
       "      <td>2018</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-04-05 09:00:00</th>\n",
       "      <td>1.28751</td>\n",
       "      <td>1.28798</td>\n",
       "      <td>1.28696</td>\n",
       "      <td>1.28736</td>\n",
       "      <td>3.392910e+09</td>\n",
       "      <td>0.566699</td>\n",
       "      <td>0</td>\n",
       "      <td>73.305</td>\n",
       "      <td>73.530</td>\n",
       "      <td>73.225</td>\n",
       "      <td>73.520</td>\n",
       "      <td>79392.9994</td>\n",
       "      <td>2018</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-04-05 10:00:00</th>\n",
       "      <td>1.28735</td>\n",
       "      <td>1.28779</td>\n",
       "      <td>1.28699</td>\n",
       "      <td>1.28699</td>\n",
       "      <td>2.091400e+09</td>\n",
       "      <td>0.559949</td>\n",
       "      <td>1</td>\n",
       "      <td>73.515</td>\n",
       "      <td>74.010</td>\n",
       "      <td>73.510</td>\n",
       "      <td>73.985</td>\n",
       "      <td>127573.9968</td>\n",
       "      <td>2018</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-04-05 11:00:00</th>\n",
       "      <td>1.28698</td>\n",
       "      <td>1.28758</td>\n",
       "      <td>1.28665</td>\n",
       "      <td>1.28699</td>\n",
       "      <td>4.170160e+09</td>\n",
       "      <td>0.571842</td>\n",
       "      <td>0</td>\n",
       "      <td>73.980</td>\n",
       "      <td>74.045</td>\n",
       "      <td>73.670</td>\n",
       "      <td>73.735</td>\n",
       "      <td>150076.0019</td>\n",
       "      <td>2018</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-04-05 12:00:00</th>\n",
       "      <td>1.28704</td>\n",
       "      <td>1.28818</td>\n",
       "      <td>1.28424</td>\n",
       "      <td>1.28651</td>\n",
       "      <td>1.092947e+10</td>\n",
       "      <td>0.554484</td>\n",
       "      <td>1</td>\n",
       "      <td>73.725</td>\n",
       "      <td>74.040</td>\n",
       "      <td>73.720</td>\n",
       "      <td>73.885</td>\n",
       "      <td>219163.0006</td>\n",
       "      <td>2018</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                        Open     High      Low    Close        Volume  \\\n",
       "2018-04-05 08:00:00  1.28627  1.28750  1.28572  1.28749  3.296140e+09   \n",
       "2018-04-05 09:00:00  1.28751  1.28798  1.28696  1.28736  3.392910e+09   \n",
       "2018-04-05 10:00:00  1.28735  1.28779  1.28699  1.28699  2.091400e+09   \n",
       "2018-04-05 11:00:00  1.28698  1.28758  1.28665  1.28699  4.170160e+09   \n",
       "2018-04-05 12:00:00  1.28704  1.28818  1.28424  1.28651  1.092947e+10   \n",
       "\n",
       "                       return  label  Open_oil  High_oil  Low_oil  Close_oil  \\\n",
       "2018-04-05 08:00:00  0.610736      0    73.500    73.515   73.285     73.310   \n",
       "2018-04-05 09:00:00  0.566699      0    73.305    73.530   73.225     73.520   \n",
       "2018-04-05 10:00:00  0.559949      1    73.515    74.010   73.510     73.985   \n",
       "2018-04-05 11:00:00  0.571842      0    73.980    74.045   73.670     73.735   \n",
       "2018-04-05 12:00:00  0.554484      1    73.725    74.040   73.720     73.885   \n",
       "\n",
       "                      Volume_oil  year  month  day  hour  \n",
       "2018-04-05 08:00:00  116010.0028  2018      4    5     8  \n",
       "2018-04-05 09:00:00   79392.9994  2018      4    5     9  \n",
       "2018-04-05 10:00:00  127573.9968  2018      4    5    10  \n",
       "2018-04-05 11:00:00  150076.0019  2018      4    5    11  \n",
       "2018-04-05 12:00:00  219163.0006  2018      4    5    12  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Make training and test dataset\n",
    "\n",
    "df_test = df.loc['2018-05-01':'2018-05-31' ,:]  \n",
    "display(df_test.head())\n",
    "display(df_test.tail())\n",
    "\n",
    "df_train = df.loc[:'2018-04-30',:] \n",
    "display(df_train.head())\n",
    "display(df_train.tail())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Open', 'High', 'Low', 'Close', 'Volume', 'return', 'label', 'Open_oil',\n",
       "       'High_oil', 'Low_oil', 'Close_oil', 'Volume_oil', 'year', 'month',\n",
       "       'day', 'hour'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_train.columns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Treinando o Modelo"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Separate features and output\n",
    "\n",
    "y_train = df_train['label']\n",
    "X_train = df_train.loc[:,['Open', 'High', 'Low', 'Close', 'Volume', 'return', 'Open_oil', 'High_oil', 'Low_oil', 'Close_oil', 'Volume_oil',\n",
    "       'year', 'month', 'day', 'hour']]\n",
    "\n",
    "y_test = df_test['label']\n",
    "X_test = df_test.loc[:,['Open', 'High', 'Low', 'Close', 'Volume', 'return', 'Open_oil', 'High_oil', 'Low_oil', 'Close_oil', 'Volume_oil',\n",
    "       'year', 'month', 'day', 'hour']]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(180, 15)\n",
      "(180,)\n",
      "% of Class0 : 0.444444 y_test\n",
      "% of Class1 : 0.555556 y_test\n"
     ]
    }
   ],
   "source": [
    "print(X_test.shape)\n",
    "print(y_test.shape)\n",
    "\n",
    "print('%% of Class0 : %f y_test' % (np.count_nonzero(y_test == 0)/float(len(y_test))))\n",
    "print('%% of Class1 : %f y_test' % (np.count_nonzero(y_test == 1)/float(len(y_test))))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(5870, 15)\n",
      "(5870,)\n",
      "% of Class0 : 0.498126 y_train\n",
      "% of Class1 : 0.501874 y_train\n"
     ]
    }
   ],
   "source": [
    "print(X_train.shape)\n",
    "print(y_train.shape)\n",
    "\n",
    "print('%% of Class0 : %f y_train' % (np.count_nonzero(y_train == 0)/float(len(y_train))))\n",
    "print('%% of Class1 : %f y_train' % (np.count_nonzero(y_train == 1)/float(len(y_train))))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "scrolled": true,
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "      Iter       Train Loss   Remaining Time \n",
      "         1           1.3861           19.98s\n",
      "         2           1.3859           19.46s\n",
      "         3           1.3857           19.28s\n",
      "         4           1.3855           19.17s\n",
      "         5           1.3853           19.10s\n",
      "         6           1.3851           19.22s\n",
      "         7           1.3849           19.01s\n",
      "         8           1.3848           18.97s\n",
      "         9           1.3846           18.94s\n",
      "        10           1.3844           18.91s\n",
      "        11           1.3843           18.88s\n",
      "        12           1.3841           18.85s\n",
      "        13           1.3839           18.83s\n",
      "        14           1.3838           18.80s\n",
      "        15           1.3836           18.78s\n",
      "        16           1.3835           18.76s\n",
      "        17           1.3833           18.68s\n",
      "        18           1.3831           18.66s\n",
      "        19           1.3830           18.64s\n",
      "        20           1.3829           18.57s\n",
      "        21           1.3827           18.55s\n",
      "        22           1.3826           18.58s\n",
      "        23           1.3824           18.56s\n",
      "        24           1.3823           18.50s\n",
      "        25           1.3822           18.49s\n",
      "        26           1.3820           18.47s\n",
      "        27           1.3819           18.45s\n",
      "        28           1.3817           18.43s\n",
      "        29           1.3816           18.42s\n",
      "        30           1.3814           18.40s\n",
      "        31           1.3813           18.38s\n",
      "        32           1.3812           18.36s\n",
      "        33           1.3810           18.34s\n",
      "        34           1.3809           18.33s\n",
      "        35           1.3808           18.31s\n",
      "        36           1.3806           18.29s\n",
      "        37           1.3805           18.27s\n",
      "        38           1.3804           18.25s\n",
      "        39           1.3802           18.23s\n",
      "        40           1.3801           18.19s\n",
      "        41           1.3800           18.17s\n",
      "        42           1.3799           18.16s\n",
      "        43           1.3798           18.14s\n",
      "        44           1.3796           18.12s\n",
      "        45           1.3795           18.10s\n",
      "        46           1.3794           18.08s\n",
      "        47           1.3793           18.05s\n",
      "        48           1.3792           18.03s\n",
      "        49           1.3791           18.01s\n",
      "        50           1.3789           17.99s\n",
      "        51           1.3788           17.96s\n",
      "        52           1.3787           17.94s\n",
      "        53           1.3786           17.92s\n",
      "        54           1.3785           17.90s\n",
      "        55           1.3784           17.89s\n",
      "        56           1.3783           17.87s\n",
      "        57           1.3782           17.85s\n",
      "        58           1.3781           17.83s\n",
      "        59           1.3779           17.82s\n",
      "        60           1.3778           17.78s\n",
      "        61           1.3776           17.76s\n",
      "        62           1.3775           17.75s\n",
      "        63           1.3774           17.73s\n",
      "        64           1.3772           17.71s\n",
      "        65           1.3771           17.69s\n",
      "        66           1.3769           17.66s\n",
      "        67           1.3768           17.66s\n",
      "        68           1.3766           17.64s\n",
      "        69           1.3765           17.61s\n",
      "        70           1.3764           17.59s\n",
      "        71           1.3763           17.57s\n",
      "        72           1.3761           17.55s\n",
      "        73           1.3760           17.54s\n",
      "        74           1.3759           17.52s\n",
      "        75           1.3757           17.50s\n",
      "        76           1.3756           17.48s\n",
      "        77           1.3755           17.47s\n",
      "        78           1.3754           17.45s\n",
      "        79           1.3752           17.43s\n",
      "        80           1.3751           17.41s\n",
      "        81           1.3750           17.39s\n",
      "        82           1.3749           17.37s\n",
      "        83           1.3747           17.36s\n",
      "        84           1.3746           17.34s\n",
      "        85           1.3744           17.31s\n",
      "        86           1.3744           17.29s\n",
      "        87           1.3743           17.27s\n",
      "        88           1.3741           17.26s\n",
      "        89           1.3740           17.24s\n",
      "        90           1.3738           17.22s\n",
      "        91           1.3737           17.20s\n",
      "        92           1.3735           17.18s\n",
      "        93           1.3733           17.16s\n",
      "        94           1.3732           17.15s\n",
      "        95           1.3731           17.12s\n",
      "        96           1.3730           17.10s\n",
      "        97           1.3728           17.08s\n",
      "        98           1.3727           17.06s\n",
      "        99           1.3726           17.04s\n",
      "       100           1.3725           17.02s\n",
      "       101           1.3724           17.00s\n",
      "       102           1.3722           16.99s\n",
      "       103           1.3720           16.96s\n",
      "       104           1.3719           16.95s\n",
      "       105           1.3718           16.93s\n",
      "       106           1.3717           16.91s\n",
      "       107           1.3715           16.88s\n",
      "       108           1.3714           16.87s\n",
      "       109           1.3713           16.85s\n",
      "       110           1.3712           16.83s\n",
      "       111           1.3710           16.81s\n",
      "       112           1.3709           16.79s\n",
      "       113           1.3708           16.78s\n",
      "       114           1.3706           16.76s\n",
      "       115           1.3705           16.75s\n",
      "       116           1.3704           16.73s\n",
      "       117           1.3703           16.70s\n",
      "       118           1.3701           16.68s\n",
      "       119           1.3701           16.66s\n",
      "       120           1.3700           16.65s\n",
      "       121           1.3698           16.63s\n",
      "       122           1.3697           16.61s\n",
      "       123           1.3696           16.59s\n",
      "       124           1.3695           16.57s\n",
      "       125           1.3694           16.55s\n",
      "       126           1.3692           16.53s\n",
      "       127           1.3691           16.52s\n",
      "       128           1.3689           16.49s\n",
      "       129           1.3689           16.47s\n",
      "       130           1.3688           16.46s\n",
      "       131           1.3687           16.44s\n",
      "       132           1.3686           16.41s\n",
      "       133           1.3685           16.39s\n",
      "       134           1.3684           16.38s\n",
      "       135           1.3682           16.36s\n",
      "       136           1.3681           16.34s\n",
      "       137           1.3680           16.32s\n",
      "       138           1.3679           16.30s\n",
      "       139           1.3678           16.28s\n",
      "       140           1.3677           16.26s\n",
      "       141           1.3676           16.24s\n",
      "       142           1.3675           16.22s\n",
      "       143           1.3673           16.20s\n",
      "       144           1.3672           16.18s\n",
      "       145           1.3671           16.16s\n",
      "       146           1.3669           16.14s\n",
      "       147           1.3669           16.12s\n",
      "       148           1.3667           16.10s\n",
      "       149           1.3666           16.08s\n",
      "       150           1.3665           16.06s\n",
      "       151           1.3665           16.05s\n",
      "       152           1.3663           16.02s\n",
      "       153           1.3662           16.00s\n",
      "       154           1.3660           15.99s\n",
      "       155           1.3659           15.97s\n",
      "       156           1.3658           15.95s\n",
      "       157           1.3656           15.93s\n",
      "       158           1.3656           15.91s\n",
      "       159           1.3654           15.89s\n",
      "       160           1.3653           15.87s\n",
      "       161           1.3652           15.85s\n",
      "       162           1.3651           15.83s\n",
      "       163           1.3649           15.82s\n",
      "       164           1.3648           15.80s\n",
      "       165           1.3648           15.78s\n",
      "       166           1.3647           15.76s\n",
      "       167           1.3646           15.74s\n",
      "       168           1.3644           15.72s\n",
      "       169           1.3643           15.70s\n",
      "       170           1.3642           15.68s\n",
      "       171           1.3640           15.66s\n",
      "       172           1.3639           15.64s\n",
      "       173           1.3638           15.63s\n",
      "       174           1.3637           15.61s\n",
      "       175           1.3636           15.59s\n",
      "       176           1.3634           15.57s\n",
      "       177           1.3633           15.55s\n",
      "       178           1.3632           15.53s\n",
      "       179           1.3631           15.52s\n",
      "       180           1.3630           15.50s\n",
      "       181           1.3629           15.48s\n",
      "       182           1.3628           15.46s\n",
      "       183           1.3626           15.44s\n",
      "       184           1.3625           15.42s\n",
      "       185           1.3624           15.40s\n",
      "       186           1.3623           15.38s\n",
      "       187           1.3622           15.36s\n",
      "       188           1.3620           15.34s\n",
      "       189           1.3618           15.32s\n",
      "       190           1.3617           15.30s\n",
      "       191           1.3616           15.28s\n",
      "       192           1.3615           15.26s\n",
      "       193           1.3615           15.25s\n",
      "       194           1.3613           15.23s\n",
      "       195           1.3612           15.21s\n",
      "       196           1.3611           15.19s\n",
      "       197           1.3609           15.17s\n",
      "       198           1.3607           15.15s\n",
      "       199           1.3606           15.13s\n",
      "       200           1.3605           15.11s\n",
      "       201           1.3604           15.09s\n",
      "       202           1.3604           15.07s\n",
      "       203           1.3602           15.06s\n",
      "       204           1.3600           15.04s\n",
      "       205           1.3599           15.02s\n",
      "       206           1.3598           15.00s\n",
      "       207           1.3597           14.98s\n",
      "       208           1.3595           14.96s\n",
      "       209           1.3595           14.95s\n",
      "       210           1.3593           14.92s\n",
      "       211           1.3592           14.91s\n",
      "       212           1.3591           14.89s\n",
      "       213           1.3590           14.87s\n",
      "       214           1.3589           14.85s\n",
      "       215           1.3588           14.83s\n",
      "       216           1.3587           14.81s\n",
      "       217           1.3585           14.79s\n",
      "       218           1.3585           14.78s\n",
      "       219           1.3583           14.76s\n",
      "       220           1.3582           14.74s\n",
      "       221           1.3581           14.72s\n",
      "       222           1.3580           14.70s\n",
      "       223           1.3580           14.68s\n",
      "       224           1.3579           14.66s\n",
      "       225           1.3577           14.64s\n",
      "       226           1.3576           14.62s\n",
      "       227           1.3575           14.61s\n",
      "       228           1.3575           14.59s\n",
      "       229           1.3573           14.57s\n",
      "       230           1.3573           14.55s\n",
      "       231           1.3571           14.53s\n",
      "       232           1.3570           14.51s\n",
      "       233           1.3568           14.49s\n",
      "       234           1.3567           14.47s\n",
      "       235           1.3566           14.45s\n",
      "       236           1.3565           14.44s\n",
      "       237           1.3563           14.42s\n",
      "       238           1.3562           14.40s\n",
      "       239           1.3561           14.38s\n",
      "       240           1.3560           14.36s\n",
      "       241           1.3560           14.34s\n",
      "       242           1.3558           14.32s\n",
      "       243           1.3557           14.31s\n",
      "       244           1.3556           14.29s\n",
      "       245           1.3555           14.27s\n",
      "       246           1.3554           14.25s\n",
      "       247           1.3552           14.23s\n",
      "       248           1.3552           14.21s\n",
      "       249           1.3550           14.19s\n",
      "       250           1.3550           14.17s\n",
      "       251           1.3549           14.16s\n",
      "       252           1.3547           14.14s\n",
      "       253           1.3546           14.12s\n",
      "       254           1.3544           14.10s\n",
      "       255           1.3542           14.08s\n",
      "       256           1.3542           14.06s\n",
      "       257           1.3541           14.04s\n",
      "       258           1.3539           14.02s\n",
      "       259           1.3538           14.00s\n",
      "       260           1.3537           13.98s\n",
      "       261           1.3536           13.96s\n",
      "       262           1.3535           13.95s\n",
      "       263           1.3534           13.93s\n",
      "       264           1.3532           13.91s\n",
      "       265           1.3530           13.89s\n",
      "       266           1.3529           13.87s\n",
      "       267           1.3528           13.85s\n",
      "       268           1.3527           13.83s\n",
      "       269           1.3526           13.81s\n",
      "       270           1.3524           13.79s\n",
      "       271           1.3523           13.77s\n",
      "       272           1.3521           13.75s\n",
      "       273           1.3519           13.74s\n",
      "       274           1.3519           13.72s\n",
      "       275           1.3518           13.70s\n",
      "       276           1.3518           13.68s\n",
      "       277           1.3516           13.66s\n",
      "       278           1.3515           13.64s\n",
      "       279           1.3513           13.62s\n",
      "       280           1.3512           13.60s\n",
      "       281           1.3511           13.58s\n",
      "       282           1.3510           13.56s\n",
      "       283           1.3509           13.54s\n",
      "       284           1.3508           13.53s\n",
      "       285           1.3507           13.51s\n",
      "       286           1.3505           13.49s\n",
      "       287           1.3503           13.47s\n",
      "       288           1.3503           13.45s\n",
      "       289           1.3501           13.43s\n",
      "       290           1.3499           13.41s\n",
      "       291           1.3499           13.39s\n",
      "       292           1.3497           13.37s\n",
      "       293           1.3496           13.36s\n",
      "       294           1.3495           13.33s\n",
      "       295           1.3494           13.32s\n",
      "       296           1.3493           13.30s\n",
      "       297           1.3492           13.28s\n",
      "       298           1.3490           13.26s\n",
      "       299           1.3488           13.24s\n",
      "       300           1.3488           13.22s\n",
      "       301           1.3486           13.20s\n",
      "       302           1.3485           13.18s\n",
      "       303           1.3484           13.16s\n",
      "       304           1.3484           13.14s\n",
      "       305           1.3482           13.13s\n",
      "       306           1.3481           13.11s\n",
      "       307           1.3479           13.09s\n",
      "       308           1.3479           13.07s\n",
      "       309           1.3478           13.05s\n",
      "       310           1.3476           13.03s\n",
      "       311           1.3474           13.01s\n",
      "       312           1.3473           12.99s\n",
      "       313           1.3471           12.97s\n",
      "       314           1.3470           12.96s\n",
      "       315           1.3469           12.94s\n",
      "       316           1.3468           12.92s\n",
      "       317           1.3467           12.90s\n",
      "       318           1.3467           12.88s\n",
      "       319           1.3466           12.86s\n",
      "       320           1.3465           12.84s\n",
      "       321           1.3464           12.82s\n",
      "       322           1.3462           12.80s\n",
      "       323           1.3461           12.78s\n",
      "       324           1.3459           12.76s\n",
      "       325           1.3458           12.74s\n",
      "       326           1.3456           12.73s\n",
      "       327           1.3455           12.70s\n",
      "       328           1.3455           12.69s\n",
      "       329           1.3453           12.67s\n",
      "       330           1.3452           12.65s\n",
      "       331           1.3451           12.63s\n",
      "       332           1.3450           12.61s\n",
      "       333           1.3449           12.59s\n",
      "       334           1.3448           12.57s\n",
      "       335           1.3446           12.55s\n",
      "       336           1.3445           12.53s\n",
      "       337           1.3444           12.52s\n",
      "       338           1.3442           12.50s\n",
      "       339           1.3441           12.48s\n",
      "       340           1.3440           12.46s\n",
      "       341           1.3439           12.44s\n",
      "       342           1.3439           12.42s\n",
      "       343           1.3438           12.40s\n",
      "       344           1.3437           12.38s\n",
      "       345           1.3436           12.37s\n",
      "       346           1.3435           12.35s\n",
      "       347           1.3434           12.33s\n",
      "       348           1.3433           12.31s\n",
      "       349           1.3432           12.29s\n",
      "       350           1.3430           12.27s\n",
      "       351           1.3428           12.25s\n",
      "       352           1.3427           12.23s\n",
      "       353           1.3426           12.21s\n",
      "       354           1.3425           12.19s\n",
      "       355           1.3424           12.18s\n",
      "       356           1.3423           12.16s\n",
      "       357           1.3422           12.14s\n",
      "       358           1.3420           12.12s\n",
      "       359           1.3419           12.10s\n",
      "       360           1.3418           12.08s\n",
      "       361           1.3417           12.06s\n",
      "       362           1.3415           12.04s\n",
      "       363           1.3414           12.02s\n",
      "       364           1.3412           12.00s\n",
      "       365           1.3410           11.98s\n",
      "       366           1.3409           11.96s\n",
      "       367           1.3408           11.95s\n",
      "       368           1.3407           11.93s\n",
      "       369           1.3406           11.91s\n",
      "       370           1.3405           11.89s\n",
      "       371           1.3403           11.87s\n",
      "       372           1.3403           11.85s\n",
      "       373           1.3401           11.83s\n",
      "       374           1.3400           11.81s\n",
      "       375           1.3398           11.79s\n",
      "       376           1.3398           11.77s\n",
      "       377           1.3397           11.76s\n",
      "       378           1.3395           11.74s\n",
      "       379           1.3394           11.72s\n",
      "       380           1.3393           11.70s\n",
      "       381           1.3392           11.69s\n",
      "       382           1.3390           11.67s\n",
      "       383           1.3389           11.65s\n",
      "       384           1.3388           11.63s\n",
      "       385           1.3387           11.61s\n",
      "       386           1.3386           11.59s\n",
      "       387           1.3385           11.57s\n",
      "       388           1.3384           11.55s\n",
      "       389           1.3382           11.53s\n",
      "       390           1.3381           11.51s\n",
      "       391           1.3379           11.50s\n",
      "       392           1.3379           11.48s\n",
      "       393           1.3378           11.46s\n",
      "       394           1.3377           11.44s\n",
      "       395           1.3375           11.42s\n",
      "       396           1.3374           11.40s\n",
      "       397           1.3373           11.38s\n",
      "       398           1.3372           11.36s\n",
      "       399           1.3371           11.34s\n",
      "       400           1.3370           11.32s\n",
      "       401           1.3368           11.31s\n",
      "       402           1.3367           11.29s\n",
      "       403           1.3367           11.27s\n",
      "       404           1.3365           11.25s\n",
      "       405           1.3364           11.23s\n",
      "       406           1.3363           11.21s\n",
      "       407           1.3362           11.19s\n",
      "       408           1.3360           11.17s\n",
      "       409           1.3359           11.15s\n",
      "       410           1.3357           11.14s\n",
      "       411           1.3356           11.12s\n",
      "       412           1.3355           11.10s\n",
      "       413           1.3354           11.08s\n",
      "       414           1.3353           11.06s\n",
      "       415           1.3351           11.04s\n",
      "       416           1.3350           11.02s\n",
      "       417           1.3349           11.00s\n",
      "       418           1.3348           10.98s\n",
      "       419           1.3347           10.96s\n",
      "       420           1.3346           10.94s\n",
      "       421           1.3345           10.93s\n",
      "       422           1.3344           10.91s\n",
      "       423           1.3342           10.89s\n",
      "       424           1.3341           10.87s\n",
      "       425           1.3340           10.85s\n",
      "       426           1.3339           10.83s\n",
      "       427           1.3339           10.81s\n",
      "       428           1.3337           10.79s\n",
      "       429           1.3336           10.77s\n",
      "       430           1.3335           10.76s\n",
      "       431           1.3334           10.74s\n",
      "       432           1.3333           10.72s\n",
      "       433           1.3332           10.70s\n",
      "       434           1.3330           10.68s\n",
      "       435           1.3329           10.66s\n",
      "       436           1.3328           10.64s\n",
      "       437           1.3327           10.62s\n",
      "       438           1.3327           10.60s\n",
      "       439           1.3326           10.58s\n",
      "       440           1.3324           10.56s\n",
      "       441           1.3323           10.55s\n",
      "       442           1.3323           10.53s\n",
      "       443           1.3321           10.51s\n",
      "       444           1.3319           10.49s\n",
      "       445           1.3318           10.47s\n",
      "       446           1.3317           10.45s\n",
      "       447           1.3316           10.43s\n",
      "       448           1.3315           10.41s\n",
      "       449           1.3314           10.40s\n",
      "       450           1.3313           10.38s\n",
      "       451           1.3312           10.36s\n",
      "       452           1.3310           10.34s\n",
      "       453           1.3309           10.32s\n",
      "       454           1.3309           10.30s\n",
      "       455           1.3308           10.28s\n",
      "       456           1.3307           10.26s\n",
      "       457           1.3306           10.24s\n",
      "       458           1.3305           10.22s\n",
      "       459           1.3304           10.20s\n",
      "       460           1.3303           10.19s\n",
      "       461           1.3302           10.17s\n",
      "       462           1.3300           10.15s\n",
      "       463           1.3300           10.13s\n",
      "       464           1.3298           10.11s\n",
      "       465           1.3297           10.09s\n",
      "       466           1.3295           10.07s\n",
      "       467           1.3294           10.05s\n",
      "       468           1.3294           10.03s\n",
      "       469           1.3293           10.01s\n",
      "       470           1.3292           10.00s\n",
      "       471           1.3290            9.98s\n",
      "       472           1.3289            9.96s\n",
      "       473           1.3288            9.94s\n",
      "       474           1.3287            9.92s\n",
      "       475           1.3286            9.90s\n",
      "       476           1.3286            9.88s\n",
      "       477           1.3284            9.86s\n",
      "       478           1.3283            9.85s\n",
      "       479           1.3281            9.83s\n",
      "       480           1.3280            9.81s\n",
      "       481           1.3278            9.79s\n",
      "       482           1.3277            9.77s\n",
      "       483           1.3277            9.75s\n",
      "       484           1.3276            9.73s\n",
      "       485           1.3274            9.71s\n",
      "       486           1.3273            9.70s\n",
      "       487           1.3272            9.68s\n",
      "       488           1.3271            9.66s\n",
      "       489           1.3270            9.64s\n",
      "       490           1.3270            9.62s\n",
      "       491           1.3268            9.60s\n",
      "       492           1.3267            9.58s\n",
      "       493           1.3265            9.56s\n",
      "       494           1.3264            9.54s\n",
      "       495           1.3263            9.52s\n",
      "       496           1.3262            9.51s\n",
      "       497           1.3262            9.49s\n",
      "       498           1.3261            9.47s\n",
      "       499           1.3260            9.45s\n",
      "       500           1.3258            9.43s\n",
      "       501           1.3257            9.41s\n",
      "       502           1.3256            9.39s\n",
      "       503           1.3255            9.37s\n",
      "       504           1.3254            9.35s\n",
      "       505           1.3253            9.34s\n",
      "       506           1.3253            9.32s\n",
      "       507           1.3251            9.30s\n",
      "       508           1.3251            9.28s\n",
      "       509           1.3250            9.26s\n",
      "       510           1.3248            9.24s\n",
      "       511           1.3246            9.22s\n",
      "       512           1.3245            9.20s\n",
      "       513           1.3245            9.18s\n",
      "       514           1.3244            9.16s\n",
      "       515           1.3243            9.15s\n",
      "       516           1.3241            9.13s\n",
      "       517           1.3240            9.11s\n",
      "       518           1.3239            9.09s\n",
      "       519           1.3238            9.07s\n",
      "       520           1.3237            9.05s\n",
      "       521           1.3236            9.03s\n",
      "       522           1.3235            9.01s\n",
      "       523           1.3235            8.99s\n",
      "       524           1.3234            8.97s\n",
      "       525           1.3232            8.96s\n",
      "       526           1.3231            8.94s\n",
      "       527           1.3230            8.92s\n",
      "       528           1.3229            8.90s\n",
      "       529           1.3228            8.88s\n",
      "       530           1.3226            8.86s\n",
      "       531           1.3225            8.84s\n",
      "       532           1.3225            8.82s\n",
      "       533           1.3224            8.80s\n",
      "       534           1.3223            8.79s\n",
      "       535           1.3222            8.77s\n",
      "       536           1.3221            8.75s\n",
      "       537           1.3220            8.73s\n",
      "       538           1.3219            8.71s\n",
      "       539           1.3218            8.69s\n",
      "       540           1.3217            8.67s\n",
      "       541           1.3215            8.65s\n",
      "       542           1.3215            8.64s\n",
      "       543           1.3213            8.62s\n",
      "       544           1.3212            8.60s\n",
      "       545           1.3212            8.58s\n",
      "       546           1.3211            8.56s\n",
      "       547           1.3210            8.54s\n",
      "       548           1.3209            8.52s\n",
      "       549           1.3209            8.50s\n",
      "       550           1.3207            8.48s\n",
      "       551           1.3206            8.46s\n",
      "       552           1.3205            8.45s\n",
      "       553           1.3205            8.43s\n",
      "       554           1.3204            8.41s\n",
      "       555           1.3203            8.39s\n",
      "       556           1.3202            8.37s\n",
      "       557           1.3201            8.35s\n",
      "       558           1.3201            8.33s\n",
      "       559           1.3199            8.31s\n",
      "       560           1.3198            8.29s\n",
      "       561           1.3196            8.28s\n",
      "       562           1.3195            8.26s\n",
      "       563           1.3194            8.24s\n",
      "       564           1.3193            8.22s\n",
      "       565           1.3192            8.20s\n",
      "       566           1.3191            8.18s\n",
      "       567           1.3190            8.16s\n",
      "       568           1.3189            8.14s\n",
      "       569           1.3188            8.12s\n",
      "       570           1.3187            8.11s\n",
      "       571           1.3186            8.09s\n",
      "       572           1.3185            8.07s\n",
      "       573           1.3185            8.05s\n",
      "       574           1.3184            8.03s\n",
      "       575           1.3183            8.01s\n",
      "       576           1.3182            7.99s\n",
      "       577           1.3181            7.97s\n",
      "       578           1.3180            7.95s\n",
      "       579           1.3178            7.94s\n",
      "       580           1.3178            7.92s\n",
      "       581           1.3176            7.90s\n",
      "       582           1.3175            7.88s\n",
      "       583           1.3175            7.86s\n",
      "       584           1.3174            7.84s\n",
      "       585           1.3173            7.82s\n",
      "       586           1.3172            7.80s\n",
      "       587           1.3171            7.79s\n",
      "       588           1.3170            7.77s\n",
      "       589           1.3169            7.75s\n",
      "       590           1.3168            7.73s\n",
      "       591           1.3167            7.71s\n",
      "       592           1.3165            7.69s\n",
      "       593           1.3165            7.67s\n",
      "       594           1.3163            7.65s\n",
      "       595           1.3163            7.63s\n",
      "       596           1.3162            7.61s\n",
      "       597           1.3161            7.60s\n",
      "       598           1.3160            7.58s\n",
      "       599           1.3158            7.56s\n",
      "       600           1.3157            7.54s\n",
      "       601           1.3157            7.52s\n",
      "       602           1.3156            7.50s\n",
      "       603           1.3155            7.48s\n",
      "       604           1.3154            7.46s\n",
      "       605           1.3153            7.44s\n",
      "       606           1.3152            7.43s\n",
      "       607           1.3151            7.41s\n",
      "       608           1.3149            7.39s\n",
      "       609           1.3149            7.37s\n",
      "       610           1.3148            7.35s\n",
      "       611           1.3147            7.33s\n",
      "       612           1.3147            7.31s\n",
      "       613           1.3145            7.29s\n",
      "       614           1.3144            7.27s\n",
      "       615           1.3143            7.26s\n",
      "       616           1.3143            7.24s\n",
      "       617           1.3142            7.22s\n",
      "       618           1.3140            7.20s\n",
      "       619           1.3139            7.18s\n",
      "       620           1.3138            7.16s\n",
      "       621           1.3137            7.14s\n",
      "       622           1.3136            7.12s\n",
      "       623           1.3135            7.10s\n",
      "       624           1.3134            7.09s\n",
      "       625           1.3132            7.07s\n",
      "       626           1.3132            7.05s\n",
      "       627           1.3131            7.03s\n",
      "       628           1.3130            7.01s\n",
      "       629           1.3129            6.99s\n",
      "       630           1.3128            6.97s\n",
      "       631           1.3127            6.95s\n",
      "       632           1.3127            6.93s\n",
      "       633           1.3126            6.92s\n",
      "       634           1.3125            6.90s\n",
      "       635           1.3123            6.88s\n",
      "       636           1.3122            6.86s\n",
      "       637           1.3121            6.84s\n",
      "       638           1.3120            6.82s\n",
      "       639           1.3119            6.80s\n",
      "       640           1.3118            6.78s\n",
      "       641           1.3117            6.77s\n",
      "       642           1.3115            6.75s\n",
      "       643           1.3114            6.73s\n",
      "       644           1.3114            6.71s\n",
      "       645           1.3113            6.69s\n",
      "       646           1.3112            6.67s\n",
      "       647           1.3111            6.65s\n",
      "       648           1.3110            6.64s\n",
      "       649           1.3109            6.62s\n",
      "       650           1.3109            6.60s\n",
      "       651           1.3107            6.58s\n",
      "       652           1.3107            6.56s\n",
      "       653           1.3106            6.54s\n",
      "       654           1.3105            6.52s\n",
      "       655           1.3104            6.50s\n",
      "       656           1.3102            6.48s\n",
      "       657           1.3101            6.47s\n",
      "       658           1.3100            6.45s\n",
      "       659           1.3099            6.43s\n",
      "       660           1.3098            6.41s\n",
      "       661           1.3098            6.39s\n",
      "       662           1.3096            6.37s\n",
      "       663           1.3096            6.35s\n",
      "       664           1.3095            6.33s\n",
      "       665           1.3094            6.31s\n",
      "       666           1.3093            6.30s\n",
      "       667           1.3093            6.28s\n",
      "       668           1.3092            6.26s\n",
      "       669           1.3091            6.24s\n",
      "       670           1.3090            6.22s\n",
      "       671           1.3088            6.20s\n",
      "       672           1.3087            6.18s\n",
      "       673           1.3086            6.16s\n",
      "       674           1.3085            6.14s\n",
      "       675           1.3084            6.13s\n",
      "       676           1.3083            6.11s\n",
      "       677           1.3082            6.09s\n",
      "       678           1.3082            6.07s\n",
      "       679           1.3080            6.05s\n",
      "       680           1.3079            6.03s\n",
      "       681           1.3078            6.01s\n",
      "       682           1.3077            5.99s\n",
      "       683           1.3076            5.97s\n",
      "       684           1.3075            5.96s\n",
      "       685           1.3074            5.94s\n",
      "       686           1.3073            5.92s\n",
      "       687           1.3072            5.90s\n",
      "       688           1.3072            5.88s\n",
      "       689           1.3071            5.86s\n",
      "       690           1.3070            5.84s\n",
      "       691           1.3069            5.82s\n",
      "       692           1.3068            5.80s\n",
      "       693           1.3066            5.79s\n",
      "       694           1.3066            5.77s\n",
      "       695           1.3065            5.75s\n",
      "       696           1.3064            5.73s\n",
      "       697           1.3063            5.71s\n",
      "       698           1.3063            5.69s\n",
      "       699           1.3062            5.67s\n",
      "       700           1.3061            5.65s\n",
      "       701           1.3060            5.63s\n",
      "       702           1.3059            5.62s\n",
      "       703           1.3058            5.60s\n",
      "       704           1.3057            5.58s\n",
      "       705           1.3056            5.56s\n",
      "       706           1.3055            5.54s\n",
      "       707           1.3054            5.52s\n",
      "       708           1.3053            5.50s\n",
      "       709           1.3052            5.48s\n",
      "       710           1.3051            5.47s\n",
      "       711           1.3050            5.45s\n",
      "       712           1.3050            5.43s\n",
      "       713           1.3048            5.41s\n",
      "       714           1.3047            5.39s\n",
      "       715           1.3045            5.37s\n",
      "       716           1.3044            5.35s\n",
      "       717           1.3043            5.33s\n",
      "       718           1.3042            5.31s\n",
      "       719           1.3042            5.30s\n",
      "       720           1.3041            5.28s\n",
      "       721           1.3040            5.26s\n",
      "       722           1.3039            5.24s\n",
      "       723           1.3038            5.22s\n",
      "       724           1.3037            5.20s\n",
      "       725           1.3036            5.18s\n",
      "       726           1.3035            5.16s\n",
      "       727           1.3034            5.14s\n",
      "       728           1.3033            5.13s\n",
      "       729           1.3033            5.11s\n",
      "       730           1.3032            5.09s\n",
      "       731           1.3031            5.07s\n",
      "       732           1.3029            5.05s\n",
      "       733           1.3029            5.03s\n",
      "       734           1.3028            5.01s\n",
      "       735           1.3027            4.99s\n",
      "       736           1.3026            4.97s\n",
      "       737           1.3025            4.96s\n",
      "       738           1.3024            4.94s\n",
      "       739           1.3022            4.92s\n",
      "       740           1.3021            4.90s\n",
      "       741           1.3021            4.88s\n",
      "       742           1.3019            4.86s\n",
      "       743           1.3019            4.84s\n",
      "       744           1.3018            4.82s\n",
      "       745           1.3017            4.80s\n",
      "       746           1.3016            4.79s\n",
      "       747           1.3015            4.77s\n",
      "       748           1.3014            4.75s\n",
      "       749           1.3013            4.73s\n",
      "       750           1.3012            4.71s\n",
      "       751           1.3011            4.69s\n",
      "       752           1.3010            4.67s\n",
      "       753           1.3009            4.65s\n",
      "       754           1.3008            4.64s\n",
      "       755           1.3007            4.62s\n",
      "       756           1.3006            4.60s\n",
      "       757           1.3004            4.58s\n",
      "       758           1.3004            4.56s\n",
      "       759           1.3003            4.54s\n",
      "       760           1.3002            4.52s\n",
      "       761           1.3001            4.50s\n",
      "       762           1.3001            4.48s\n",
      "       763           1.3000            4.47s\n",
      "       764           1.2999            4.45s\n",
      "       765           1.2998            4.43s\n",
      "       766           1.2997            4.41s\n",
      "       767           1.2997            4.39s\n",
      "       768           1.2996            4.37s\n",
      "       769           1.2994            4.35s\n",
      "       770           1.2993            4.33s\n",
      "       771           1.2992            4.31s\n",
      "       772           1.2991            4.30s\n",
      "       773           1.2990            4.28s\n",
      "       774           1.2989            4.26s\n",
      "       775           1.2988            4.24s\n",
      "       776           1.2987            4.22s\n",
      "       777           1.2986            4.20s\n",
      "       778           1.2985            4.18s\n",
      "       779           1.2984            4.16s\n",
      "       780           1.2983            4.14s\n",
      "       781           1.2982            4.13s\n",
      "       782           1.2981            4.11s\n",
      "       783           1.2980            4.09s\n",
      "       784           1.2979            4.07s\n",
      "       785           1.2979            4.05s\n",
      "       786           1.2978            4.03s\n",
      "       787           1.2977            4.01s\n",
      "       788           1.2976            3.99s\n",
      "       789           1.2975            3.98s\n",
      "       790           1.2974            3.96s\n",
      "       791           1.2973            3.94s\n",
      "       792           1.2972            3.92s\n",
      "       793           1.2971            3.90s\n",
      "       794           1.2970            3.88s\n",
      "       795           1.2970            3.86s\n",
      "       796           1.2969            3.84s\n",
      "       797           1.2968            3.82s\n",
      "       798           1.2967            3.81s\n",
      "       799           1.2966            3.79s\n",
      "       800           1.2965            3.77s\n",
      "       801           1.2964            3.75s\n",
      "       802           1.2963            3.73s\n",
      "       803           1.2962            3.71s\n",
      "       804           1.2961            3.69s\n",
      "       805           1.2960            3.67s\n",
      "       806           1.2960            3.65s\n",
      "       807           1.2959            3.64s\n",
      "       808           1.2958            3.62s\n",
      "       809           1.2957            3.60s\n",
      "       810           1.2956            3.58s\n",
      "       811           1.2955            3.56s\n",
      "       812           1.2954            3.54s\n",
      "       813           1.2953            3.52s\n",
      "       814           1.2952            3.50s\n",
      "       815           1.2951            3.49s\n",
      "       816           1.2950            3.47s\n",
      "       817           1.2949            3.45s\n",
      "       818           1.2947            3.43s\n",
      "       819           1.2946            3.41s\n",
      "       820           1.2945            3.39s\n",
      "       821           1.2944            3.37s\n",
      "       822           1.2944            3.35s\n",
      "       823           1.2943            3.33s\n",
      "       824           1.2943            3.32s\n",
      "       825           1.2941            3.30s\n",
      "       826           1.2941            3.28s\n",
      "       827           1.2940            3.26s\n",
      "       828           1.2940            3.24s\n",
      "       829           1.2939            3.22s\n",
      "       830           1.2938            3.20s\n",
      "       831           1.2937            3.18s\n",
      "       832           1.2936            3.17s\n",
      "       833           1.2934            3.15s\n",
      "       834           1.2933            3.13s\n",
      "       835           1.2932            3.11s\n",
      "       836           1.2931            3.09s\n",
      "       837           1.2930            3.07s\n",
      "       838           1.2929            3.05s\n",
      "       839           1.2929            3.03s\n",
      "       840           1.2927            3.01s\n",
      "       841           1.2927            3.00s\n",
      "       842           1.2926            2.98s\n",
      "       843           1.2925            2.96s\n",
      "       844           1.2924            2.94s\n",
      "       845           1.2922            2.92s\n",
      "       846           1.2922            2.90s\n",
      "       847           1.2921            2.88s\n",
      "       848           1.2921            2.86s\n",
      "       849           1.2920            2.84s\n",
      "       850           1.2919            2.83s\n",
      "       851           1.2918            2.81s\n",
      "       852           1.2916            2.79s\n",
      "       853           1.2915            2.77s\n",
      "       854           1.2914            2.75s\n",
      "       855           1.2913            2.73s\n",
      "       856           1.2912            2.71s\n",
      "       857           1.2911            2.69s\n",
      "       858           1.2910            2.67s\n",
      "       859           1.2910            2.66s\n",
      "       860           1.2908            2.64s\n",
      "       861           1.2908            2.62s\n",
      "       862           1.2907            2.60s\n",
      "       863           1.2906            2.58s\n",
      "       864           1.2906            2.56s\n",
      "       865           1.2904            2.54s\n",
      "       866           1.2903            2.52s\n",
      "       867           1.2902            2.51s\n",
      "       868           1.2902            2.49s\n",
      "       869           1.2900            2.47s\n",
      "       870           1.2899            2.45s\n",
      "       871           1.2898            2.43s\n",
      "       872           1.2897            2.41s\n",
      "       873           1.2896            2.39s\n",
      "       874           1.2895            2.37s\n",
      "       875           1.2894            2.35s\n",
      "       876           1.2893            2.34s\n",
      "       877           1.2892            2.32s\n",
      "       878           1.2892            2.30s\n",
      "       879           1.2891            2.28s\n",
      "       880           1.2891            2.26s\n",
      "       881           1.2890            2.24s\n",
      "       882           1.2889            2.22s\n",
      "       883           1.2888            2.20s\n",
      "       884           1.2887            2.19s\n",
      "       885           1.2886            2.17s\n",
      "       886           1.2884            2.15s\n",
      "       887           1.2883            2.13s\n",
      "       888           1.2882            2.11s\n",
      "       889           1.2882            2.09s\n",
      "       890           1.2880            2.07s\n",
      "       891           1.2879            2.05s\n",
      "       892           1.2878            2.03s\n",
      "       893           1.2878            2.02s\n",
      "       894           1.2877            2.00s\n",
      "       895           1.2876            1.98s\n",
      "       896           1.2875            1.96s\n",
      "       897           1.2874            1.94s\n",
      "       898           1.2873            1.92s\n",
      "       899           1.2872            1.90s\n",
      "       900           1.2871            1.88s\n",
      "       901           1.2870            1.86s\n",
      "       902           1.2869            1.85s\n",
      "       903           1.2868            1.83s\n",
      "       904           1.2867            1.81s\n",
      "       905           1.2867            1.79s\n",
      "       906           1.2866            1.77s\n",
      "       907           1.2864            1.75s\n",
      "       908           1.2863            1.73s\n",
      "       909           1.2862            1.71s\n",
      "       910           1.2861            1.70s\n",
      "       911           1.2860            1.68s\n",
      "       912           1.2859            1.66s\n",
      "       913           1.2858            1.64s\n",
      "       914           1.2858            1.62s\n",
      "       915           1.2857            1.60s\n",
      "       916           1.2856            1.58s\n",
      "       917           1.2855            1.56s\n",
      "       918           1.2854            1.54s\n",
      "       919           1.2853            1.53s\n",
      "       920           1.2852            1.51s\n",
      "       921           1.2851            1.49s\n",
      "       922           1.2850            1.47s\n",
      "       923           1.2850            1.45s\n",
      "       924           1.2848            1.43s\n",
      "       925           1.2847            1.41s\n",
      "       926           1.2847            1.39s\n",
      "       927           1.2846            1.38s\n",
      "       928           1.2845            1.36s\n",
      "       929           1.2844            1.34s\n",
      "       930           1.2843            1.32s\n",
      "       931           1.2843            1.30s\n",
      "       932           1.2841            1.28s\n",
      "       933           1.2840            1.26s\n",
      "       934           1.2839            1.24s\n",
      "       935           1.2839            1.22s\n",
      "       936           1.2838            1.21s\n",
      "       937           1.2837            1.19s\n",
      "       938           1.2836            1.17s\n",
      "       939           1.2835            1.15s\n",
      "       940           1.2834            1.13s\n",
      "       941           1.2833            1.11s\n",
      "       942           1.2832            1.09s\n",
      "       943           1.2831            1.07s\n",
      "       944           1.2830            1.05s\n",
      "       945           1.2829            1.04s\n",
      "       946           1.2828            1.02s\n",
      "       947           1.2828            1.00s\n",
      "       948           1.2827            0.98s\n",
      "       949           1.2825            0.96s\n",
      "       950           1.2824            0.94s\n",
      "       951           1.2824            0.92s\n",
      "       952           1.2822            0.90s\n",
      "       953           1.2822            0.89s\n",
      "       954           1.2820            0.87s\n",
      "       955           1.2819            0.85s\n",
      "       956           1.2819            0.83s\n",
      "       957           1.2818            0.81s\n",
      "       958           1.2818            0.79s\n",
      "       959           1.2816            0.77s\n",
      "       960           1.2815            0.75s\n",
      "       961           1.2814            0.73s\n",
      "       962           1.2813            0.72s\n",
      "       963           1.2812            0.70s\n",
      "       964           1.2811            0.68s\n",
      "       965           1.2810            0.66s\n",
      "       966           1.2810            0.64s\n",
      "       967           1.2808            0.62s\n",
      "       968           1.2807            0.60s\n",
      "       969           1.2806            0.58s\n",
      "       970           1.2806            0.57s\n",
      "       971           1.2805            0.55s\n",
      "       972           1.2803            0.53s\n",
      "       973           1.2802            0.51s\n",
      "       974           1.2801            0.49s\n",
      "       975           1.2801            0.47s\n",
      "       976           1.2800            0.45s\n",
      "       977           1.2799            0.43s\n",
      "       978           1.2798            0.41s\n",
      "       979           1.2797            0.40s\n",
      "       980           1.2796            0.38s\n",
      "       981           1.2794            0.36s\n",
      "       982           1.2794            0.34s\n",
      "       983           1.2793            0.32s\n",
      "       984           1.2792            0.30s\n",
      "       985           1.2791            0.28s\n",
      "       986           1.2790            0.26s\n",
      "       987           1.2789            0.24s\n",
      "       988           1.2788            0.23s\n",
      "       989           1.2787            0.21s\n",
      "       990           1.2786            0.19s\n",
      "       991           1.2785            0.17s\n",
      "       992           1.2785            0.15s\n",
      "       993           1.2783            0.13s\n",
      "       994           1.2782            0.11s\n",
      "       995           1.2781            0.09s\n",
      "       996           1.2780            0.08s\n",
      "       997           1.2780            0.06s\n",
      "       998           1.2779            0.04s\n",
      "       999           1.2778            0.02s\n",
      "      1000           1.2777            0.00s\n",
      "      Iter       Train Loss   Remaining Time \n",
      "         1           1.3860           19.98s\n",
      "         2           1.3858           19.46s\n",
      "         3           1.3855           19.61s\n",
      "         4           1.3853           19.42s\n",
      "         5           1.3850           19.30s\n",
      "         6           1.3848           19.38s\n",
      "         7           1.3846           19.29s\n",
      "         8           1.3844           19.22s\n",
      "         9           1.3841           19.16s\n",
      "        10           1.3839           19.11s\n",
      "        11           1.3837           19.06s\n",
      "        12           1.3835           19.10s\n",
      "        13           1.3833           19.06s\n",
      "        14           1.3831           19.02s\n",
      "        15           1.3829           18.98s\n",
      "        16           1.3827           18.94s\n",
      "        17           1.3825           18.91s\n",
      "        18           1.3823           18.88s\n",
      "        19           1.3821           18.85s\n",
      "        20           1.3819           18.82s\n",
      "        21           1.3818           18.83s\n",
      "        22           1.3816           18.85s\n",
      "        23           1.3814           18.82s\n",
      "        24           1.3812           18.79s\n",
      "        25           1.3811           18.76s\n",
      "        26           1.3809           18.77s\n",
      "        27           1.3807           18.74s\n",
      "        28           1.3806           18.71s\n",
      "        29           1.3804           18.68s\n",
      "        30           1.3802           18.69s\n",
      "        31           1.3800           18.63s\n",
      "        32           1.3799           18.63s\n",
      "        33           1.3797           18.61s\n",
      "        34           1.3795           18.61s\n",
      "        35           1.3794           18.56s\n",
      "        36           1.3792           18.53s\n",
      "        37           1.3791           18.53s\n",
      "        38           1.3789           18.51s\n",
      "        39           1.3788           18.51s\n",
      "        40           1.3786           18.48s\n",
      "        41           1.3785           18.45s\n",
      "        42           1.3783           18.43s\n",
      "        43           1.3782           18.41s\n",
      "        44           1.3780           18.40s\n",
      "        45           1.3779           18.38s\n",
      "        46           1.3778           18.35s\n",
      "        47           1.3776           18.35s\n",
      "        48           1.3775           18.33s\n",
      "        49           1.3773           18.30s\n",
      "        50           1.3772           18.28s\n",
      "        51           1.3771           18.25s\n",
      "        52           1.3770           18.23s\n",
      "        53           1.3768           18.21s\n",
      "        54           1.3767           18.18s\n",
      "        55           1.3765           18.18s\n",
      "        56           1.3764           18.16s\n",
      "        57           1.3763           18.13s\n",
      "        58           1.3761           18.11s\n",
      "        59           1.3760           18.09s\n",
      "        60           1.3759           18.08s\n",
      "        61           1.3758           18.06s\n",
      "        62           1.3756           18.03s\n",
      "        63           1.3755           18.01s\n",
      "        64           1.3754           17.99s\n",
      "        65           1.3753           17.97s\n",
      "        66           1.3751           17.94s\n",
      "        67           1.3750           17.94s\n",
      "        68           1.3749           17.93s\n",
      "        69           1.3748           17.90s\n",
      "        70           1.3747           17.88s\n",
      "        71           1.3746           17.86s\n",
      "        72           1.3745           17.84s\n",
      "        73           1.3744           17.83s\n",
      "        74           1.3742           17.81s\n",
      "        75           1.3741           17.78s\n",
      "        76           1.3740           17.76s\n",
      "        77           1.3739           17.74s\n",
      "        78           1.3738           17.73s\n",
      "        79           1.3737           17.71s\n",
      "        80           1.3736           17.69s\n",
      "        81           1.3735           17.68s\n",
      "        82           1.3734           17.65s\n",
      "        83           1.3733           17.63s\n",
      "        84           1.3732           17.61s\n",
      "        85           1.3731           17.59s\n",
      "        86           1.3730           17.58s\n",
      "        87           1.3728           17.56s\n",
      "        88           1.3727           17.55s\n",
      "        89           1.3726           17.53s\n",
      "        90           1.3725           17.51s\n",
      "        91           1.3724           17.49s\n",
      "        92           1.3723           17.48s\n",
      "        93           1.3722           17.46s\n",
      "        94           1.3720           17.44s\n",
      "        95           1.3720           17.41s\n",
      "        96           1.3719           17.39s\n",
      "        97           1.3718           17.37s\n",
      "        98           1.3716           17.35s\n",
      "        99           1.3715           17.33s\n",
      "       100           1.3715           17.31s\n",
      "       101           1.3714           17.29s\n",
      "       102           1.3713           17.28s\n",
      "       103           1.3711           17.26s\n",
      "       104           1.3710           17.24s\n",
      "       105           1.3709           17.22s\n",
      "       106           1.3708           17.20s\n",
      "       107           1.3707           17.18s\n",
      "       108           1.3706           17.16s\n",
      "       109           1.3705           17.14s\n",
      "       110           1.3703           17.12s\n",
      "       111           1.3703           17.10s\n",
      "       112           1.3701           17.08s\n",
      "       113           1.3700           17.06s\n",
      "       114           1.3699           17.04s\n",
      "       115           1.3698           17.02s\n",
      "       116           1.3696           17.00s\n",
      "       117           1.3695           16.98s\n",
      "       118           1.3693           16.96s\n",
      "       119           1.3692           16.94s\n",
      "       120           1.3691           16.92s\n",
      "       121           1.3690           16.90s\n",
      "       122           1.3688           16.88s\n",
      "       123           1.3687           16.86s\n",
      "       124           1.3686           16.85s\n",
      "       125           1.3686           16.84s\n",
      "       126           1.3684           16.81s\n",
      "       127           1.3683           16.80s\n",
      "       128           1.3682           16.78s\n",
      "       129           1.3680           16.76s\n",
      "       130           1.3678           16.74s\n",
      "       131           1.3676           16.72s\n",
      "       132           1.3676           16.70s\n",
      "       133           1.3675           16.68s\n",
      "       134           1.3674           16.66s\n",
      "       135           1.3672           16.64s\n",
      "       136           1.3670           16.62s\n",
      "       137           1.3670           16.60s\n",
      "       138           1.3669           16.58s\n",
      "       139           1.3667           16.56s\n",
      "       140           1.3665           16.54s\n",
      "       141           1.3664           16.52s\n",
      "       142           1.3662           16.50s\n",
      "       143           1.3662           16.47s\n",
      "       144           1.3660           16.45s\n",
      "       145           1.3658           16.43s\n",
      "       146           1.3657           16.42s\n",
      "       147           1.3655           16.40s\n",
      "       148           1.3654           16.38s\n",
      "       149           1.3654           16.36s\n",
      "       150           1.3651           16.34s\n",
      "       151           1.3650           16.32s\n",
      "       152           1.3649           16.30s\n",
      "       153           1.3648           16.28s\n",
      "       154           1.3646           16.26s\n",
      "       155           1.3644           16.24s\n",
      "       156           1.3644           16.22s\n",
      "       157           1.3643           16.20s\n",
      "       158           1.3641           16.18s\n",
      "       159           1.3639           16.16s\n",
      "       160           1.3638           16.14s\n",
      "       161           1.3636           16.12s\n",
      "       162           1.3635           16.10s\n",
      "       163           1.3634           16.08s\n",
      "       164           1.3633           16.06s\n",
      "       165           1.3631           16.04s\n",
      "       166           1.3630           16.02s\n",
      "       167           1.3629           16.00s\n",
      "       168           1.3627           15.98s\n",
      "       169           1.3626           15.96s\n",
      "       170           1.3623           15.94s\n",
      "       171           1.3622           15.93s\n",
      "       172           1.3622           15.91s\n",
      "       173           1.3620           15.89s\n",
      "       174           1.3618           15.86s\n",
      "       175           1.3617           15.84s\n",
      "       176           1.3615           15.83s\n",
      "       177           1.3615           15.81s\n",
      "       178           1.3614           15.79s\n",
      "       179           1.3612           15.77s\n",
      "       180           1.3611           15.75s\n",
      "       181           1.3609           15.73s\n",
      "       182           1.3609           15.71s\n",
      "       183           1.3607           15.69s\n",
      "       184           1.3605           15.67s\n",
      "       185           1.3605           15.65s\n",
      "       186           1.3603           15.63s\n",
      "       187           1.3602           15.61s\n",
      "       188           1.3601           15.59s\n",
      "       189           1.3600           15.57s\n",
      "       190           1.3598           15.55s\n",
      "       191           1.3596           15.53s\n",
      "       192           1.3595           15.51s\n",
      "       193           1.3594           15.49s\n",
      "       194           1.3593           15.47s\n",
      "       195           1.3592           15.46s\n",
      "       196           1.3591           15.44s\n",
      "       197           1.3589           15.42s\n",
      "       198           1.3587           15.40s\n",
      "       199           1.3585           15.38s\n",
      "       200           1.3584           15.36s\n",
      "       201           1.3583           15.34s\n",
      "       202           1.3582           15.32s\n",
      "       203           1.3581           15.30s\n",
      "       204           1.3579           15.28s\n",
      "       205           1.3578           15.26s\n",
      "       206           1.3577           15.24s\n",
      "       207           1.3576           15.22s\n",
      "       208           1.3575           15.20s\n",
      "       209           1.3573           15.18s\n",
      "       210           1.3572           15.16s\n",
      "       211           1.3570           15.14s\n",
      "       212           1.3569           15.13s\n",
      "       213           1.3569           15.11s\n",
      "       214           1.3567           15.09s\n",
      "       215           1.3566           15.07s\n",
      "       216           1.3564           15.07s\n",
      "       217           1.3562           15.05s\n",
      "       218           1.3560           15.03s\n",
      "       219           1.3559           15.01s\n",
      "       220           1.3558           14.99s\n",
      "       221           1.3556           14.97s\n",
      "       222           1.3555           14.95s\n",
      "       223           1.3554           14.93s\n",
      "       224           1.3552           14.91s\n",
      "       225           1.3550           14.89s\n",
      "       226           1.3549           14.87s\n",
      "       227           1.3548           14.85s\n",
      "       228           1.3546           14.83s\n",
      "       229           1.3545           14.81s\n",
      "       230           1.3543           14.79s\n",
      "       231           1.3542           14.77s\n",
      "       232           1.3541           14.76s\n",
      "       233           1.3540           14.74s\n",
      "       234           1.3539           14.72s\n",
      "       235           1.3537           14.70s\n",
      "       236           1.3535           14.68s\n",
      "       237           1.3534           14.66s\n",
      "       238           1.3533           14.64s\n",
      "       239           1.3532           14.62s\n",
      "       240           1.3531           14.60s\n",
      "       241           1.3530           14.58s\n",
      "       242           1.3528           14.56s\n",
      "       243           1.3526           14.54s\n",
      "       244           1.3526           14.53s\n",
      "       245           1.3525           14.51s\n",
      "       246           1.3523           14.49s\n",
      "       247           1.3522           14.47s\n",
      "       248           1.3520           14.45s\n",
      "       249           1.3518           14.43s\n",
      "       250           1.3517           14.41s\n",
      "       251           1.3516           14.39s\n",
      "       252           1.3515           14.37s\n",
      "       253           1.3514           14.35s\n",
      "       254           1.3512           14.33s\n",
      "       255           1.3511           14.31s\n",
      "       256           1.3509           14.29s\n",
      "       257           1.3507           14.27s\n",
      "       258           1.3506           14.25s\n",
      "       259           1.3506           14.23s\n",
      "       260           1.3504           14.21s\n",
      "       261           1.3503           14.20s\n",
      "       262           1.3501           14.18s\n",
      "       263           1.3499           14.16s\n",
      "       264           1.3498           14.14s\n",
      "       265           1.3497           14.12s\n",
      "       266           1.3496           14.10s\n",
      "       267           1.3495           14.08s\n",
      "       268           1.3493           14.06s\n",
      "       269           1.3491           14.04s\n",
      "       270           1.3490           14.02s\n",
      "       271           1.3490           14.00s\n",
      "       272           1.3488           13.98s\n",
      "       273           1.3486           13.96s\n",
      "       274           1.3484           13.94s\n",
      "       275           1.3483           13.92s\n",
      "       276           1.3481           13.90s\n",
      "       277           1.3480           13.88s\n",
      "       278           1.3480           13.87s\n",
      "       279           1.3478           13.85s\n",
      "       280           1.3478           13.83s\n",
      "       281           1.3475           13.81s\n",
      "       282           1.3475           13.79s\n",
      "       283           1.3474           13.77s\n",
      "       284           1.3472           13.75s\n",
      "       285           1.3471           13.73s\n",
      "       286           1.3469           13.71s\n",
      "       287           1.3468           13.69s\n",
      "       288           1.3468           13.67s\n",
      "       289           1.3467           13.65s\n",
      "       290           1.3466           13.63s\n",
      "       291           1.3464           13.61s\n",
      "       292           1.3464           13.60s\n",
      "       293           1.3462           13.58s\n",
      "       294           1.3461           13.56s\n",
      "       295           1.3460           13.54s\n",
      "       296           1.3459           13.52s\n",
      "       297           1.3459           13.50s\n",
      "       298           1.3457           13.48s\n",
      "       299           1.3455           13.46s\n",
      "       300           1.3455           13.44s\n",
      "       301           1.3453           13.42s\n",
      "       302           1.3452           13.40s\n",
      "       303           1.3451           13.38s\n",
      "       304           1.3451           13.36s\n",
      "       305           1.3450           13.34s\n",
      "       306           1.3449           13.32s\n",
      "       307           1.3447           13.31s\n",
      "       308           1.3446           13.29s\n",
      "       309           1.3444           13.27s\n",
      "       310           1.3444           13.25s\n",
      "       311           1.3442           13.23s\n",
      "       312           1.3440           13.21s\n",
      "       313           1.3440           13.19s\n",
      "       314           1.3438           13.17s\n",
      "       315           1.3438           13.15s\n",
      "       316           1.3436           13.13s\n",
      "       317           1.3435           13.11s\n",
      "       318           1.3434           13.10s\n",
      "       319           1.3433           13.08s\n",
      "       320           1.3432           13.06s\n",
      "       321           1.3430           13.04s\n",
      "       322           1.3430           13.02s\n",
      "       323           1.3429           13.00s\n",
      "       324           1.3428           12.98s\n",
      "       325           1.3427           12.96s\n",
      "       326           1.3426           12.94s\n",
      "       327           1.3424           12.92s\n",
      "       328           1.3422           12.90s\n",
      "       329           1.3421           12.88s\n",
      "       330           1.3420           12.86s\n",
      "       331           1.3419           12.84s\n",
      "       332           1.3418           12.83s\n",
      "       333           1.3417           12.81s\n",
      "       334           1.3415           12.79s\n",
      "       335           1.3415           12.77s\n",
      "       336           1.3414           12.75s\n",
      "       337           1.3413           12.73s\n",
      "       338           1.3411           12.71s\n",
      "       339           1.3409           12.69s\n",
      "       340           1.3408           12.67s\n",
      "       341           1.3406           12.65s\n",
      "       342           1.3405           12.63s\n",
      "       343           1.3404           12.61s\n",
      "       344           1.3402           12.59s\n",
      "       345           1.3401           12.57s\n",
      "       346           1.3400           12.56s\n",
      "       347           1.3399           12.54s\n",
      "       348           1.3398           12.52s\n",
      "       349           1.3397           12.50s\n",
      "       350           1.3395           12.48s\n",
      "       351           1.3394           12.46s\n",
      "       352           1.3393           12.44s\n",
      "       353           1.3392           12.42s\n",
      "       354           1.3392           12.40s\n",
      "       355           1.3390           12.38s\n",
      "       356           1.3389           12.36s\n",
      "       357           1.3387           12.34s\n",
      "       358           1.3386           12.32s\n",
      "       359           1.3385           12.30s\n",
      "       360           1.3384           12.28s\n",
      "       361           1.3382           12.26s\n",
      "       362           1.3381           12.25s\n",
      "       363           1.3380           12.23s\n",
      "       364           1.3379           12.21s\n",
      "       365           1.3378           12.19s\n",
      "       366           1.3377           12.17s\n",
      "       367           1.3377           12.15s\n",
      "       368           1.3375           12.13s\n",
      "       369           1.3374           12.11s\n",
      "       370           1.3372           12.09s\n",
      "       371           1.3371           12.07s\n",
      "       372           1.3370           12.05s\n",
      "       373           1.3369           12.04s\n",
      "       374           1.3368           12.02s\n",
      "       375           1.3367           12.00s\n",
      "       376           1.3365           11.98s\n",
      "       377           1.3364           11.96s\n",
      "       378           1.3363           11.94s\n",
      "       379           1.3362           11.92s\n",
      "       380           1.3362           11.90s\n",
      "       381           1.3361           11.88s\n",
      "       382           1.3361           11.86s\n",
      "       383           1.3359           11.84s\n",
      "       384           1.3358           11.82s\n",
      "       385           1.3356           11.80s\n",
      "       386           1.3355           11.79s\n",
      "       387           1.3353           11.77s\n",
      "       388           1.3353           11.75s\n",
      "       389           1.3351           11.73s\n",
      "       390           1.3350           11.71s\n",
      "       391           1.3348           11.69s\n",
      "       392           1.3347           11.67s\n",
      "       393           1.3346           11.65s\n",
      "       394           1.3344           11.63s\n",
      "       395           1.3343           11.61s\n",
      "       396           1.3342           11.59s\n",
      "       397           1.3341           11.57s\n",
      "       398           1.3340           11.55s\n",
      "       399           1.3340           11.54s\n",
      "       400           1.3339           11.52s\n",
      "       401           1.3339           11.50s\n",
      "       402           1.3338           11.48s\n",
      "       403           1.3337           11.46s\n",
      "       404           1.3335           11.44s\n",
      "       405           1.3334           11.42s\n",
      "       406           1.3332           11.40s\n",
      "       407           1.3331           11.38s\n",
      "       408           1.3329           11.36s\n",
      "       409           1.3329           11.34s\n",
      "       410           1.3328           11.32s\n",
      "       411           1.3327           11.30s\n",
      "       412           1.3326           11.28s\n",
      "       413           1.3325           11.27s\n",
      "       414           1.3323           11.25s\n",
      "       415           1.3322           11.23s\n",
      "       416           1.3320           11.21s\n",
      "       417           1.3320           11.19s\n",
      "       418           1.3318           11.17s\n",
      "       419           1.3317           11.15s\n",
      "       420           1.3315           11.13s\n",
      "       421           1.3314           11.11s\n",
      "       422           1.3313           11.09s\n",
      "       423           1.3312           11.07s\n",
      "       424           1.3312           11.05s\n",
      "       425           1.3311           11.04s\n",
      "       426           1.3310           11.02s\n",
      "       427           1.3308           11.00s\n",
      "       428           1.3307           10.98s\n",
      "       429           1.3305           10.96s\n",
      "       430           1.3303           10.94s\n",
      "       431           1.3302           10.92s\n",
      "       432           1.3302           10.90s\n",
      "       433           1.3301           10.88s\n",
      "       434           1.3301           10.86s\n",
      "       435           1.3300           10.84s\n",
      "       436           1.3300           10.82s\n",
      "       437           1.3298           10.80s\n",
      "       438           1.3297           10.78s\n",
      "       439           1.3296           10.77s\n",
      "       440           1.3295           10.75s\n",
      "       441           1.3294           10.73s\n",
      "       442           1.3293           10.71s\n",
      "       443           1.3292           10.69s\n",
      "       444           1.3290           10.67s\n",
      "       445           1.3289           10.65s\n",
      "       446           1.3288           10.63s\n",
      "       447           1.3286           10.61s\n",
      "       448           1.3285           10.59s\n",
      "       449           1.3285           10.57s\n",
      "       450           1.3283           10.55s\n",
      "       451           1.3282           10.53s\n",
      "       452           1.3281           10.51s\n",
      "       453           1.3280           10.49s\n",
      "       454           1.3279           10.48s\n",
      "       455           1.3278           10.46s\n",
      "       456           1.3278           10.44s\n",
      "       457           1.3276           10.42s\n",
      "       458           1.3276           10.40s\n",
      "       459           1.3275           10.38s\n",
      "       460           1.3274           10.36s\n",
      "       461           1.3273           10.34s\n",
      "       462           1.3271           10.32s\n",
      "       463           1.3270           10.30s\n",
      "       464           1.3269           10.28s\n",
      "       465           1.3268           10.26s\n",
      "       466           1.3267           10.24s\n",
      "       467           1.3265           10.23s\n",
      "       468           1.3264           10.21s\n",
      "       469           1.3263           10.19s\n",
      "       470           1.3262           10.17s\n",
      "       471           1.3261           10.15s\n",
      "       472           1.3260           10.13s\n",
      "       473           1.3259           10.11s\n",
      "       474           1.3257           10.09s\n",
      "       475           1.3256           10.07s\n",
      "       476           1.3255           10.05s\n",
      "       477           1.3253           10.03s\n",
      "       478           1.3253           10.02s\n",
      "       479           1.3252           10.00s\n",
      "       480           1.3250            9.98s\n",
      "       481           1.3249            9.96s\n",
      "       482           1.3248            9.94s\n",
      "       483           1.3247            9.92s\n",
      "       484           1.3247            9.90s\n",
      "       485           1.3246            9.88s\n",
      "       486           1.3245            9.86s\n",
      "       487           1.3245            9.84s\n",
      "       488           1.3244            9.82s\n",
      "       489           1.3242            9.81s\n",
      "       490           1.3242            9.79s\n",
      "       491           1.3240            9.77s\n",
      "       492           1.3239            9.75s\n",
      "       493           1.3238            9.73s\n",
      "       494           1.3237            9.71s\n",
      "       495           1.3236            9.69s\n",
      "       496           1.3235            9.67s\n",
      "       497           1.3234            9.65s\n",
      "       498           1.3233            9.63s\n",
      "       499           1.3232            9.61s\n",
      "       500           1.3230            9.59s\n",
      "       501           1.3229            9.58s\n",
      "       502           1.3227            9.56s\n",
      "       503           1.3225            9.54s\n",
      "       504           1.3224            9.52s\n",
      "       505           1.3223            9.50s\n",
      "       506           1.3222            9.48s\n",
      "       507           1.3221            9.46s\n",
      "       508           1.3220            9.44s\n",
      "       509           1.3220            9.42s\n",
      "       510           1.3220            9.40s\n",
      "       511           1.3219            9.38s\n",
      "       512           1.3218            9.37s\n",
      "       513           1.3216            9.35s\n",
      "       514           1.3215            9.33s\n",
      "       515           1.3214            9.31s\n",
      "       516           1.3212            9.29s\n",
      "       517           1.3211            9.27s\n",
      "       518           1.3211            9.25s\n",
      "       519           1.3209            9.23s\n",
      "       520           1.3208            9.21s\n",
      "       521           1.3206            9.19s\n",
      "       522           1.3205            9.17s\n",
      "       523           1.3204            9.15s\n",
      "       524           1.3202            9.13s\n",
      "       525           1.3202            9.11s\n",
      "       526           1.3201            9.09s\n",
      "       527           1.3199            9.07s\n",
      "       528           1.3198            9.06s\n",
      "       529           1.3197            9.04s\n",
      "       530           1.3195            9.02s\n",
      "       531           1.3194            9.00s\n",
      "       532           1.3193            8.98s\n",
      "       533           1.3191            8.96s\n",
      "       534           1.3189            8.94s\n",
      "       535           1.3188            8.92s\n",
      "       536           1.3188            8.90s\n",
      "       537           1.3187            8.88s\n",
      "       538           1.3186            8.86s\n",
      "       539           1.3184            8.84s\n",
      "       540           1.3183            8.82s\n",
      "       541           1.3182            8.81s\n",
      "       542           1.3180            8.79s\n",
      "       543           1.3179            8.77s\n",
      "       544           1.3177            8.75s\n",
      "       545           1.3176            8.73s\n",
      "       546           1.3176            8.71s\n",
      "       547           1.3175            8.69s\n",
      "       548           1.3175            8.67s\n",
      "       549           1.3173            8.65s\n",
      "       550           1.3172            8.64s\n",
      "       551           1.3170            8.62s\n",
      "       552           1.3169            8.60s\n",
      "       553           1.3168            8.58s\n",
      "       554           1.3167            8.56s\n",
      "       555           1.3165            8.54s\n",
      "       556           1.3164            8.52s\n",
      "       557           1.3164            8.50s\n",
      "       558           1.3162            8.48s\n",
      "       559           1.3161            8.46s\n",
      "       560           1.3159            8.44s\n",
      "       561           1.3159            8.42s\n",
      "       562           1.3158            8.40s\n",
      "       563           1.3157            8.39s\n",
      "       564           1.3157            8.37s\n",
      "       565           1.3156            8.35s\n",
      "       566           1.3155            8.33s\n",
      "       567           1.3153            8.31s\n",
      "       568           1.3152            8.29s\n",
      "       569           1.3151            8.27s\n",
      "       570           1.3150            8.25s\n",
      "       571           1.3148            8.23s\n",
      "       572           1.3147            8.21s\n",
      "       573           1.3146            8.19s\n",
      "       574           1.3145            8.17s\n",
      "       575           1.3144            8.15s\n",
      "       576           1.3142            8.13s\n",
      "       577           1.3140            8.12s\n",
      "       578           1.3138            8.10s\n",
      "       579           1.3137            8.08s\n",
      "       580           1.3136            8.06s\n",
      "       581           1.3135            8.04s\n",
      "       582           1.3134            8.02s\n",
      "       583           1.3133            8.00s\n",
      "       584           1.3132            7.98s\n",
      "       585           1.3131            7.96s\n",
      "       586           1.3129            7.94s\n",
      "       587           1.3128            7.92s\n",
      "       588           1.3128            7.90s\n",
      "       589           1.3127            7.88s\n",
      "       590           1.3126            7.87s\n",
      "       591           1.3125            7.85s\n",
      "       592           1.3124            7.83s\n",
      "       593           1.3123            7.81s\n",
      "       594           1.3122            7.79s\n",
      "       595           1.3120            7.77s\n",
      "       596           1.3119            7.75s\n",
      "       597           1.3118            7.73s\n",
      "       598           1.3116            7.71s\n",
      "       599           1.3115            7.69s\n",
      "       600           1.3114            7.67s\n",
      "       601           1.3113            7.66s\n",
      "       602           1.3111            7.64s\n",
      "       603           1.3110            7.62s\n",
      "       604           1.3109            7.60s\n",
      "       605           1.3108            7.58s\n",
      "       606           1.3106            7.56s\n",
      "       607           1.3105            7.54s\n",
      "       608           1.3104            7.52s\n",
      "       609           1.3104            7.50s\n",
      "       610           1.3102            7.48s\n",
      "       611           1.3101            7.46s\n",
      "       612           1.3099            7.44s\n",
      "       613           1.3098            7.42s\n",
      "       614           1.3097            7.41s\n",
      "       615           1.3096            7.39s\n",
      "       616           1.3095            7.37s\n",
      "       617           1.3094            7.35s\n",
      "       618           1.3092            7.33s\n",
      "       619           1.3092            7.31s\n",
      "       620           1.3091            7.29s\n",
      "       621           1.3089            7.27s\n",
      "       622           1.3089            7.25s\n",
      "       623           1.3088            7.23s\n",
      "       624           1.3087            7.21s\n",
      "       625           1.3087            7.19s\n",
      "       626           1.3085            7.17s\n",
      "       627           1.3084            7.16s\n",
      "       628           1.3083            7.14s\n",
      "       629           1.3082            7.12s\n",
      "       630           1.3082            7.10s\n",
      "       631           1.3080            7.08s\n",
      "       632           1.3079            7.06s\n",
      "       633           1.3078            7.04s\n",
      "       634           1.3077            7.02s\n",
      "       635           1.3075            7.00s\n",
      "       636           1.3075            6.98s\n",
      "       637           1.3074            6.96s\n",
      "       638           1.3072            6.94s\n",
      "       639           1.3071            6.92s\n",
      "       640           1.3069            6.91s\n",
      "       641           1.3068            6.89s\n",
      "       642           1.3067            6.87s\n",
      "       643           1.3066            6.85s\n",
      "       644           1.3065            6.83s\n",
      "       645           1.3064            6.81s\n",
      "       646           1.3063            6.79s\n",
      "       647           1.3061            6.77s\n",
      "       648           1.3060            6.75s\n",
      "       649           1.3059            6.73s\n",
      "       650           1.3058            6.71s\n",
      "       651           1.3057            6.69s\n",
      "       652           1.3055            6.68s\n",
      "       653           1.3054            6.66s\n",
      "       654           1.3052            6.64s\n",
      "       655           1.3051            6.62s\n",
      "       656           1.3050            6.60s\n",
      "       657           1.3049            6.58s\n",
      "       658           1.3048            6.56s\n",
      "       659           1.3047            6.54s\n",
      "       660           1.3045            6.52s\n",
      "       661           1.3045            6.50s\n",
      "       662           1.3044            6.48s\n",
      "       663           1.3043            6.46s\n",
      "       664           1.3043            6.44s\n",
      "       665           1.3041            6.43s\n",
      "       666           1.3041            6.41s\n",
      "       667           1.3039            6.39s\n",
      "       668           1.3037            6.37s\n",
      "       669           1.3036            6.35s\n",
      "       670           1.3035            6.33s\n",
      "       671           1.3033            6.31s\n",
      "       672           1.3032            6.29s\n",
      "       673           1.3031            6.27s\n",
      "       674           1.3031            6.25s\n",
      "       675           1.3030            6.23s\n",
      "       676           1.3029            6.21s\n",
      "       677           1.3028            6.20s\n",
      "       678           1.3027            6.18s\n",
      "       679           1.3025            6.16s\n",
      "       680           1.3024            6.14s\n",
      "       681           1.3023            6.12s\n",
      "       682           1.3021            6.10s\n",
      "       683           1.3021            6.08s\n",
      "       684           1.3020            6.06s\n",
      "       685           1.3019            6.04s\n",
      "       686           1.3018            6.02s\n",
      "       687           1.3016            6.00s\n",
      "       688           1.3015            5.98s\n",
      "       689           1.3013            5.96s\n",
      "       690           1.3011            5.95s\n",
      "       691           1.3010            5.93s\n",
      "       692           1.3009            5.91s\n",
      "       693           1.3008            5.89s\n",
      "       694           1.3007            5.87s\n",
      "       695           1.3006            5.85s\n",
      "       696           1.3005            5.83s\n",
      "       697           1.3004            5.81s\n",
      "       698           1.3003            5.79s\n",
      "       699           1.3003            5.77s\n",
      "       700           1.3002            5.75s\n",
      "       701           1.3001            5.73s\n",
      "       702           1.2999            5.71s\n",
      "       703           1.2998            5.70s\n",
      "       704           1.2997            5.68s\n",
      "       705           1.2995            5.66s\n",
      "       706           1.2994            5.64s\n",
      "       707           1.2993            5.62s\n",
      "       708           1.2992            5.60s\n",
      "       709           1.2991            5.58s\n",
      "       710           1.2990            5.56s\n",
      "       711           1.2990            5.54s\n",
      "       712           1.2988            5.52s\n",
      "       713           1.2987            5.50s\n",
      "       714           1.2987            5.48s\n",
      "       715           1.2985            5.47s\n",
      "       716           1.2984            5.45s\n",
      "       717           1.2984            5.43s\n",
      "       718           1.2983            5.41s\n",
      "       719           1.2981            5.39s\n",
      "       720           1.2980            5.37s\n",
      "       721           1.2979            5.35s\n",
      "       722           1.2977            5.33s\n",
      "       723           1.2976            5.31s\n",
      "       724           1.2975            5.29s\n",
      "       725           1.2974            5.27s\n",
      "       726           1.2973            5.25s\n",
      "       727           1.2973            5.24s\n",
      "       728           1.2971            5.22s\n",
      "       729           1.2971            5.20s\n",
      "       730           1.2970            5.18s\n",
      "       731           1.2969            5.16s\n",
      "       732           1.2967            5.14s\n",
      "       733           1.2966            5.12s\n",
      "       734           1.2965            5.10s\n",
      "       735           1.2964            5.08s\n",
      "       736           1.2963            5.06s\n",
      "       737           1.2963            5.04s\n",
      "       738           1.2961            5.03s\n",
      "       739           1.2960            5.01s\n",
      "       740           1.2958            4.99s\n",
      "       741           1.2957            4.97s\n",
      "       742           1.2956            4.95s\n",
      "       743           1.2954            4.93s\n",
      "       744           1.2953            4.91s\n",
      "       745           1.2952            4.89s\n",
      "       746           1.2950            4.87s\n",
      "       747           1.2949            4.85s\n",
      "       748           1.2948            4.83s\n",
      "       749           1.2947            4.81s\n",
      "       750           1.2946            4.79s\n",
      "       751           1.2946            4.78s\n",
      "       752           1.2945            4.76s\n",
      "       753           1.2944            4.74s\n",
      "       754           1.2943            4.72s\n",
      "       755           1.2942            4.70s\n",
      "       756           1.2942            4.68s\n",
      "       757           1.2940            4.66s\n",
      "       758           1.2939            4.64s\n",
      "       759           1.2937            4.62s\n",
      "       760           1.2936            4.60s\n",
      "       761           1.2936            4.58s\n",
      "       762           1.2935            4.56s\n",
      "       763           1.2933            4.55s\n",
      "       764           1.2932            4.53s\n",
      "       765           1.2931            4.51s\n",
      "       766           1.2930            4.49s\n",
      "       767           1.2929            4.47s\n",
      "       768           1.2929            4.45s\n",
      "       769           1.2928            4.43s\n",
      "       770           1.2927            4.41s\n",
      "       771           1.2925            4.39s\n",
      "       772           1.2924            4.37s\n",
      "       773           1.2923            4.35s\n",
      "       774           1.2922            4.33s\n",
      "       775           1.2921            4.32s\n",
      "       776           1.2920            4.30s\n",
      "       777           1.2918            4.28s\n",
      "       778           1.2916            4.26s\n",
      "       779           1.2915            4.24s\n",
      "       780           1.2914            4.22s\n",
      "       781           1.2913            4.20s\n",
      "       782           1.2912            4.18s\n",
      "       783           1.2911            4.16s\n",
      "       784           1.2910            4.14s\n",
      "       785           1.2910            4.12s\n",
      "       786           1.2909            4.10s\n",
      "       787           1.2909            4.08s\n",
      "       788           1.2908            4.07s\n",
      "       789           1.2906            4.05s\n",
      "       790           1.2904            4.03s\n",
      "       791           1.2904            4.01s\n",
      "       792           1.2903            3.99s\n",
      "       793           1.2902            3.97s\n",
      "       794           1.2900            3.95s\n",
      "       795           1.2899            3.93s\n",
      "       796           1.2898            3.91s\n",
      "       797           1.2898            3.89s\n",
      "       798           1.2897            3.87s\n",
      "       799           1.2896            3.85s\n",
      "       800           1.2896            3.84s\n",
      "       801           1.2895            3.82s\n",
      "       802           1.2893            3.80s\n",
      "       803           1.2892            3.78s\n",
      "       804           1.2891            3.76s\n",
      "       805           1.2890            3.74s\n",
      "       806           1.2889            3.72s\n",
      "       807           1.2888            3.70s\n",
      "       808           1.2886            3.68s\n",
      "       809           1.2885            3.66s\n",
      "       810           1.2884            3.64s\n",
      "       811           1.2882            3.62s\n",
      "       812           1.2881            3.61s\n",
      "       813           1.2880            3.59s\n",
      "       814           1.2879            3.57s\n",
      "       815           1.2878            3.55s\n",
      "       816           1.2878            3.53s\n",
      "       817           1.2877            3.51s\n",
      "       818           1.2877            3.49s\n",
      "       819           1.2875            3.47s\n",
      "       820           1.2874            3.45s\n",
      "       821           1.2873            3.43s\n",
      "       822           1.2871            3.41s\n",
      "       823           1.2870            3.39s\n",
      "       824           1.2869            3.38s\n",
      "       825           1.2869            3.36s\n",
      "       826           1.2868            3.34s\n",
      "       827           1.2867            3.32s\n",
      "       828           1.2866            3.30s\n",
      "       829           1.2864            3.28s\n",
      "       830           1.2862            3.26s\n",
      "       831           1.2862            3.24s\n",
      "       832           1.2861            3.22s\n",
      "       833           1.2860            3.20s\n",
      "       834           1.2859            3.18s\n",
      "       835           1.2857            3.16s\n",
      "       836           1.2856            3.15s\n",
      "       837           1.2855            3.13s\n",
      "       838           1.2855            3.11s\n",
      "       839           1.2855            3.09s\n",
      "       840           1.2853            3.07s\n",
      "       841           1.2852            3.05s\n",
      "       842           1.2851            3.03s\n",
      "       843           1.2850            3.01s\n",
      "       844           1.2850            2.99s\n",
      "       845           1.2848            2.97s\n",
      "       846           1.2848            2.95s\n",
      "       847           1.2847            2.93s\n",
      "       848           1.2846            2.91s\n",
      "       849           1.2844            2.90s\n",
      "       850           1.2843            2.88s\n",
      "       851           1.2841            2.86s\n",
      "       852           1.2841            2.84s\n",
      "       853           1.2840            2.82s\n",
      "       854           1.2838            2.80s\n",
      "       855           1.2838            2.78s\n",
      "       856           1.2837            2.76s\n",
      "       857           1.2836            2.74s\n",
      "       858           1.2835            2.72s\n",
      "       859           1.2833            2.70s\n",
      "       860           1.2832            2.69s\n",
      "       861           1.2830            2.67s\n",
      "       862           1.2829            2.65s\n",
      "       863           1.2828            2.63s\n",
      "       864           1.2826            2.61s\n",
      "       865           1.2825            2.59s\n",
      "       866           1.2824            2.57s\n",
      "       867           1.2824            2.55s\n",
      "       868           1.2824            2.53s\n",
      "       869           1.2823            2.51s\n",
      "       870           1.2821            2.49s\n",
      "       871           1.2820            2.47s\n",
      "       872           1.2818            2.45s\n",
      "       873           1.2817            2.44s\n",
      "       874           1.2816            2.42s\n",
      "       875           1.2815            2.40s\n",
      "       876           1.2815            2.38s\n",
      "       877           1.2814            2.36s\n",
      "       878           1.2813            2.34s\n",
      "       879           1.2813            2.32s\n",
      "       880           1.2812            2.30s\n",
      "       881           1.2811            2.28s\n",
      "       882           1.2809            2.26s\n",
      "       883           1.2808            2.24s\n",
      "       884           1.2808            2.22s\n",
      "       885           1.2808            2.21s\n",
      "       886           1.2807            2.19s\n",
      "       887           1.2806            2.17s\n",
      "       888           1.2805            2.15s\n",
      "       889           1.2803            2.13s\n",
      "       890           1.2803            2.11s\n",
      "       891           1.2802            2.09s\n",
      "       892           1.2802            2.07s\n",
      "       893           1.2801            2.05s\n",
      "       894           1.2800            2.03s\n",
      "       895           1.2799            2.01s\n",
      "       896           1.2798            1.99s\n",
      "       897           1.2797            1.98s\n",
      "       898           1.2796            1.96s\n",
      "       899           1.2795            1.94s\n",
      "       900           1.2794            1.92s\n",
      "       901           1.2793            1.90s\n",
      "       902           1.2792            1.88s\n",
      "       903           1.2791            1.86s\n",
      "       904           1.2790            1.84s\n",
      "       905           1.2790            1.82s\n",
      "       906           1.2789            1.80s\n",
      "       907           1.2788            1.78s\n",
      "       908           1.2788            1.76s\n",
      "       909           1.2786            1.75s\n",
      "       910           1.2785            1.73s\n",
      "       911           1.2785            1.71s\n",
      "       912           1.2784            1.69s\n",
      "       913           1.2783            1.67s\n",
      "       914           1.2782            1.65s\n",
      "       915           1.2781            1.63s\n",
      "       916           1.2780            1.61s\n",
      "       917           1.2779            1.59s\n",
      "       918           1.2778            1.57s\n",
      "       919           1.2776            1.55s\n",
      "       920           1.2775            1.53s\n",
      "       921           1.2775            1.52s\n",
      "       922           1.2774            1.50s\n",
      "       923           1.2774            1.48s\n",
      "       924           1.2774            1.46s\n",
      "       925           1.2772            1.44s\n",
      "       926           1.2772            1.42s\n",
      "       927           1.2771            1.40s\n",
      "       928           1.2770            1.38s\n",
      "       929           1.2768            1.36s\n",
      "       930           1.2767            1.34s\n",
      "       931           1.2767            1.32s\n",
      "       932           1.2766            1.30s\n",
      "       933           1.2765            1.29s\n",
      "       934           1.2764            1.27s\n",
      "       935           1.2763            1.25s\n",
      "       936           1.2762            1.23s\n",
      "       937           1.2761            1.21s\n",
      "       938           1.2761            1.19s\n",
      "       939           1.2760            1.17s\n",
      "       940           1.2760            1.15s\n",
      "       941           1.2759            1.13s\n",
      "       942           1.2757            1.11s\n",
      "       943           1.2756            1.09s\n",
      "       944           1.2756            1.07s\n",
      "       945           1.2755            1.06s\n",
      "       946           1.2755            1.04s\n",
      "       947           1.2754            1.02s\n",
      "       948           1.2753            1.00s\n",
      "       949           1.2752            0.98s\n",
      "       950           1.2751            0.96s\n",
      "       951           1.2750            0.94s\n",
      "       952           1.2749            0.92s\n",
      "       953           1.2748            0.90s\n",
      "       954           1.2747            0.88s\n",
      "       955           1.2746            0.86s\n",
      "       956           1.2744            0.84s\n",
      "       957           1.2743            0.82s\n",
      "       958           1.2742            0.81s\n",
      "       959           1.2742            0.79s\n",
      "       960           1.2741            0.77s\n",
      "       961           1.2740            0.75s\n",
      "       962           1.2739            0.73s\n",
      "       963           1.2738            0.71s\n",
      "       964           1.2738            0.69s\n",
      "       965           1.2737            0.67s\n",
      "       966           1.2736            0.65s\n",
      "       967           1.2735            0.63s\n",
      "       968           1.2735            0.61s\n",
      "       969           1.2734            0.59s\n",
      "       970           1.2733            0.58s\n",
      "       971           1.2732            0.56s\n",
      "       972           1.2732            0.54s\n",
      "       973           1.2731            0.52s\n",
      "       974           1.2730            0.50s\n",
      "       975           1.2728            0.48s\n",
      "       976           1.2727            0.46s\n",
      "       977           1.2727            0.44s\n",
      "       978           1.2726            0.42s\n",
      "       979           1.2726            0.40s\n",
      "       980           1.2725            0.38s\n",
      "       981           1.2724            0.36s\n",
      "       982           1.2724            0.35s\n",
      "       983           1.2723            0.33s\n",
      "       984           1.2722            0.31s\n",
      "       985           1.2721            0.29s\n",
      "       986           1.2720            0.27s\n",
      "       987           1.2719            0.25s\n",
      "       988           1.2718            0.23s\n",
      "       989           1.2718            0.21s\n",
      "       990           1.2717            0.19s\n",
      "       991           1.2717            0.17s\n",
      "       992           1.2716            0.15s\n",
      "       993           1.2715            0.13s\n",
      "       994           1.2714            0.12s\n",
      "       995           1.2713            0.10s\n",
      "       996           1.2713            0.08s\n",
      "       997           1.2711            0.06s\n",
      "       998           1.2710            0.04s\n",
      "       999           1.2709            0.02s\n",
      "      1000           1.2708            0.00s\n",
      "      Iter       Train Loss   Remaining Time \n",
      "         1           1.3860           19.98s\n",
      "         2           1.3858           19.46s\n",
      "         3           1.3855           19.28s\n",
      "         4           1.3853           19.42s\n",
      "         5           1.3850           19.30s\n",
      "         6           1.3848           19.38s\n",
      "         7           1.3846           19.29s\n",
      "         8           1.3844           19.34s\n",
      "         9           1.3841           19.27s\n",
      "        10           1.3839           19.31s\n",
      "        11           1.3837           19.24s\n",
      "        12           1.3835           19.18s\n",
      "        13           1.3833           19.21s\n",
      "        14           1.3831           19.16s\n",
      "        15           1.3829           19.11s\n",
      "        16           1.3827           19.13s\n",
      "        17           1.3825           19.08s\n",
      "        18           1.3823           19.04s\n",
      "        19           1.3821           19.00s\n",
      "        20           1.3819           19.01s\n",
      "        21           1.3818           18.97s\n",
      "        22           1.3816           18.98s\n",
      "        23           1.3814           18.95s\n",
      "        24           1.3812           18.91s\n",
      "        25           1.3811           18.92s\n",
      "        26           1.3809           18.88s\n",
      "        27           1.3807           18.88s\n",
      "        28           1.3805           18.85s\n",
      "        29           1.3803           18.82s\n",
      "        30           1.3802           18.82s\n",
      "        31           1.3800           18.79s\n",
      "        32           1.3798           18.76s\n",
      "        33           1.3796           18.75s\n",
      "        34           1.3795           18.72s\n",
      "        35           1.3793           18.69s\n",
      "        36           1.3791           18.66s\n",
      "        37           1.3789           18.66s\n",
      "        38           1.3788           18.63s\n",
      "        39           1.3786           18.63s\n",
      "        40           1.3784           18.60s\n",
      "        41           1.3783           18.60s\n",
      "        42           1.3782           18.59s\n",
      "        43           1.3780           18.56s\n",
      "        44           1.3779           18.56s\n",
      "        45           1.3777           18.53s\n",
      "        46           1.3776           18.50s\n",
      "        47           1.3774           18.47s\n",
      "        48           1.3773           18.45s\n",
      "        49           1.3772           18.44s\n",
      "        50           1.3770           18.41s\n",
      "        51           1.3768           18.38s\n",
      "        52           1.3767           18.38s\n",
      "        53           1.3766           18.35s\n",
      "        54           1.3764           18.32s\n",
      "        55           1.3763           18.32s\n",
      "        56           1.3762           18.29s\n",
      "        57           1.3760           18.26s\n",
      "        58           1.3759           18.26s\n",
      "        59           1.3758           18.23s\n",
      "        60           1.3757           18.20s\n",
      "        61           1.3756           18.20s\n",
      "        62           1.3754           18.17s\n",
      "        63           1.3753           18.16s\n",
      "        64           1.3752           18.14s\n",
      "        65           1.3751           18.12s\n",
      "        66           1.3749           18.10s\n",
      "        67           1.3748           18.08s\n",
      "        68           1.3747           18.06s\n",
      "        69           1.3746           18.04s\n",
      "        70           1.3745           18.02s\n",
      "        71           1.3743           18.00s\n",
      "        72           1.3742           17.98s\n",
      "        73           1.3741           17.97s\n",
      "        74           1.3740           17.94s\n",
      "        75           1.3739           17.92s\n",
      "        76           1.3738           17.91s\n",
      "        77           1.3736           17.88s\n",
      "        78           1.3735           17.86s\n",
      "        79           1.3735           17.85s\n",
      "        80           1.3733           17.83s\n",
      "        81           1.3732           17.81s\n",
      "        82           1.3731           17.79s\n",
      "        83           1.3730           17.78s\n",
      "        84           1.3729           17.75s\n",
      "        85           1.3728           17.73s\n",
      "        86           1.3727           17.72s\n",
      "        87           1.3726           17.69s\n",
      "        88           1.3725           17.68s\n",
      "        89           1.3724           17.66s\n",
      "        90           1.3723           17.64s\n",
      "        91           1.3722           17.62s\n",
      "        92           1.3721           17.61s\n",
      "        93           1.3718           17.58s\n",
      "        94           1.3718           17.56s\n",
      "        95           1.3715           17.54s\n",
      "        96           1.3715           17.51s\n",
      "        97           1.3713           17.50s\n",
      "        98           1.3711           17.48s\n",
      "        99           1.3709           17.46s\n",
      "       100           1.3708           17.44s\n",
      "       101           1.3706           17.42s\n",
      "       102           1.3705           17.40s\n",
      "       103           1.3703           17.37s\n",
      "       104           1.3703           17.36s\n",
      "       105           1.3701           17.34s\n",
      "       106           1.3700           17.31s\n",
      "       107           1.3699           17.29s\n",
      "       108           1.3697           17.27s\n",
      "       109           1.3696           17.26s\n",
      "       110           1.3695           17.24s\n",
      "       111           1.3695           17.22s\n",
      "       112           1.3694           17.21s\n",
      "       113           1.3693           17.18s\n",
      "       114           1.3692           17.16s\n",
      "       115           1.3690           17.15s\n",
      "       116           1.3689           17.12s\n",
      "       117           1.3688           17.10s\n",
      "       118           1.3687           17.09s\n",
      "       119           1.3685           17.06s\n",
      "       120           1.3684           17.04s\n",
      "       121           1.3683           17.03s\n",
      "       122           1.3682           17.01s\n",
      "       123           1.3681           16.98s\n",
      "       124           1.3680           16.96s\n",
      "       125           1.3679           16.95s\n",
      "       126           1.3678           16.93s\n",
      "       127           1.3677           16.91s\n",
      "       128           1.3676           16.89s\n",
      "       129           1.3676           16.87s\n",
      "       130           1.3674           16.85s\n",
      "       131           1.3673           16.83s\n",
      "       132           1.3671           16.81s\n",
      "       133           1.3671           16.81s\n",
      "       134           1.3670           16.79s\n",
      "       135           1.3669           16.77s\n",
      "       136           1.3668           16.75s\n",
      "       137           1.3667           16.72s\n",
      "       138           1.3666           16.71s\n",
      "       139           1.3666           16.69s\n",
      "       140           1.3664           16.67s\n",
      "       141           1.3663           16.64s\n",
      "       142           1.3663           16.63s\n",
      "       143           1.3662           16.61s\n",
      "       144           1.3661           16.59s\n",
      "       145           1.3660           16.57s\n",
      "       146           1.3659           16.55s\n",
      "       147           1.3658           16.53s\n",
      "       148           1.3657           16.51s\n",
      "       149           1.3656           16.49s\n",
      "       150           1.3655           16.47s\n",
      "       151           1.3653           16.45s\n",
      "       152           1.3652           16.43s\n",
      "       153           1.3650           16.41s\n",
      "       154           1.3650           16.39s\n",
      "       155           1.3649           16.37s\n",
      "       156           1.3648           16.36s\n",
      "       157           1.3647           16.33s\n",
      "       158           1.3646           16.32s\n",
      "       159           1.3644           16.30s\n",
      "       160           1.3643           16.27s\n",
      "       161           1.3641           16.25s\n",
      "       162           1.3641           16.24s\n",
      "       163           1.3640           16.22s\n",
      "       164           1.3638           16.19s\n",
      "       165           1.3637           16.17s\n",
      "       166           1.3636           16.16s\n",
      "       167           1.3634           16.14s\n",
      "       168           1.3633           16.12s\n",
      "       169           1.3632           16.09s\n",
      "       170           1.3631           16.08s\n",
      "       171           1.3630           16.06s\n",
      "       172           1.3629           16.04s\n",
      "       173           1.3627           16.01s\n",
      "       174           1.3626           15.99s\n",
      "       175           1.3625           15.98s\n",
      "       176           1.3624           15.96s\n",
      "       177           1.3624           15.94s\n",
      "       178           1.3622           15.92s\n",
      "       179           1.3621           15.90s\n",
      "       180           1.3620           15.88s\n",
      "       181           1.3619           15.86s\n",
      "       182           1.3617           15.84s\n",
      "       183           1.3616           15.82s\n",
      "       184           1.3615           15.80s\n",
      "       185           1.3615           15.78s\n",
      "       186           1.3614           15.76s\n",
      "       187           1.3612           15.74s\n",
      "       188           1.3611           15.73s\n",
      "       189           1.3611           15.71s\n",
      "       190           1.3610           15.68s\n",
      "       191           1.3608           15.66s\n",
      "       192           1.3608           15.65s\n",
      "       193           1.3606           15.63s\n",
      "       194           1.3605           15.60s\n",
      "       195           1.3604           15.58s\n",
      "       196           1.3604           15.57s\n",
      "       197           1.3602           15.55s\n",
      "       198           1.3600           15.53s\n",
      "       199           1.3600           15.50s\n",
      "       200           1.3599           15.49s\n",
      "       201           1.3598           15.47s\n",
      "       202           1.3597           15.45s\n",
      "       203           1.3596           15.43s\n",
      "       204           1.3594           15.41s\n",
      "       205           1.3593           15.39s\n",
      "       206           1.3592           15.37s\n",
      "       207           1.3592           15.35s\n",
      "       208           1.3590           15.33s\n",
      "       209           1.3590           15.31s\n",
      "       210           1.3589           15.29s\n",
      "       211           1.3587           15.28s\n",
      "       212           1.3586           15.25s\n",
      "       213           1.3585           15.24s\n",
      "       214           1.3584           15.22s\n",
      "       215           1.3583           15.20s\n",
      "       216           1.3582           15.18s\n",
      "       217           1.3581           15.16s\n",
      "       218           1.3580           15.14s\n",
      "       219           1.3578           15.12s\n",
      "       220           1.3577           15.10s\n",
      "       221           1.3576           15.08s\n",
      "       222           1.3576           15.06s\n",
      "       223           1.3575           15.04s\n",
      "       224           1.3573           15.02s\n",
      "       225           1.3572           15.00s\n",
      "       226           1.3571           14.98s\n",
      "       227           1.3570           14.97s\n",
      "       228           1.3569           14.95s\n",
      "       229           1.3569           14.93s\n",
      "       230           1.3568           14.91s\n",
      "       231           1.3566           14.89s\n",
      "       232           1.3565           14.87s\n",
      "       233           1.3564           14.85s\n",
      "       234           1.3563           14.83s\n",
      "       235           1.3562           14.81s\n",
      "       236           1.3561           14.79s\n",
      "       237           1.3560           14.77s\n",
      "       238           1.3558           14.75s\n",
      "       239           1.3558           14.74s\n",
      "       240           1.3557           14.72s\n",
      "       241           1.3556           14.70s\n",
      "       242           1.3555           14.68s\n",
      "       243           1.3554           14.66s\n",
      "       244           1.3553           14.64s\n",
      "       245           1.3552           14.62s\n",
      "       246           1.3551           14.60s\n",
      "       247           1.3549           14.58s\n",
      "       248           1.3548           14.56s\n",
      "       249           1.3547           14.54s\n",
      "       250           1.3546           14.52s\n",
      "       251           1.3544           14.51s\n",
      "       252           1.3544           14.49s\n",
      "       253           1.3543           14.47s\n",
      "       254           1.3542           14.45s\n",
      "       255           1.3541           14.43s\n",
      "       256           1.3541           14.41s\n",
      "       257           1.3539           14.39s\n",
      "       258           1.3538           14.37s\n",
      "       259           1.3537           14.35s\n",
      "       260           1.3536           14.33s\n",
      "       261           1.3535           14.31s\n",
      "       262           1.3534           14.29s\n",
      "       263           1.3532           14.27s\n",
      "       264           1.3531           14.25s\n",
      "       265           1.3531           14.23s\n",
      "       266           1.3529           14.21s\n",
      "       267           1.3527           14.19s\n",
      "       268           1.3527           14.18s\n",
      "       269           1.3526           14.16s\n",
      "       270           1.3526           14.13s\n",
      "       271           1.3524           14.12s\n",
      "       272           1.3524           14.10s\n",
      "       273           1.3522           14.08s\n",
      "       274           1.3521           14.06s\n",
      "       275           1.3521           14.04s\n",
      "       276           1.3520           14.02s\n",
      "       277           1.3519           14.00s\n",
      "       278           1.3518           13.98s\n",
      "       279           1.3517           13.96s\n",
      "       280           1.3516           13.94s\n",
      "       281           1.3515           13.92s\n",
      "       282           1.3513           13.90s\n",
      "       283           1.3513           13.88s\n",
      "       284           1.3512           13.86s\n",
      "       285           1.3511           13.84s\n",
      "       286           1.3510           13.82s\n",
      "       287           1.3509           13.81s\n",
      "       288           1.3508           13.79s\n",
      "       289           1.3507           13.77s\n",
      "       290           1.3506           13.75s\n",
      "       291           1.3505           13.73s\n",
      "       292           1.3504           13.71s\n",
      "       293           1.3503           13.69s\n",
      "       294           1.3500           13.67s\n",
      "       295           1.3499           13.65s\n",
      "       296           1.3498           13.63s\n",
      "       297           1.3497           13.61s\n",
      "       298           1.3496           13.59s\n",
      "       299           1.3495           13.57s\n",
      "       300           1.3494           13.55s\n",
      "       301           1.3493           13.53s\n",
      "       302           1.3492           13.52s\n",
      "       303           1.3492           13.50s\n",
      "       304           1.3489           13.48s\n",
      "       305           1.3488           13.46s\n",
      "       306           1.3487           13.44s\n",
      "       307           1.3486           13.42s\n",
      "       308           1.3485           13.40s\n",
      "       309           1.3484           13.38s\n",
      "       310           1.3482           13.36s\n",
      "       311           1.3480           13.35s\n",
      "       312           1.3479           13.33s\n",
      "       313           1.3478           13.31s\n",
      "       314           1.3476           13.29s\n",
      "       315           1.3476           13.27s\n",
      "       316           1.3475           13.25s\n",
      "       317           1.3474           13.23s\n",
      "       318           1.3473           13.21s\n",
      "       319           1.3471           13.19s\n",
      "       320           1.3470           13.17s\n",
      "       321           1.3469           13.15s\n",
      "       322           1.3469           13.13s\n",
      "       323           1.3468           13.11s\n",
      "       324           1.3465           13.09s\n",
      "       325           1.3463           13.07s\n",
      "       326           1.3462           13.06s\n",
      "       327           1.3461           13.04s\n",
      "       328           1.3460           13.02s\n",
      "       329           1.3459           13.00s\n",
      "       330           1.3458           12.98s\n",
      "       331           1.3458           12.96s\n",
      "       332           1.3457           12.94s\n",
      "       333           1.3456           12.92s\n",
      "       334           1.3453           12.90s\n",
      "       335           1.3452           12.88s\n",
      "       336           1.3451           12.86s\n",
      "       337           1.3450           12.84s\n",
      "       338           1.3449           12.82s\n",
      "       339           1.3448           12.80s\n",
      "       340           1.3446           12.78s\n",
      "       341           1.3445           12.76s\n",
      "       342           1.3445           12.75s\n",
      "       343           1.3444           12.73s\n",
      "       344           1.3443           12.71s\n",
      "       345           1.3441           12.69s\n",
      "       346           1.3440           12.67s\n",
      "       347           1.3439           12.65s\n",
      "       348           1.3439           12.63s\n",
      "       349           1.3437           12.61s\n",
      "       350           1.3435           12.59s\n",
      "       351           1.3434           12.57s\n",
      "       352           1.3432           12.55s\n",
      "       353           1.3431           12.53s\n",
      "       354           1.3431           12.51s\n",
      "       355           1.3430           12.49s\n",
      "       356           1.3429           12.47s\n",
      "       357           1.3426           12.45s\n",
      "       358           1.3426           12.43s\n",
      "       359           1.3425           12.41s\n",
      "       360           1.3423           12.39s\n",
      "       361           1.3423           12.38s\n",
      "       362           1.3421           12.36s\n",
      "       363           1.3420           12.34s\n",
      "       364           1.3420           12.32s\n",
      "       365           1.3418           12.30s\n",
      "       366           1.3417           12.28s\n",
      "       367           1.3416           12.26s\n",
      "       368           1.3416           12.24s\n",
      "       369           1.3415           12.22s\n",
      "       370           1.3414           12.20s\n",
      "       371           1.3413           12.18s\n",
      "       372           1.3412           12.16s\n",
      "       373           1.3411           12.14s\n",
      "       374           1.3410           12.12s\n",
      "       375           1.3409           12.11s\n",
      "       376           1.3408           12.09s\n",
      "       377           1.3406           12.07s\n",
      "       378           1.3405           12.05s\n",
      "       379           1.3404           12.03s\n",
      "       380           1.3403           12.01s\n",
      "       381           1.3402           11.99s\n",
      "       382           1.3401           11.97s\n",
      "       383           1.3401           11.95s\n",
      "       384           1.3399           11.93s\n",
      "       385           1.3398           11.91s\n",
      "       386           1.3397           11.89s\n",
      "       387           1.3396           11.87s\n",
      "       388           1.3396           11.85s\n",
      "       389           1.3395           11.83s\n",
      "       390           1.3394           11.82s\n",
      "       391           1.3393           11.80s\n",
      "       392           1.3393           11.78s\n",
      "       393           1.3391           11.76s\n",
      "       394           1.3390           11.74s\n",
      "       395           1.3389           11.72s\n",
      "       396           1.3388           11.70s\n",
      "       397           1.3387           11.68s\n",
      "       398           1.3386           11.66s\n",
      "       399           1.3385           11.64s\n",
      "       400           1.3385           11.62s\n",
      "       401           1.3382           11.60s\n",
      "       402           1.3381           11.58s\n",
      "       403           1.3379           11.57s\n",
      "       404           1.3378           11.55s\n",
      "       405           1.3378           11.53s\n",
      "       406           1.3377           11.51s\n",
      "       407           1.3377           11.49s\n",
      "       408           1.3375           11.47s\n",
      "       409           1.3375           11.45s\n",
      "       410           1.3374           11.43s\n",
      "       411           1.3373           11.41s\n",
      "       412           1.3372           11.39s\n",
      "       413           1.3370           11.37s\n",
      "       414           1.3369           11.35s\n",
      "       415           1.3368           11.33s\n",
      "       416           1.3367           11.31s\n",
      "       417           1.3366           11.29s\n",
      "       418           1.3364           11.27s\n",
      "       419           1.3363           11.25s\n",
      "       420           1.3361           11.23s\n",
      "       421           1.3360           11.21s\n",
      "       422           1.3359           11.19s\n",
      "       423           1.3358           11.18s\n",
      "       424           1.3358           11.16s\n",
      "       425           1.3357           11.14s\n",
      "       426           1.3355           11.12s\n",
      "       427           1.3354           11.10s\n",
      "       428           1.3353           11.08s\n",
      "       429           1.3353           11.06s\n",
      "       430           1.3352           11.04s\n",
      "       431           1.3351           11.02s\n",
      "       432           1.3350           11.00s\n",
      "       433           1.3349           10.98s\n",
      "       434           1.3348           10.96s\n",
      "       435           1.3347           10.94s\n",
      "       436           1.3346           10.92s\n",
      "       437           1.3345           10.90s\n",
      "       438           1.3344           10.88s\n",
      "       439           1.3344           10.86s\n",
      "       440           1.3342           10.85s\n",
      "       441           1.3340           10.83s\n",
      "       442           1.3339           10.81s\n",
      "       443           1.3338           10.79s\n",
      "       444           1.3338           10.77s\n",
      "       445           1.3337           10.75s\n",
      "       446           1.3337           10.73s\n",
      "       447           1.3335           10.71s\n",
      "       448           1.3334           10.69s\n",
      "       449           1.3333           10.68s\n",
      "       450           1.3333           10.66s\n",
      "       451           1.3332           10.64s\n",
      "       452           1.3331           10.62s\n",
      "       453           1.3329           10.60s\n",
      "       454           1.3329           10.58s\n",
      "       455           1.3328           10.56s\n",
      "       456           1.3327           10.54s\n",
      "       457           1.3326           10.52s\n",
      "       458           1.3326           10.50s\n",
      "       459           1.3325           10.48s\n",
      "       460           1.3323           10.46s\n",
      "       461           1.3322           10.44s\n",
      "       462           1.3322           10.42s\n",
      "       463           1.3321           10.40s\n",
      "       464           1.3320           10.39s\n",
      "       465           1.3319           10.37s\n",
      "       466           1.3318           10.35s\n",
      "       467           1.3317           10.33s\n",
      "       468           1.3315           10.31s\n",
      "       469           1.3314           10.29s\n",
      "       470           1.3314           10.27s\n",
      "       471           1.3313           10.25s\n",
      "       472           1.3312           10.23s\n",
      "       473           1.3312           10.21s\n",
      "       474           1.3311           10.19s\n",
      "       475           1.3310           10.17s\n",
      "       476           1.3309           10.15s\n",
      "       477           1.3308           10.13s\n",
      "       478           1.3306           10.12s\n",
      "       479           1.3304           10.10s\n",
      "       480           1.3303           10.08s\n",
      "       481           1.3302           10.06s\n",
      "       482           1.3302           10.04s\n",
      "       483           1.3299           10.02s\n",
      "       484           1.3299           10.00s\n",
      "       485           1.3298            9.98s\n",
      "       486           1.3297            9.96s\n",
      "       487           1.3296            9.94s\n",
      "       488           1.3294            9.92s\n",
      "       489           1.3294            9.90s\n",
      "       490           1.3293            9.88s\n",
      "       491           1.3292            9.86s\n",
      "       492           1.3291            9.85s\n",
      "       493           1.3290            9.83s\n",
      "       494           1.3289            9.81s\n",
      "       495           1.3287            9.79s\n",
      "       496           1.3286            9.77s\n",
      "       497           1.3285            9.75s\n",
      "       498           1.3285            9.73s\n",
      "       499           1.3284            9.71s\n",
      "       500           1.3282            9.69s\n",
      "       501           1.3281            9.67s\n",
      "       502           1.3279            9.65s\n",
      "       503           1.3279            9.63s\n",
      "       504           1.3279            9.61s\n",
      "       505           1.3278            9.59s\n",
      "       506           1.3278            9.57s\n",
      "       507           1.3277            9.55s\n",
      "       508           1.3276            9.54s\n",
      "       509           1.3274            9.52s\n",
      "       510           1.3273            9.50s\n",
      "       511           1.3272            9.48s\n",
      "       512           1.3270            9.46s\n",
      "       513           1.3269            9.44s\n",
      "       514           1.3268            9.42s\n",
      "       515           1.3267            9.40s\n",
      "       516           1.3266            9.38s\n",
      "       517           1.3264            9.36s\n",
      "       518           1.3264            9.34s\n",
      "       519           1.3263            9.32s\n",
      "       520           1.3262            9.30s\n",
      "       521           1.3262            9.28s\n",
      "       522           1.3261            9.26s\n",
      "       523           1.3261            9.24s\n",
      "       524           1.3259            9.22s\n",
      "       525           1.3258            9.21s\n",
      "       526           1.3257            9.19s\n",
      "       527           1.3257            9.17s\n",
      "       528           1.3256            9.15s\n",
      "       529           1.3255            9.13s\n",
      "       530           1.3254            9.11s\n",
      "       531           1.3253            9.09s\n",
      "       532           1.3251            9.07s\n",
      "       533           1.3250            9.05s\n",
      "       534           1.3250            9.03s\n",
      "       535           1.3249            9.01s\n",
      "       536           1.3248            8.99s\n",
      "       537           1.3246            8.97s\n",
      "       538           1.3246            8.95s\n",
      "       539           1.3245            8.93s\n",
      "       540           1.3244            8.91s\n",
      "       541           1.3243            8.90s\n",
      "       542           1.3243            8.88s\n",
      "       543           1.3242            8.86s\n",
      "       544           1.3241            8.84s\n",
      "       545           1.3239            8.82s\n",
      "       546           1.3238            8.80s\n",
      "       547           1.3238            8.78s\n",
      "       548           1.3236            8.76s\n",
      "       549           1.3235            8.74s\n",
      "       550           1.3234            8.72s\n",
      "       551           1.3232            8.70s\n",
      "       552           1.3232            8.68s\n",
      "       553           1.3232            8.66s\n",
      "       554           1.3231            8.64s\n",
      "       555           1.3229            8.62s\n",
      "       556           1.3228            8.61s\n",
      "       557           1.3228            8.59s\n",
      "       558           1.3227            8.57s\n",
      "       559           1.3226            8.55s\n",
      "       560           1.3224            8.53s\n",
      "       561           1.3223            8.51s\n",
      "       562           1.3221            8.49s\n",
      "       563           1.3221            8.47s\n",
      "       564           1.3220            8.45s\n",
      "       565           1.3219            8.43s\n",
      "       566           1.3217            8.41s\n",
      "       567           1.3215            8.39s\n",
      "       568           1.3214            8.37s\n",
      "       569           1.3213            8.35s\n",
      "       570           1.3212            8.33s\n",
      "       571           1.3210            8.31s\n",
      "       572           1.3210            8.29s\n",
      "       573           1.3209            8.27s\n",
      "       574           1.3207            8.26s\n",
      "       575           1.3206            8.24s\n",
      "       576           1.3205            8.22s\n",
      "       577           1.3203            8.20s\n",
      "       578           1.3203            8.18s\n",
      "       579           1.3202            8.16s\n",
      "       580           1.3202            8.14s\n",
      "       581           1.3201            8.12s\n",
      "       582           1.3200            8.10s\n",
      "       583           1.3199            8.08s\n",
      "       584           1.3197            8.06s\n",
      "       585           1.3196            8.04s\n",
      "       586           1.3195            8.02s\n",
      "       587           1.3195            8.00s\n",
      "       588           1.3194            7.98s\n",
      "       589           1.3194            7.97s\n",
      "       590           1.3193            7.95s\n",
      "       591           1.3192            7.93s\n",
      "       592           1.3191            7.91s\n",
      "       593           1.3190            7.89s\n",
      "       594           1.3188            7.87s\n",
      "       595           1.3187            7.85s\n",
      "       596           1.3186            7.83s\n",
      "       597           1.3186            7.81s\n",
      "       598           1.3185            7.79s\n",
      "       599           1.3184            7.77s\n",
      "       600           1.3183            7.75s\n",
      "       601           1.3183            7.73s\n",
      "       602           1.3182            7.71s\n",
      "       603           1.3181            7.69s\n",
      "       604           1.3181            7.67s\n",
      "       605           1.3180            7.66s\n",
      "       606           1.3178            7.64s\n",
      "       607           1.3177            7.62s\n",
      "       608           1.3175            7.60s\n",
      "       609           1.3174            7.58s\n",
      "       610           1.3172            7.56s\n",
      "       611           1.3171            7.54s\n",
      "       612           1.3170            7.52s\n",
      "       613           1.3169            7.50s\n",
      "       614           1.3167            7.48s\n",
      "       615           1.3166            7.46s\n",
      "       616           1.3166            7.44s\n",
      "       617           1.3165            7.42s\n",
      "       618           1.3164            7.40s\n",
      "       619           1.3164            7.38s\n",
      "       620           1.3164            7.36s\n",
      "       621           1.3163            7.34s\n",
      "       622           1.3162            7.32s\n",
      "       623           1.3161            7.31s\n",
      "       624           1.3160            7.29s\n",
      "       625           1.3158            7.27s\n",
      "       626           1.3157            7.25s\n",
      "       627           1.3156            7.23s\n",
      "       628           1.3155            7.21s\n",
      "       629           1.3155            7.19s\n",
      "       630           1.3155            7.17s\n",
      "       631           1.3154            7.15s\n",
      "       632           1.3153            7.13s\n",
      "       633           1.3152            7.11s\n",
      "       634           1.3150            7.09s\n",
      "       635           1.3149            7.07s\n",
      "       636           1.3149            7.05s\n",
      "       637           1.3148            7.04s\n",
      "       638           1.3147            7.02s\n",
      "       639           1.3145            7.00s\n",
      "       640           1.3144            6.98s\n",
      "       641           1.3144            6.96s\n",
      "       642           1.3143            6.94s\n",
      "       643           1.3143            6.92s\n",
      "       644           1.3142            6.90s\n",
      "       645           1.3142            6.88s\n",
      "       646           1.3141            6.86s\n",
      "       647           1.3139            6.84s\n",
      "       648           1.3138            6.82s\n",
      "       649           1.3137            6.80s\n",
      "       650           1.3135            6.78s\n",
      "       651           1.3134            6.76s\n",
      "       652           1.3133            6.74s\n",
      "       653           1.3132            6.73s\n",
      "       654           1.3132            6.71s\n",
      "       655           1.3130            6.69s\n",
      "       656           1.3128            6.67s\n",
      "       657           1.3128            6.65s\n",
      "       658           1.3127            6.63s\n",
      "       659           1.3126            6.61s\n",
      "       660           1.3124            6.59s\n",
      "       661           1.3124            6.57s\n",
      "       662           1.3122            6.55s\n",
      "       663           1.3121            6.53s\n",
      "       664           1.3120            6.51s\n",
      "       665           1.3119            6.49s\n",
      "       666           1.3118            6.47s\n",
      "       667           1.3118            6.45s\n",
      "       668           1.3116            6.43s\n",
      "       669           1.3115            6.42s\n",
      "       670           1.3113            6.40s\n",
      "       671           1.3113            6.38s\n",
      "       672           1.3112            6.36s\n",
      "       673           1.3111            6.34s\n",
      "       674           1.3109            6.32s\n",
      "       675           1.3109            6.30s\n",
      "       676           1.3107            6.28s\n",
      "       677           1.3106            6.26s\n",
      "       678           1.3105            6.24s\n",
      "       679           1.3104            6.22s\n",
      "       680           1.3103            6.20s\n",
      "       681           1.3103            6.18s\n",
      "       682           1.3102            6.16s\n",
      "       683           1.3101            6.14s\n",
      "       684           1.3099            6.13s\n",
      "       685           1.3098            6.11s\n",
      "       686           1.3096            6.09s\n",
      "       687           1.3095            6.07s\n",
      "       688           1.3094            6.05s\n",
      "       689           1.3093            6.03s\n",
      "       690           1.3091            6.01s\n",
      "       691           1.3091            5.99s\n",
      "       692           1.3091            5.97s\n",
      "       693           1.3090            5.95s\n",
      "       694           1.3088            5.93s\n",
      "       695           1.3087            5.91s\n",
      "       696           1.3086            5.89s\n",
      "       697           1.3085            5.87s\n",
      "       698           1.3085            5.85s\n",
      "       699           1.3084            5.83s\n",
      "       700           1.3083            5.82s\n",
      "       701           1.3081            5.80s\n",
      "       702           1.3080            5.78s\n",
      "       703           1.3079            5.76s\n",
      "       704           1.3078            5.74s\n",
      "       705           1.3077            5.72s\n",
      "       706           1.3076            5.70s\n",
      "       707           1.3074            5.68s\n",
      "       708           1.3072            5.66s\n",
      "       709           1.3072            5.64s\n",
      "       710           1.3071            5.62s\n",
      "       711           1.3069            5.60s\n",
      "       712           1.3067            5.58s\n",
      "       713           1.3066            5.56s\n",
      "       714           1.3065            5.55s\n",
      "       715           1.3065            5.53s\n",
      "       716           1.3063            5.51s\n",
      "       717           1.3062            5.49s\n",
      "       718           1.3061            5.47s\n",
      "       719           1.3061            5.45s\n",
      "       720           1.3059            5.43s\n",
      "       721           1.3059            5.41s\n",
      "       722           1.3057            5.39s\n",
      "       723           1.3057            5.37s\n",
      "       724           1.3055            5.35s\n",
      "       725           1.3054            5.34s\n",
      "       726           1.3054            5.32s\n",
      "       727           1.3053            5.30s\n",
      "       728           1.3052            5.28s\n",
      "       729           1.3052            5.26s\n",
      "       730           1.3050            5.24s\n",
      "       731           1.3049            5.22s\n",
      "       732           1.3047            5.20s\n",
      "       733           1.3046            5.18s\n",
      "       734           1.3046            5.16s\n",
      "       735           1.3045            5.15s\n",
      "       736           1.3044            5.13s\n",
      "       737           1.3043            5.11s\n",
      "       738           1.3041            5.09s\n",
      "       739           1.3040            5.07s\n",
      "       740           1.3038            5.06s\n",
      "       741           1.3037            5.04s\n",
      "       742           1.3035            5.02s\n",
      "       743           1.3034            5.00s\n",
      "       744           1.3033            4.98s\n",
      "       745           1.3031            4.96s\n",
      "       746           1.3031            4.94s\n",
      "       747           1.3030            4.92s\n",
      "       748           1.3029            4.90s\n",
      "       749           1.3028            4.88s\n",
      "       750           1.3027            4.86s\n",
      "       751           1.3027            4.84s\n",
      "       752           1.3026            4.82s\n",
      "       753           1.3026            4.80s\n",
      "       754           1.3025            4.78s\n",
      "       755           1.3024            4.77s\n",
      "       756           1.3023            4.75s\n",
      "       757           1.3022            4.73s\n",
      "       758           1.3021            4.71s\n",
      "       759           1.3020            4.69s\n",
      "       760           1.3019            4.67s\n",
      "       761           1.3018            4.65s\n",
      "       762           1.3016            4.63s\n",
      "       763           1.3016            4.61s\n",
      "       764           1.3014            4.59s\n",
      "       765           1.3012            4.57s\n",
      "       766           1.3011            4.55s\n",
      "       767           1.3010            4.53s\n",
      "       768           1.3009            4.51s\n",
      "       769           1.3009            4.49s\n",
      "       770           1.3008            4.47s\n",
      "       771           1.3008            4.45s\n",
      "       772           1.3007            4.43s\n",
      "       773           1.3006            4.41s\n",
      "       774           1.3005            4.40s\n",
      "       775           1.3004            4.38s\n",
      "       776           1.3003            4.36s\n",
      "       777           1.3001            4.34s\n",
      "       778           1.3001            4.32s\n",
      "       779           1.3000            4.30s\n",
      "       780           1.2999            4.28s\n",
      "       781           1.2997            4.26s\n",
      "       782           1.2996            4.24s\n",
      "       783           1.2995            4.22s\n",
      "       784           1.2995            4.20s\n",
      "       785           1.2994            4.18s\n",
      "       786           1.2993            4.16s\n",
      "       787           1.2992            4.14s\n",
      "       788           1.2991            4.12s\n",
      "       789           1.2990            4.10s\n",
      "       790           1.2989            4.08s\n",
      "       791           1.2988            4.06s\n",
      "       792           1.2986            4.04s\n",
      "       793           1.2985            4.03s\n",
      "       794           1.2984            4.01s\n",
      "       795           1.2983            3.99s\n",
      "       796           1.2981            3.97s\n",
      "       797           1.2980            3.95s\n",
      "       798           1.2979            3.93s\n",
      "       799           1.2978            3.91s\n",
      "       800           1.2978            3.89s\n",
      "       801           1.2977            3.87s\n",
      "       802           1.2977            3.85s\n",
      "       803           1.2975            3.83s\n",
      "       804           1.2974            3.81s\n",
      "       805           1.2973            3.79s\n",
      "       806           1.2971            3.77s\n",
      "       807           1.2969            3.75s\n",
      "       808           1.2969            3.73s\n",
      "       809           1.2968            3.71s\n",
      "       810           1.2967            3.69s\n",
      "       811           1.2966            3.68s\n",
      "       812           1.2964            3.66s\n",
      "       813           1.2963            3.64s\n",
      "       814           1.2963            3.62s\n",
      "       815           1.2962            3.60s\n",
      "       816           1.2962            3.58s\n",
      "       817           1.2960            3.56s\n",
      "       818           1.2959            3.54s\n",
      "       819           1.2957            3.52s\n",
      "       820           1.2956            3.50s\n",
      "       821           1.2955            3.48s\n",
      "       822           1.2953            3.46s\n",
      "       823           1.2952            3.44s\n",
      "       824           1.2951            3.42s\n",
      "       825           1.2949            3.40s\n",
      "       826           1.2948            3.38s\n",
      "       827           1.2947            3.36s\n",
      "       828           1.2946            3.34s\n",
      "       829           1.2945            3.32s\n",
      "       830           1.2943            3.31s\n",
      "       831           1.2941            3.29s\n",
      "       832           1.2941            3.27s\n",
      "       833           1.2940            3.25s\n",
      "       834           1.2939            3.23s\n",
      "       835           1.2939            3.21s\n",
      "       836           1.2937            3.19s\n",
      "       837           1.2936            3.17s\n",
      "       838           1.2935            3.15s\n",
      "       839           1.2933            3.13s\n",
      "       840           1.2932            3.11s\n",
      "       841           1.2932            3.09s\n",
      "       842           1.2930            3.07s\n",
      "       843           1.2929            3.05s\n",
      "       844           1.2928            3.03s\n",
      "       845           1.2926            3.01s\n",
      "       846           1.2926            3.00s\n",
      "       847           1.2926            2.98s\n",
      "       848           1.2925            2.96s\n",
      "       849           1.2923            2.94s\n",
      "       850           1.2923            2.92s\n",
      "       851           1.2922            2.90s\n",
      "       852           1.2921            2.88s\n",
      "       853           1.2921            2.86s\n",
      "       854           1.2921            2.84s\n",
      "       855           1.2919            2.82s\n",
      "       856           1.2918            2.80s\n",
      "       857           1.2917            2.78s\n",
      "       858           1.2916            2.76s\n",
      "       859           1.2915            2.74s\n",
      "       860           1.2914            2.72s\n",
      "       861           1.2913            2.70s\n",
      "       862           1.2912            2.68s\n",
      "       863           1.2911            2.66s\n",
      "       864           1.2909            2.64s\n",
      "       865           1.2908            2.63s\n",
      "       866           1.2908            2.61s\n",
      "       867           1.2906            2.59s\n",
      "       868           1.2906            2.57s\n",
      "       869           1.2905            2.55s\n",
      "       870           1.2905            2.53s\n",
      "       871           1.2904            2.51s\n",
      "       872           1.2903            2.49s\n",
      "       873           1.2901            2.47s\n",
      "       874           1.2900            2.45s\n",
      "       875           1.2899            2.43s\n",
      "       876           1.2899            2.41s\n",
      "       877           1.2897            2.39s\n",
      "       878           1.2896            2.37s\n",
      "       879           1.2896            2.35s\n",
      "       880           1.2895            2.33s\n",
      "       881           1.2893            2.31s\n",
      "       882           1.2893            2.29s\n",
      "       883           1.2893            2.28s\n",
      "       884           1.2891            2.26s\n",
      "       885           1.2891            2.24s\n",
      "       886           1.2890            2.22s\n",
      "       887           1.2889            2.20s\n",
      "       888           1.2887            2.18s\n",
      "       889           1.2887            2.16s\n",
      "       890           1.2885            2.14s\n",
      "       891           1.2884            2.12s\n",
      "       892           1.2883            2.10s\n",
      "       893           1.2882            2.08s\n",
      "       894           1.2881            2.06s\n",
      "       895           1.2881            2.04s\n",
      "       896           1.2880            2.02s\n",
      "       897           1.2879            2.00s\n",
      "       898           1.2879            1.98s\n",
      "       899           1.2878            1.96s\n",
      "       900           1.2878            1.94s\n",
      "       901           1.2877            1.92s\n",
      "       902           1.2875            1.91s\n",
      "       903           1.2874            1.89s\n",
      "       904           1.2873            1.87s\n",
      "       905           1.2872            1.85s\n",
      "       906           1.2872            1.83s\n",
      "       907           1.2871            1.81s\n",
      "       908           1.2870            1.79s\n",
      "       909           1.2869            1.77s\n",
      "       910           1.2869            1.75s\n",
      "       911           1.2868            1.73s\n",
      "       912           1.2866            1.71s\n",
      "       913           1.2865            1.69s\n",
      "       914           1.2864            1.67s\n",
      "       915           1.2863            1.65s\n",
      "       916           1.2862            1.63s\n",
      "       917           1.2860            1.61s\n",
      "       918           1.2859            1.59s\n",
      "       919           1.2859            1.57s\n",
      "       920           1.2857            1.56s\n",
      "       921           1.2856            1.54s\n",
      "       922           1.2855            1.52s\n",
      "       923           1.2854            1.50s\n",
      "       924           1.2853            1.48s\n",
      "       925           1.2853            1.46s\n",
      "       926           1.2852            1.44s\n",
      "       927           1.2851            1.42s\n",
      "       928           1.2850            1.40s\n",
      "       929           1.2849            1.38s\n",
      "       930           1.2848            1.36s\n",
      "       931           1.2846            1.34s\n",
      "       932           1.2845            1.32s\n",
      "       933           1.2844            1.30s\n",
      "       934           1.2843            1.28s\n",
      "       935           1.2843            1.26s\n",
      "       936           1.2842            1.24s\n",
      "       937           1.2841            1.22s\n",
      "       938           1.2841            1.21s\n",
      "       939           1.2840            1.19s\n",
      "       940           1.2838            1.17s\n",
      "       941           1.2837            1.15s\n",
      "       942           1.2836            1.13s\n",
      "       943           1.2835            1.11s\n",
      "       944           1.2834            1.09s\n",
      "       945           1.2833            1.07s\n",
      "       946           1.2832            1.05s\n",
      "       947           1.2831            1.03s\n",
      "       948           1.2829            1.01s\n",
      "       949           1.2828            0.99s\n",
      "       950           1.2827            0.97s\n",
      "       951           1.2826            0.95s\n",
      "       952           1.2825            0.93s\n",
      "       953           1.2824            0.91s\n",
      "       954           1.2823            0.89s\n",
      "       955           1.2822            0.87s\n",
      "       956           1.2821            0.86s\n",
      "       957           1.2820            0.84s\n",
      "       958           1.2820            0.82s\n",
      "       959           1.2819            0.80s\n",
      "       960           1.2817            0.78s\n",
      "       961           1.2815            0.76s\n",
      "       962           1.2814            0.74s\n",
      "       963           1.2813            0.72s\n",
      "       964           1.2812            0.70s\n",
      "       965           1.2810            0.68s\n",
      "       966           1.2810            0.66s\n",
      "       967           1.2809            0.64s\n",
      "       968           1.2809            0.62s\n",
      "       969           1.2807            0.60s\n",
      "       970           1.2807            0.58s\n",
      "       971           1.2805            0.56s\n",
      "       972           1.2804            0.54s\n",
      "       973           1.2803            0.52s\n",
      "       974           1.2803            0.51s\n",
      "       975           1.2802            0.49s\n",
      "       976           1.2801            0.47s\n",
      "       977           1.2799            0.45s\n",
      "       978           1.2798            0.43s\n",
      "       979           1.2797            0.41s\n",
      "       980           1.2795            0.39s\n",
      "       981           1.2794            0.37s\n",
      "       982           1.2793            0.35s\n",
      "       983           1.2791            0.33s\n",
      "       984           1.2790            0.31s\n",
      "       985           1.2788            0.29s\n",
      "       986           1.2787            0.27s\n",
      "       987           1.2787            0.25s\n",
      "       988           1.2785            0.23s\n",
      "       989           1.2784            0.21s\n",
      "       990           1.2783            0.19s\n",
      "       991           1.2781            0.17s\n",
      "       992           1.2780            0.16s\n",
      "       993           1.2779            0.14s\n",
      "       994           1.2778            0.12s\n",
      "       995           1.2776            0.10s\n",
      "       996           1.2775            0.08s\n",
      "       997           1.2774            0.06s\n",
      "       998           1.2772            0.04s\n",
      "       999           1.2771            0.02s\n",
      "      1000           1.2770            0.00s\n",
      "      Iter       Train Loss   Remaining Time \n",
      "         1           1.3860           19.98s\n",
      "         2           1.3858           19.46s\n",
      "         3           1.3855           19.28s\n",
      "         4           1.3853           19.42s\n",
      "         5           1.3851           19.30s\n",
      "         6           1.3849           19.22s\n",
      "         7           1.3846           19.15s\n",
      "         8           1.3843           19.10s\n",
      "         9           1.3841           19.05s\n",
      "        10           1.3838           19.01s\n",
      "        11           1.3836           19.06s\n",
      "        12           1.3833           19.02s\n",
      "        13           1.3831           18.98s\n",
      "        14           1.3828           18.95s\n",
      "        15           1.3826           18.98s\n",
      "        16           1.3823           18.94s\n",
      "        17           1.3821           18.91s\n",
      "        18           1.3818           18.88s\n",
      "        19           1.3816           18.90s\n",
      "        20           1.3814           18.87s\n",
      "        21           1.3812           18.93s\n",
      "        22           1.3809           18.98s\n",
      "        23           1.3807           18.95s\n",
      "        24           1.3805           18.91s\n",
      "        25           1.3803           18.92s\n",
      "        26           1.3800           18.88s\n",
      "        27           1.3799           18.85s\n",
      "        28           1.3797           18.82s\n",
      "        29           1.3794           18.78s\n",
      "        30           1.3792           18.75s\n",
      "        31           1.3790           18.75s\n",
      "        32           1.3788           18.76s\n",
      "        33           1.3785           18.72s\n",
      "        34           1.3783           18.69s\n",
      "        35           1.3781           18.69s\n",
      "        36           1.3779           18.66s\n",
      "        37           1.3777           18.64s\n",
      "        38           1.3775           18.61s\n",
      "        39           1.3773           18.60s\n",
      "        40           1.3771           18.58s\n",
      "        41           1.3769           18.55s\n",
      "        42           1.3767           18.52s\n",
      "        43           1.3765           18.52s\n",
      "        44           1.3763           18.49s\n",
      "        45           1.3761           18.46s\n",
      "        46           1.3760           18.44s\n",
      "        47           1.3758           18.41s\n",
      "        48           1.3756           18.41s\n",
      "        49           1.3755           18.38s\n",
      "        50           1.3752           18.35s\n",
      "        51           1.3751           18.33s\n",
      "        52           1.3749           18.30s\n",
      "        53           1.3748           18.28s\n",
      "        54           1.3746           18.27s\n",
      "        55           1.3744           18.25s\n",
      "        56           1.3742           18.24s\n",
      "        57           1.3741           18.21s\n",
      "        58           1.3739           18.19s\n",
      "        59           1.3738           18.18s\n",
      "        60           1.3736           18.16s\n",
      "        61           1.3734           18.13s\n",
      "        62           1.3733           18.11s\n",
      "        63           1.3732           18.09s\n",
      "        64           1.3730           18.06s\n",
      "        65           1.3728           18.05s\n",
      "        66           1.3727           18.03s\n",
      "        67           1.3725           18.02s\n",
      "        68           1.3723           18.00s\n",
      "        69           1.3722           17.97s\n",
      "        70           1.3721           17.95s\n",
      "        71           1.3719           17.94s\n",
      "        72           1.3718           17.92s\n",
      "        73           1.3716           17.89s\n",
      "        74           1.3715           17.88s\n",
      "        75           1.3713           17.86s\n",
      "        76           1.3712           17.85s\n",
      "        77           1.3711           17.85s\n",
      "        78           1.3709           17.84s\n",
      "        79           1.3708           17.81s\n",
      "        80           1.3706           17.80s\n",
      "        81           1.3705           17.78s\n",
      "        82           1.3704           17.77s\n",
      "        83           1.3703           17.74s\n",
      "        84           1.3701           17.72s\n",
      "        85           1.3700           17.70s\n",
      "        86           1.3699           17.68s\n",
      "        87           1.3698           17.66s\n",
      "        88           1.3696           17.65s\n",
      "        89           1.3695           17.63s\n",
      "        90           1.3694           17.61s\n",
      "        91           1.3693           17.59s\n",
      "        92           1.3691           17.57s\n",
      "        93           1.3690           17.55s\n",
      "        94           1.3690           17.53s\n",
      "        95           1.3689           17.51s\n",
      "        96           1.3688           17.49s\n",
      "        97           1.3687           17.47s\n",
      "        98           1.3684           17.45s\n",
      "        99           1.3683           17.44s\n",
      "       100           1.3681           17.42s\n",
      "       101           1.3680           17.39s\n",
      "       102           1.3678           17.38s\n",
      "       103           1.3677           17.36s\n",
      "       104           1.3675           17.33s\n",
      "       105           1.3674           17.31s\n",
      "       106           1.3673           17.29s\n",
      "       107           1.3671           17.28s\n",
      "       108           1.3670           17.27s\n",
      "       109           1.3668           17.30s\n",
      "       110           1.3667           17.30s\n",
      "       111           1.3665           17.28s\n",
      "       112           1.3664           17.26s\n",
      "       113           1.3662           17.25s\n",
      "       114           1.3662           17.23s\n",
      "       115           1.3661           17.22s\n",
      "       116           1.3660           17.20s\n",
      "       117           1.3658           17.19s\n",
      "       118           1.3657           17.17s\n",
      "       119           1.3656           17.15s\n",
      "       120           1.3654           17.13s\n",
      "       121           1.3653           17.12s\n",
      "       122           1.3652           17.10s\n",
      "       123           1.3650           17.08s\n",
      "       124           1.3648           17.06s\n",
      "       125           1.3648           17.04s\n",
      "       126           1.3646           17.03s\n",
      "       127           1.3644           17.01s\n",
      "       128           1.3643           16.98s\n",
      "       129           1.3641           16.96s\n",
      "       130           1.3640           16.94s\n",
      "       131           1.3638           16.92s\n",
      "       132           1.3637           16.91s\n",
      "       133           1.3636           16.88s\n",
      "       134           1.3635           16.87s\n",
      "       135           1.3633           16.85s\n",
      "       136           1.3631           16.82s\n",
      "       137           1.3630           16.81s\n",
      "       138           1.3628           16.79s\n",
      "       139           1.3628           16.77s\n",
      "       140           1.3626           16.75s\n",
      "       141           1.3624           16.73s\n",
      "       142           1.3623           16.71s\n",
      "       143           1.3621           16.68s\n",
      "       144           1.3620           16.67s\n",
      "       145           1.3620           16.66s\n",
      "       146           1.3618           16.64s\n",
      "       147           1.3616           16.62s\n",
      "       148           1.3615           16.60s\n",
      "       149           1.3613           16.59s\n",
      "       150           1.3611           16.57s\n",
      "       151           1.3610           16.55s\n",
      "       152           1.3608           16.52s\n",
      "       153           1.3608           16.51s\n",
      "       154           1.3607           16.49s\n",
      "       155           1.3606           16.47s\n",
      "       156           1.3605           16.45s\n",
      "       157           1.3603           16.44s\n",
      "       158           1.3601           16.43s\n",
      "       159           1.3600           16.41s\n",
      "       160           1.3599           16.39s\n",
      "       161           1.3598           16.37s\n",
      "       162           1.3596           16.35s\n",
      "       163           1.3595           16.33s\n",
      "       164           1.3594           16.31s\n",
      "       165           1.3592           16.29s\n",
      "       166           1.3590           16.27s\n",
      "       167           1.3589           16.25s\n",
      "       168           1.3588           16.23s\n",
      "       169           1.3586           16.21s\n",
      "       170           1.3585           16.19s\n",
      "       171           1.3583           16.17s\n",
      "       172           1.3582           16.15s\n",
      "       173           1.3580           16.13s\n",
      "       174           1.3580           16.11s\n",
      "       175           1.3579           16.09s\n",
      "       176           1.3578           16.08s\n",
      "       177           1.3577           16.06s\n",
      "       178           1.3575           16.04s\n",
      "       179           1.3573           16.02s\n",
      "       180           1.3572           16.00s\n",
      "       181           1.3570           15.98s\n",
      "       182           1.3568           15.96s\n",
      "       183           1.3568           15.94s\n",
      "       184           1.3567           15.92s\n",
      "       185           1.3566           15.89s\n",
      "       186           1.3565           15.88s\n",
      "       187           1.3563           15.86s\n",
      "       188           1.3562           15.84s\n",
      "       189           1.3560           15.82s\n",
      "       190           1.3559           15.80s\n",
      "       191           1.3559           15.79s\n",
      "       192           1.3557           15.77s\n",
      "       193           1.3555           15.75s\n",
      "       194           1.3554           15.73s\n",
      "       195           1.3552           15.71s\n",
      "       196           1.3552           15.69s\n",
      "       197           1.3551           15.67s\n",
      "       198           1.3549           15.66s\n",
      "       199           1.3549           15.63s\n",
      "       200           1.3547           15.62s\n",
      "       201           1.3546           15.59s\n",
      "       202           1.3544           15.57s\n",
      "       203           1.3543           15.56s\n",
      "       204           1.3542           15.53s\n",
      "       205           1.3540           15.51s\n",
      "       206           1.3539           15.49s\n",
      "       207           1.3537           15.47s\n",
      "       208           1.3536           15.45s\n",
      "       209           1.3534           15.43s\n",
      "       210           1.3533           15.41s\n",
      "       211           1.3532           15.39s\n",
      "       212           1.3531           15.37s\n",
      "       213           1.3531           15.36s\n",
      "       214           1.3529           15.33s\n",
      "       215           1.3529           15.31s\n",
      "       216           1.3527           15.29s\n",
      "       217           1.3525           15.27s\n",
      "       218           1.3524           15.25s\n",
      "       219           1.3523           15.23s\n",
      "       220           1.3521           15.21s\n",
      "       221           1.3520           15.19s\n",
      "       222           1.3519           15.17s\n",
      "       223           1.3519           15.15s\n",
      "       224           1.3517           15.13s\n",
      "       225           1.3516           15.11s\n",
      "       226           1.3514           15.09s\n",
      "       227           1.3513           15.07s\n",
      "       228           1.3512           15.05s\n",
      "       229           1.3511           15.03s\n",
      "       230           1.3509           15.01s\n",
      "       231           1.3508           14.99s\n",
      "       232           1.3507           14.97s\n",
      "       233           1.3506           14.95s\n",
      "       234           1.3504           14.93s\n",
      "       235           1.3503           14.91s\n",
      "       236           1.3502           14.89s\n",
      "       237           1.3500           14.87s\n",
      "       238           1.3500           14.86s\n",
      "       239           1.3498           14.83s\n",
      "       240           1.3497           14.82s\n",
      "       241           1.3496           14.80s\n",
      "       242           1.3494           14.78s\n",
      "       243           1.3493           14.76s\n",
      "       244           1.3491           14.75s\n",
      "       245           1.3490           14.74s\n",
      "       246           1.3489           14.72s\n",
      "       247           1.3488           14.70s\n",
      "       248           1.3487           14.69s\n",
      "       249           1.3485           14.66s\n",
      "       250           1.3484           14.64s\n",
      "       251           1.3484           14.62s\n",
      "       252           1.3482           14.60s\n",
      "       253           1.3481           14.59s\n",
      "       254           1.3479           14.56s\n",
      "       255           1.3478           14.54s\n",
      "       256           1.3477           14.52s\n",
      "       257           1.3476           14.50s\n",
      "       258           1.3475           14.48s\n",
      "       259           1.3473           14.46s\n",
      "       260           1.3471           14.44s\n",
      "       261           1.3471           14.42s\n",
      "       262           1.3470           14.40s\n",
      "       263           1.3468           14.38s\n",
      "       264           1.3467           14.36s\n",
      "       265           1.3466           14.34s\n",
      "       266           1.3465           14.32s\n",
      "       267           1.3464           14.30s\n",
      "       268           1.3463           14.28s\n",
      "       269           1.3461           14.26s\n",
      "       270           1.3460           14.24s\n",
      "       271           1.3458           14.22s\n",
      "       272           1.3457           14.20s\n",
      "       273           1.3456           14.18s\n",
      "       274           1.3455           14.16s\n",
      "       275           1.3453           14.14s\n",
      "       276           1.3452           14.12s\n",
      "       277           1.3450           14.11s\n",
      "       278           1.3449           14.08s\n",
      "       279           1.3447           14.06s\n",
      "       280           1.3447           14.04s\n",
      "       281           1.3446           14.02s\n",
      "       282           1.3445           14.00s\n",
      "       283           1.3444           13.98s\n",
      "       284           1.3442           13.96s\n",
      "       285           1.3441           13.94s\n",
      "       286           1.3440           13.92s\n",
      "       287           1.3438           13.90s\n",
      "       288           1.3437           13.88s\n",
      "       289           1.3435           13.86s\n",
      "       290           1.3434           13.84s\n",
      "       291           1.3433           13.82s\n",
      "       292           1.3433           13.80s\n",
      "       293           1.3432           13.78s\n",
      "       294           1.3430           13.76s\n",
      "       295           1.3429           13.74s\n",
      "       296           1.3428           13.72s\n",
      "       297           1.3427           13.70s\n",
      "       298           1.3426           13.68s\n",
      "       299           1.3425           13.66s\n",
      "       300           1.3424           13.64s\n",
      "       301           1.3422           13.62s\n",
      "       302           1.3421           13.61s\n",
      "       303           1.3419           13.59s\n",
      "       304           1.3417           13.57s\n",
      "       305           1.3416           13.56s\n",
      "       306           1.3415           13.54s\n",
      "       307           1.3414           13.52s\n",
      "       308           1.3413           13.50s\n",
      "       309           1.3412           13.48s\n",
      "       310           1.3411           13.46s\n",
      "       311           1.3410           13.44s\n",
      "       312           1.3409           13.42s\n",
      "       313           1.3408           13.40s\n",
      "       314           1.3406           13.38s\n",
      "       315           1.3405           13.36s\n",
      "       316           1.3404           13.34s\n",
      "       317           1.3403           13.32s\n",
      "       318           1.3402           13.31s\n",
      "       319           1.3401           13.28s\n",
      "       320           1.3400           13.27s\n",
      "       321           1.3399           13.25s\n",
      "       322           1.3397           13.23s\n",
      "       323           1.3396           13.20s\n",
      "       324           1.3394           13.19s\n",
      "       325           1.3393           13.17s\n",
      "       326           1.3393           13.15s\n",
      "       327           1.3392           13.13s\n",
      "       328           1.3391           13.11s\n",
      "       329           1.3389           13.09s\n",
      "       330           1.3388           13.07s\n",
      "       331           1.3387           13.05s\n",
      "       332           1.3385           13.03s\n",
      "       333           1.3384           13.01s\n",
      "       334           1.3384           12.99s\n",
      "       335           1.3382           12.98s\n",
      "       336           1.3381           12.96s\n",
      "       337           1.3380           12.94s\n",
      "       338           1.3379           12.92s\n",
      "       339           1.3378           12.90s\n",
      "       340           1.3377           12.88s\n",
      "       341           1.3376           12.86s\n",
      "       342           1.3374           12.84s\n",
      "       343           1.3373           12.82s\n",
      "       344           1.3372           12.80s\n",
      "       345           1.3371           12.78s\n",
      "       346           1.3370           12.76s\n",
      "       347           1.3368           12.74s\n",
      "       348           1.3367           12.72s\n",
      "       349           1.3365           12.70s\n",
      "       350           1.3365           12.68s\n",
      "       351           1.3363           12.66s\n",
      "       352           1.3363           12.64s\n",
      "       353           1.3362           12.62s\n",
      "       354           1.3360           12.61s\n",
      "       355           1.3359           12.59s\n",
      "       356           1.3359           12.57s\n",
      "       357           1.3358           12.55s\n",
      "       358           1.3357           12.53s\n",
      "       359           1.3356           12.51s\n",
      "       360           1.3355           12.49s\n",
      "       361           1.3354           12.47s\n",
      "       362           1.3353           12.45s\n",
      "       363           1.3352           12.43s\n",
      "       364           1.3350           12.41s\n",
      "       365           1.3349           12.39s\n",
      "       366           1.3347           12.37s\n",
      "       367           1.3346           12.35s\n",
      "       368           1.3345           12.33s\n",
      "       369           1.3344           12.31s\n",
      "       370           1.3343           12.30s\n",
      "       371           1.3342           12.27s\n",
      "       372           1.3341           12.26s\n",
      "       373           1.3340           12.24s\n",
      "       374           1.3338           12.22s\n",
      "       375           1.3337           12.20s\n",
      "       376           1.3336           12.18s\n",
      "       377           1.3335           12.16s\n",
      "       378           1.3334           12.14s\n",
      "       379           1.3333           12.12s\n",
      "       380           1.3332           12.11s\n",
      "       381           1.3330           12.10s\n",
      "       382           1.3329           12.08s\n",
      "       383           1.3328           12.07s\n",
      "       384           1.3328           12.05s\n",
      "       385           1.3327           12.03s\n",
      "       386           1.3326           12.01s\n",
      "       387           1.3324           11.99s\n",
      "       388           1.3322           11.97s\n",
      "       389           1.3320           11.95s\n",
      "       390           1.3319           11.93s\n",
      "       391           1.3318           11.91s\n",
      "       392           1.3317           11.89s\n",
      "       393           1.3316           11.87s\n",
      "       394           1.3315           11.85s\n",
      "       395           1.3314           11.83s\n",
      "       396           1.3313           11.81s\n",
      "       397           1.3312           11.79s\n",
      "       398           1.3310           11.77s\n",
      "       399           1.3309           11.75s\n",
      "       400           1.3308           11.73s\n",
      "       401           1.3307           11.71s\n",
      "       402           1.3306           11.69s\n",
      "       403           1.3305           11.67s\n",
      "       404           1.3304           11.65s\n",
      "       405           1.3302           11.63s\n",
      "       406           1.3301           11.61s\n",
      "       407           1.3299           11.59s\n",
      "       408           1.3298           11.57s\n",
      "       409           1.3298           11.55s\n",
      "       410           1.3296           11.53s\n",
      "       411           1.3295           11.51s\n",
      "       412           1.3294           11.49s\n",
      "       413           1.3293           11.47s\n",
      "       414           1.3292           11.45s\n",
      "       415           1.3291           11.43s\n",
      "       416           1.3290           11.41s\n",
      "       417           1.3289           11.39s\n",
      "       418           1.3288           11.37s\n",
      "       419           1.3287           11.35s\n",
      "       420           1.3286           11.33s\n",
      "       421           1.3284           11.31s\n",
      "       422           1.3283           11.29s\n",
      "       423           1.3282           11.27s\n",
      "       424           1.3281           11.25s\n",
      "       425           1.3280           11.23s\n",
      "       426           1.3279           11.21s\n",
      "       427           1.3278           11.19s\n",
      "       428           1.3278           11.18s\n",
      "       429           1.3276           11.16s\n",
      "       430           1.3275           11.14s\n",
      "       431           1.3274           11.12s\n",
      "       432           1.3273           11.10s\n",
      "       433           1.3271           11.08s\n",
      "       434           1.3270           11.06s\n",
      "       435           1.3268           11.04s\n",
      "       436           1.3267           11.02s\n",
      "       437           1.3266           11.00s\n",
      "       438           1.3265           10.98s\n",
      "       439           1.3263           10.96s\n",
      "       440           1.3262           10.94s\n",
      "       441           1.3262           10.92s\n",
      "       442           1.3261           10.90s\n",
      "       443           1.3260           10.88s\n",
      "       444           1.3259           10.86s\n",
      "       445           1.3258           10.84s\n",
      "       446           1.3257           10.82s\n",
      "       447           1.3256           10.80s\n",
      "       448           1.3254           10.78s\n",
      "       449           1.3253           10.76s\n",
      "       450           1.3252           10.74s\n",
      "       451           1.3251           10.72s\n",
      "       452           1.3250           10.70s\n",
      "       453           1.3249           10.68s\n",
      "       454           1.3248           10.66s\n",
      "       455           1.3247           10.64s\n",
      "       456           1.3246           10.62s\n",
      "       457           1.3245           10.60s\n",
      "       458           1.3244           10.58s\n",
      "       459           1.3243           10.56s\n",
      "       460           1.3242           10.54s\n",
      "       461           1.3240           10.53s\n",
      "       462           1.3238           10.50s\n",
      "       463           1.3237           10.48s\n",
      "       464           1.3236           10.46s\n",
      "       465           1.3235           10.44s\n",
      "       466           1.3234           10.42s\n",
      "       467           1.3233           10.41s\n",
      "       468           1.3232           10.39s\n",
      "       469           1.3231           10.37s\n",
      "       470           1.3230           10.35s\n",
      "       471           1.3229           10.33s\n",
      "       472           1.3228           10.31s\n",
      "       473           1.3227           10.29s\n",
      "       474           1.3227           10.27s\n",
      "       475           1.3225           10.25s\n",
      "       476           1.3224           10.23s\n",
      "       477           1.3223           10.21s\n",
      "       478           1.3222           10.19s\n",
      "       479           1.3221           10.17s\n",
      "       480           1.3219           10.15s\n",
      "       481           1.3218           10.13s\n",
      "       482           1.3216           10.11s\n",
      "       483           1.3215           10.09s\n",
      "       484           1.3214           10.07s\n",
      "       485           1.3213           10.05s\n",
      "       486           1.3212           10.03s\n",
      "       487           1.3210           10.01s\n",
      "       488           1.3210            9.99s\n",
      "       489           1.3209            9.97s\n",
      "       490           1.3207            9.95s\n",
      "       491           1.3206            9.93s\n",
      "       492           1.3205            9.91s\n",
      "       493           1.3204            9.89s\n",
      "       494           1.3203            9.87s\n",
      "       495           1.3201            9.85s\n",
      "       496           1.3201            9.83s\n",
      "       497           1.3199            9.81s\n",
      "       498           1.3198            9.79s\n",
      "       499           1.3198            9.77s\n",
      "       500           1.3197            9.75s\n",
      "       501           1.3196            9.73s\n",
      "       502           1.3194            9.71s\n",
      "       503           1.3193            9.69s\n",
      "       504           1.3191            9.67s\n",
      "       505           1.3190            9.65s\n",
      "       506           1.3189            9.63s\n",
      "       507           1.3188            9.61s\n",
      "       508           1.3187            9.59s\n",
      "       509           1.3185            9.57s\n",
      "       510           1.3184            9.55s\n",
      "       511           1.3183            9.53s\n",
      "       512           1.3182            9.52s\n",
      "       513           1.3182            9.50s\n",
      "       514           1.3180            9.48s\n",
      "       515           1.3180            9.46s\n",
      "       516           1.3179            9.44s\n",
      "       517           1.3177            9.42s\n",
      "       518           1.3176            9.40s\n",
      "       519           1.3175            9.38s\n",
      "       520           1.3174            9.36s\n",
      "       521           1.3173            9.34s\n",
      "       522           1.3172            9.32s\n",
      "       523           1.3171            9.30s\n",
      "       524           1.3170            9.28s\n",
      "       525           1.3168            9.26s\n",
      "       526           1.3167            9.24s\n",
      "       527           1.3166            9.22s\n",
      "       528           1.3166            9.20s\n",
      "       529           1.3165            9.18s\n",
      "       530           1.3164            9.16s\n",
      "       531           1.3163            9.14s\n",
      "       532           1.3161            9.12s\n",
      "       533           1.3160            9.10s\n",
      "       534           1.3159            9.08s\n",
      "       535           1.3157            9.06s\n",
      "       536           1.3156            9.04s\n",
      "       537           1.3155            9.02s\n",
      "       538           1.3155            9.00s\n",
      "       539           1.3153            8.98s\n",
      "       540           1.3152            8.96s\n",
      "       541           1.3151            8.94s\n",
      "       542           1.3150            8.92s\n",
      "       543           1.3149            8.90s\n",
      "       544           1.3148            8.88s\n",
      "       545           1.3146            8.86s\n",
      "       546           1.3145            8.84s\n",
      "       547           1.3143            8.82s\n",
      "       548           1.3142            8.80s\n",
      "       549           1.3141            8.78s\n",
      "       550           1.3140            8.76s\n",
      "       551           1.3139            8.74s\n",
      "       552           1.3138            8.72s\n",
      "       553           1.3136            8.70s\n",
      "       554           1.3135            8.68s\n",
      "       555           1.3134            8.67s\n",
      "       556           1.3133            8.65s\n",
      "       557           1.3132            8.63s\n",
      "       558           1.3132            8.61s\n",
      "       559           1.3131            8.59s\n",
      "       560           1.3130            8.57s\n",
      "       561           1.3129            8.55s\n",
      "       562           1.3127            8.53s\n",
      "       563           1.3126            8.51s\n",
      "       564           1.3125            8.49s\n",
      "       565           1.3123            8.47s\n",
      "       566           1.3122            8.45s\n",
      "       567           1.3121            8.43s\n",
      "       568           1.3120            8.41s\n",
      "       569           1.3118            8.39s\n",
      "       570           1.3117            8.37s\n",
      "       571           1.3116            8.35s\n",
      "       572           1.3115            8.33s\n",
      "       573           1.3114            8.31s\n",
      "       574           1.3113            8.29s\n",
      "       575           1.3112            8.27s\n",
      "       576           1.3111            8.25s\n",
      "       577           1.3109            8.23s\n",
      "       578           1.3109            8.21s\n",
      "       579           1.3108            8.19s\n",
      "       580           1.3106            8.17s\n",
      "       581           1.3105            8.15s\n",
      "       582           1.3105            8.13s\n",
      "       583           1.3103            8.11s\n",
      "       584           1.3102            8.09s\n",
      "       585           1.3101            8.07s\n",
      "       586           1.3100            8.05s\n",
      "       587           1.3098            8.03s\n",
      "       588           1.3097            8.02s\n",
      "       589           1.3096            8.00s\n",
      "       590           1.3095            7.98s\n",
      "       591           1.3094            7.96s\n",
      "       592           1.3093            7.94s\n",
      "       593           1.3092            7.92s\n",
      "       594           1.3091            7.90s\n",
      "       595           1.3090            7.88s\n",
      "       596           1.3088            7.86s\n",
      "       597           1.3087            7.84s\n",
      "       598           1.3086            7.82s\n",
      "       599           1.3085            7.80s\n",
      "       600           1.3084            7.78s\n",
      "       601           1.3082            7.76s\n",
      "       602           1.3082            7.74s\n",
      "       603           1.3080            7.72s\n",
      "       604           1.3079            7.70s\n",
      "       605           1.3078            7.68s\n",
      "       606           1.3077            7.66s\n",
      "       607           1.3075            7.64s\n",
      "       608           1.3075            7.62s\n",
      "       609           1.3074            7.60s\n",
      "       610           1.3073            7.58s\n",
      "       611           1.3071            7.56s\n",
      "       612           1.3070            7.54s\n",
      "       613           1.3069            7.52s\n",
      "       614           1.3068            7.51s\n",
      "       615           1.3067            7.49s\n",
      "       616           1.3066            7.47s\n",
      "       617           1.3065            7.45s\n",
      "       618           1.3063            7.43s\n",
      "       619           1.3062            7.41s\n",
      "       620           1.3061            7.39s\n",
      "       621           1.3060            7.37s\n",
      "       622           1.3059            7.35s\n",
      "       623           1.3058            7.33s\n",
      "       624           1.3056            7.31s\n",
      "       625           1.3054            7.29s\n",
      "       626           1.3054            7.27s\n",
      "       627           1.3053            7.25s\n",
      "       628           1.3053            7.23s\n",
      "       629           1.3051            7.21s\n",
      "       630           1.3051            7.19s\n",
      "       631           1.3050            7.17s\n",
      "       632           1.3049            7.15s\n",
      "       633           1.3047            7.13s\n",
      "       634           1.3046            7.11s\n",
      "       635           1.3045            7.10s\n",
      "       636           1.3044            7.08s\n",
      "       637           1.3042            7.06s\n",
      "       638           1.3041            7.04s\n",
      "       639           1.3040            7.02s\n",
      "       640           1.3039            7.00s\n",
      "       641           1.3037            6.98s\n",
      "       642           1.3036            6.96s\n",
      "       643           1.3034            6.94s\n",
      "       644           1.3033            6.92s\n",
      "       645           1.3031            6.90s\n",
      "       646           1.3030            6.88s\n",
      "       647           1.3029            6.86s\n",
      "       648           1.3029            6.84s\n",
      "       649           1.3028            6.82s\n",
      "       650           1.3027            6.80s\n",
      "       651           1.3027            6.78s\n",
      "       652           1.3026            6.76s\n",
      "       653           1.3025            6.74s\n",
      "       654           1.3024            6.73s\n",
      "       655           1.3023            6.71s\n",
      "       656           1.3022            6.69s\n",
      "       657           1.3020            6.67s\n",
      "       658           1.3019            6.65s\n",
      "       659           1.3018            6.63s\n",
      "       660           1.3017            6.61s\n",
      "       661           1.3016            6.59s\n",
      "       662           1.3015            6.57s\n",
      "       663           1.3015            6.55s\n",
      "       664           1.3014            6.53s\n",
      "       665           1.3012            6.51s\n",
      "       666           1.3011            6.49s\n",
      "       667           1.3009            6.47s\n",
      "       668           1.3007            6.45s\n",
      "       669           1.3006            6.43s\n",
      "       670           1.3005            6.41s\n",
      "       671           1.3004            6.39s\n",
      "       672           1.3003            6.37s\n",
      "       673           1.3001            6.35s\n",
      "       674           1.3000            6.33s\n",
      "       675           1.2999            6.31s\n",
      "       676           1.2999            6.29s\n",
      "       677           1.2998            6.28s\n",
      "       678           1.2997            6.26s\n",
      "       679           1.2996            6.24s\n",
      "       680           1.2995            6.22s\n",
      "       681           1.2994            6.20s\n",
      "       682           1.2993            6.18s\n",
      "       683           1.2992            6.16s\n",
      "       684           1.2992            6.14s\n",
      "       685           1.2991            6.12s\n",
      "       686           1.2989            6.10s\n",
      "       687           1.2988            6.08s\n",
      "       688           1.2987            6.06s\n",
      "       689           1.2986            6.04s\n",
      "       690           1.2985            6.02s\n",
      "       691           1.2983            6.00s\n",
      "       692           1.2982            5.98s\n",
      "       693           1.2981            5.96s\n",
      "       694           1.2980            5.94s\n",
      "       695           1.2979            5.92s\n",
      "       696           1.2979            5.90s\n",
      "       697           1.2978            5.88s\n",
      "       698           1.2976            5.87s\n",
      "       699           1.2976            5.85s\n",
      "       700           1.2975            5.83s\n",
      "       701           1.2973            5.81s\n",
      "       702           1.2971            5.79s\n",
      "       703           1.2970            5.77s\n",
      "       704           1.2968            5.75s\n",
      "       705           1.2967            5.73s\n",
      "       706           1.2966            5.71s\n",
      "       707           1.2965            5.69s\n",
      "       708           1.2964            5.67s\n",
      "       709           1.2963            5.65s\n",
      "       710           1.2963            5.63s\n",
      "       711           1.2962            5.61s\n",
      "       712           1.2961            5.59s\n",
      "       713           1.2960            5.57s\n",
      "       714           1.2959            5.55s\n",
      "       715           1.2957            5.53s\n",
      "       716           1.2956            5.51s\n",
      "       717           1.2956            5.49s\n",
      "       718           1.2955            5.48s\n",
      "       719           1.2954            5.46s\n",
      "       720           1.2953            5.44s\n",
      "       721           1.2952            5.42s\n",
      "       722           1.2950            5.40s\n",
      "       723           1.2949            5.38s\n",
      "       724           1.2948            5.36s\n",
      "       725           1.2947            5.34s\n",
      "       726           1.2946            5.32s\n",
      "       727           1.2945            5.30s\n",
      "       728           1.2944            5.28s\n",
      "       729           1.2944            5.26s\n",
      "       730           1.2943            5.24s\n",
      "       731           1.2941            5.22s\n",
      "       732           1.2940            5.20s\n",
      "       733           1.2939            5.18s\n",
      "       734           1.2938            5.16s\n",
      "       735           1.2937            5.14s\n",
      "       736           1.2936            5.12s\n",
      "       737           1.2935            5.10s\n",
      "       738           1.2934            5.08s\n",
      "       739           1.2933            5.07s\n",
      "       740           1.2932            5.05s\n",
      "       741           1.2931            5.03s\n",
      "       742           1.2930            5.01s\n",
      "       743           1.2928            4.99s\n",
      "       744           1.2927            4.97s\n",
      "       745           1.2927            4.95s\n",
      "       746           1.2926            4.93s\n",
      "       747           1.2925            4.91s\n",
      "       748           1.2923            4.89s\n",
      "       749           1.2922            4.87s\n",
      "       750           1.2921            4.85s\n",
      "       751           1.2920            4.83s\n",
      "       752           1.2919            4.81s\n",
      "       753           1.2918            4.79s\n",
      "       754           1.2917            4.77s\n",
      "       755           1.2916            4.75s\n",
      "       756           1.2915            4.73s\n",
      "       757           1.2913            4.71s\n",
      "       758           1.2912            4.69s\n",
      "       759           1.2911            4.68s\n",
      "       760           1.2910            4.66s\n",
      "       761           1.2909            4.64s\n",
      "       762           1.2907            4.62s\n",
      "       763           1.2906            4.60s\n",
      "       764           1.2905            4.58s\n",
      "       765           1.2905            4.56s\n",
      "       766           1.2904            4.54s\n",
      "       767           1.2903            4.52s\n",
      "       768           1.2902            4.50s\n",
      "       769           1.2902            4.48s\n",
      "       770           1.2901            4.46s\n",
      "       771           1.2901            4.44s\n",
      "       772           1.2900            4.42s\n",
      "       773           1.2899            4.40s\n",
      "       774           1.2898            4.38s\n",
      "       775           1.2897            4.36s\n",
      "       776           1.2895            4.34s\n",
      "       777           1.2894            4.32s\n",
      "       778           1.2894            4.31s\n",
      "       779           1.2893            4.29s\n",
      "       780           1.2892            4.27s\n",
      "       781           1.2891            4.25s\n",
      "       782           1.2890            4.23s\n",
      "       783           1.2890            4.21s\n",
      "       784           1.2888            4.19s\n",
      "       785           1.2887            4.17s\n",
      "       786           1.2886            4.15s\n",
      "       787           1.2885            4.13s\n",
      "       788           1.2884            4.11s\n",
      "       789           1.2882            4.09s\n",
      "       790           1.2881            4.07s\n",
      "       791           1.2880            4.05s\n",
      "       792           1.2879            4.03s\n",
      "       793           1.2877            4.01s\n",
      "       794           1.2875            3.99s\n",
      "       795           1.2874            3.97s\n",
      "       796           1.2874            3.96s\n",
      "       797           1.2873            3.94s\n",
      "       798           1.2873            3.92s\n",
      "       799           1.2871            3.90s\n",
      "       800           1.2870            3.88s\n",
      "       801           1.2869            3.86s\n",
      "       802           1.2868            3.84s\n",
      "       803           1.2867            3.82s\n",
      "       804           1.2866            3.80s\n",
      "       805           1.2865            3.78s\n",
      "       806           1.2864            3.76s\n",
      "       807           1.2863            3.74s\n",
      "       808           1.2861            3.72s\n",
      "       809           1.2861            3.70s\n",
      "       810           1.2859            3.68s\n",
      "       811           1.2858            3.66s\n",
      "       812           1.2858            3.65s\n",
      "       813           1.2857            3.63s\n",
      "       814           1.2856            3.61s\n",
      "       815           1.2855            3.59s\n",
      "       816           1.2854            3.57s\n",
      "       817           1.2853            3.55s\n",
      "       818           1.2853            3.53s\n",
      "       819           1.2852            3.51s\n",
      "       820           1.2850            3.49s\n",
      "       821           1.2848            3.47s\n",
      "       822           1.2847            3.45s\n",
      "       823           1.2846            3.43s\n",
      "       824           1.2845            3.41s\n",
      "       825           1.2845            3.39s\n",
      "       826           1.2844            3.37s\n",
      "       827           1.2843            3.35s\n",
      "       828           1.2843            3.33s\n",
      "       829           1.2843            3.32s\n",
      "       830           1.2841            3.30s\n",
      "       831           1.2840            3.28s\n",
      "       832           1.2840            3.26s\n",
      "       833           1.2839            3.24s\n",
      "       834           1.2837            3.22s\n",
      "       835           1.2836            3.20s\n",
      "       836           1.2834            3.18s\n",
      "       837           1.2833            3.16s\n",
      "       838           1.2832            3.14s\n",
      "       839           1.2831            3.12s\n",
      "       840           1.2829            3.10s\n",
      "       841           1.2828            3.08s\n",
      "       842           1.2827            3.06s\n",
      "       843           1.2826            3.04s\n",
      "       844           1.2825            3.02s\n",
      "       845           1.2824            3.00s\n",
      "       846           1.2823            2.98s\n",
      "       847           1.2822            2.97s\n",
      "       848           1.2822            2.95s\n",
      "       849           1.2821            2.93s\n",
      "       850           1.2819            2.91s\n",
      "       851           1.2818            2.89s\n",
      "       852           1.2817            2.87s\n",
      "       853           1.2816            2.85s\n",
      "       854           1.2815            2.83s\n",
      "       855           1.2814            2.81s\n",
      "       856           1.2812            2.79s\n",
      "       857           1.2811            2.77s\n",
      "       858           1.2810            2.75s\n",
      "       859           1.2809            2.73s\n",
      "       860           1.2808            2.71s\n",
      "       861           1.2807            2.69s\n",
      "       862           1.2807            2.67s\n",
      "       863           1.2806            2.65s\n",
      "       864           1.2805            2.64s\n",
      "       865           1.2804            2.62s\n",
      "       866           1.2803            2.60s\n",
      "       867           1.2803            2.58s\n",
      "       868           1.2802            2.56s\n",
      "       869           1.2802            2.54s\n",
      "       870           1.2802            2.52s\n",
      "       871           1.2801            2.50s\n",
      "       872           1.2799            2.48s\n",
      "       873           1.2798            2.46s\n",
      "       874           1.2797            2.44s\n",
      "       875           1.2796            2.42s\n",
      "       876           1.2796            2.40s\n",
      "       877           1.2795            2.38s\n",
      "       878           1.2794            2.36s\n",
      "       879           1.2792            2.34s\n",
      "       880           1.2791            2.33s\n",
      "       881           1.2790            2.31s\n",
      "       882           1.2789            2.29s\n",
      "       883           1.2788            2.27s\n",
      "       884           1.2787            2.25s\n",
      "       885           1.2787            2.23s\n",
      "       886           1.2786            2.21s\n",
      "       887           1.2785            2.19s\n",
      "       888           1.2784            2.17s\n",
      "       889           1.2783            2.15s\n",
      "       890           1.2781            2.13s\n",
      "       891           1.2780            2.11s\n",
      "       892           1.2779            2.09s\n",
      "       893           1.2779            2.07s\n",
      "       894           1.2778            2.05s\n",
      "       895           1.2777            2.03s\n",
      "       896           1.2777            2.02s\n",
      "       897           1.2777            2.00s\n",
      "       898           1.2775            1.98s\n",
      "       899           1.2775            1.96s\n",
      "       900           1.2773            1.94s\n",
      "       901           1.2771            1.92s\n",
      "       902           1.2770            1.90s\n",
      "       903           1.2770            1.88s\n",
      "       904           1.2769            1.86s\n",
      "       905           1.2767            1.84s\n",
      "       906           1.2767            1.82s\n",
      "       907           1.2766            1.80s\n",
      "       908           1.2765            1.78s\n",
      "       909           1.2764            1.76s\n",
      "       910           1.2762            1.74s\n",
      "       911           1.2761            1.72s\n",
      "       912           1.2761            1.71s\n",
      "       913           1.2761            1.69s\n",
      "       914           1.2759            1.67s\n",
      "       915           1.2758            1.65s\n",
      "       916           1.2757            1.63s\n",
      "       917           1.2756            1.61s\n",
      "       918           1.2754            1.59s\n",
      "       919           1.2753            1.57s\n",
      "       920           1.2753            1.55s\n",
      "       921           1.2751            1.53s\n",
      "       922           1.2751            1.51s\n",
      "       923           1.2750            1.49s\n",
      "       924           1.2748            1.47s\n",
      "       925           1.2747            1.45s\n",
      "       926           1.2747            1.43s\n",
      "       927           1.2745            1.41s\n",
      "       928           1.2745            1.40s\n",
      "       929           1.2743            1.38s\n",
      "       930           1.2742            1.36s\n",
      "       931           1.2741            1.34s\n",
      "       932           1.2740            1.32s\n",
      "       933           1.2739            1.30s\n",
      "       934           1.2738            1.28s\n",
      "       935           1.2738            1.26s\n",
      "       936           1.2737            1.24s\n",
      "       937           1.2736            1.22s\n",
      "       938           1.2736            1.20s\n",
      "       939           1.2734            1.18s\n",
      "       940           1.2733            1.16s\n",
      "       941           1.2732            1.14s\n",
      "       942           1.2731            1.12s\n",
      "       943           1.2730            1.10s\n",
      "       944           1.2729            1.09s\n",
      "       945           1.2728            1.07s\n",
      "       946           1.2728            1.05s\n",
      "       947           1.2727            1.03s\n",
      "       948           1.2727            1.01s\n",
      "       949           1.2726            0.99s\n",
      "       950           1.2725            0.97s\n",
      "       951           1.2723            0.95s\n",
      "       952           1.2723            0.93s\n",
      "       953           1.2721            0.91s\n",
      "       954           1.2720            0.89s\n",
      "       955           1.2719            0.87s\n",
      "       956           1.2717            0.85s\n",
      "       957           1.2717            0.83s\n",
      "       958           1.2716            0.81s\n",
      "       959           1.2715            0.79s\n",
      "       960           1.2715            0.78s\n",
      "       961           1.2714            0.76s\n",
      "       962           1.2713            0.74s\n",
      "       963           1.2712            0.72s\n",
      "       964           1.2710            0.70s\n",
      "       965           1.2709            0.68s\n",
      "       966           1.2708            0.66s\n",
      "       967           1.2706            0.64s\n",
      "       968           1.2705            0.62s\n",
      "       969           1.2704            0.60s\n",
      "       970           1.2703            0.58s\n",
      "       971           1.2703            0.56s\n",
      "       972           1.2701            0.54s\n",
      "       973           1.2700            0.52s\n",
      "       974           1.2699            0.50s\n",
      "       975           1.2698            0.48s\n",
      "       976           1.2697            0.47s\n",
      "       977           1.2696            0.45s\n",
      "       978           1.2695            0.43s\n",
      "       979           1.2693            0.41s\n",
      "       980           1.2692            0.39s\n",
      "       981           1.2691            0.37s\n",
      "       982           1.2690            0.35s\n",
      "       983           1.2690            0.33s\n",
      "       984           1.2688            0.31s\n",
      "       985           1.2688            0.29s\n",
      "       986           1.2686            0.27s\n",
      "       987           1.2685            0.25s\n",
      "       988           1.2685            0.23s\n",
      "       989           1.2684            0.21s\n",
      "       990           1.2683            0.19s\n",
      "       991           1.2682            0.17s\n",
      "       992           1.2681            0.16s\n",
      "       993           1.2681            0.14s\n",
      "       994           1.2679            0.12s\n",
      "       995           1.2679            0.10s\n",
      "       996           1.2678            0.08s\n",
      "       997           1.2676            0.06s\n",
      "       998           1.2675            0.04s\n",
      "       999           1.2674            0.02s\n",
      "      1000           1.2674            0.00s\n",
      "      Iter       Train Loss   Remaining Time \n",
      "         1           1.3861           18.98s\n",
      "         2           1.3859           18.96s\n",
      "         3           1.3858           18.94s\n",
      "         4           1.3856           18.92s\n",
      "         5           1.3854           18.91s\n",
      "         6           1.3853           18.89s\n",
      "         7           1.3851           18.87s\n",
      "         8           1.3850           18.72s\n",
      "         9           1.3848           18.72s\n",
      "        10           1.3847           18.81s\n",
      "        11           1.3845           18.79s\n",
      "        12           1.3844           18.77s\n",
      "        13           1.3842           18.75s\n",
      "        14           1.3841           18.73s\n",
      "        15           1.3839           18.72s\n",
      "        16           1.3838           18.63s\n",
      "        17           1.3836           18.62s\n",
      "        18           1.3835           18.60s\n",
      "        19           1.3833           18.59s\n",
      "        20           1.3831           18.57s\n",
      "        21           1.3829           18.55s\n",
      "        22           1.3828           18.54s\n",
      "        23           1.3825           18.52s\n",
      "        24           1.3824           18.50s\n",
      "        25           1.3822           18.49s\n",
      "        26           1.3821           18.47s\n",
      "        27           1.3818           18.45s\n",
      "        28           1.3817           18.43s\n",
      "        29           1.3815           18.42s\n",
      "        30           1.3814           18.40s\n",
      "        31           1.3812           18.38s\n",
      "        32           1.3810           18.39s\n",
      "        33           1.3808           18.37s\n",
      "        34           1.3807           18.38s\n",
      "        35           1.3805           18.36s\n",
      "        36           1.3803           18.34s\n",
      "        37           1.3802           18.32s\n",
      "        38           1.3800           18.33s\n",
      "        39           1.3798           18.31s\n",
      "        40           1.3797           18.31s\n",
      "        41           1.3795           18.29s\n",
      "        42           1.3794           18.27s\n",
      "        43           1.3793           18.25s\n",
      "        44           1.3791           18.25s\n",
      "        45           1.3789           18.23s\n",
      "        46           1.3788           18.21s\n",
      "        47           1.3786           18.19s\n",
      "        48           1.3784           18.17s\n",
      "        49           1.3783           18.15s\n",
      "        50           1.3781           18.15s\n",
      "        51           1.3780           18.20s\n",
      "        52           1.3779           18.18s\n",
      "        53           1.3778           18.17s\n",
      "        54           1.3776           18.15s\n",
      "        55           1.3774           18.14s\n",
      "        56           1.3773           18.12s\n",
      "        57           1.3772           18.10s\n",
      "        58           1.3770           18.08s\n",
      "        59           1.3769           18.05s\n",
      "        60           1.3768           18.03s\n",
      "        61           1.3766           18.01s\n",
      "        62           1.3765           17.99s\n",
      "        63           1.3764           17.98s\n",
      "        64           1.3762           17.97s\n",
      "        65           1.3760           17.97s\n",
      "        66           1.3760           17.96s\n",
      "        67           1.3758           17.94s\n",
      "        68           1.3757           17.91s\n",
      "        69           1.3755           17.90s\n",
      "        70           1.3755           17.88s\n",
      "        71           1.3753           17.87s\n",
      "        72           1.3752           17.85s\n",
      "        73           1.3751           17.82s\n",
      "        74           1.3749           17.81s\n",
      "        75           1.3748           17.78s\n",
      "        76           1.3747           17.76s\n",
      "        77           1.3746           17.75s\n",
      "        78           1.3745           17.73s\n",
      "        79           1.3743           17.71s\n",
      "        80           1.3742           17.69s\n",
      "        81           1.3741           17.73s\n",
      "        82           1.3739           17.71s\n",
      "        83           1.3737           17.69s\n",
      "        84           1.3736           17.67s\n",
      "        85           1.3735           17.64s\n",
      "        86           1.3733           17.62s\n",
      "        87           1.3732           17.61s\n",
      "        88           1.3731           17.59s\n",
      "        89           1.3730           17.58s\n",
      "        90           1.3729           17.55s\n",
      "        91           1.3727           17.53s\n",
      "        92           1.3726           17.51s\n",
      "        93           1.3725           17.49s\n",
      "        94           1.3723           17.46s\n",
      "        95           1.3722           17.45s\n",
      "        96           1.3721           17.42s\n",
      "        97           1.3720           17.40s\n",
      "        98           1.3718           17.38s\n",
      "        99           1.3717           17.36s\n",
      "       100           1.3715           17.33s\n",
      "       101           1.3715           17.32s\n",
      "       102           1.3713           17.30s\n",
      "       103           1.3711           17.28s\n",
      "       104           1.3711           17.26s\n",
      "       105           1.3709           17.24s\n",
      "       106           1.3708           17.21s\n",
      "       107           1.3706           17.18s\n",
      "       108           1.3705           17.16s\n",
      "       109           1.3705           17.15s\n",
      "       110           1.3703           17.13s\n",
      "       111           1.3702           17.11s\n",
      "       112           1.3701           17.09s\n",
      "       113           1.3699           17.07s\n",
      "       114           1.3698           17.05s\n",
      "       115           1.3697           17.03s\n",
      "       116           1.3696           17.00s\n",
      "       117           1.3694           16.98s\n",
      "       118           1.3692           16.96s\n",
      "       119           1.3692           16.94s\n",
      "       120           1.3691           16.92s\n",
      "       121           1.3689           16.90s\n",
      "       122           1.3688           16.88s\n",
      "       123           1.3687           16.86s\n",
      "       124           1.3685           16.83s\n",
      "       125           1.3683           16.81s\n",
      "       126           1.3682           16.81s\n",
      "       127           1.3681           16.79s\n",
      "       128           1.3679           16.77s\n",
      "       129           1.3678           16.75s\n",
      "       130           1.3677           16.73s\n",
      "       131           1.3676           16.71s\n",
      "       132           1.3674           16.69s\n",
      "       133           1.3672           16.68s\n",
      "       134           1.3672           16.65s\n",
      "       135           1.3670           16.63s\n",
      "       136           1.3669           16.61s\n",
      "       137           1.3668           16.59s\n",
      "       138           1.3666           16.57s\n",
      "       139           1.3665           16.55s\n",
      "       140           1.3664           16.53s\n",
      "       141           1.3662           16.51s\n",
      "       142           1.3661           16.49s\n",
      "       143           1.3660           16.47s\n",
      "       144           1.3658           16.45s\n",
      "       145           1.3656           16.43s\n",
      "       146           1.3655           16.41s\n",
      "       147           1.3653           16.39s\n",
      "       148           1.3653           16.37s\n",
      "       149           1.3651           16.35s\n",
      "       150           1.3650           16.33s\n",
      "       151           1.3648           16.31s\n",
      "       152           1.3647           16.28s\n",
      "       153           1.3645           16.26s\n",
      "       154           1.3644           16.24s\n",
      "       155           1.3642           16.23s\n",
      "       156           1.3641           16.21s\n",
      "       157           1.3640           16.18s\n",
      "       158           1.3638           16.16s\n",
      "       159           1.3638           16.14s\n",
      "       160           1.3637           16.13s\n",
      "       161           1.3636           16.11s\n",
      "       162           1.3635           16.09s\n",
      "       163           1.3634           16.06s\n",
      "       164           1.3632           16.04s\n",
      "       165           1.3631           16.02s\n",
      "       166           1.3629           16.00s\n",
      "       167           1.3628           15.99s\n",
      "       168           1.3627           15.97s\n",
      "       169           1.3626           15.95s\n",
      "       170           1.3624           15.93s\n",
      "       171           1.3623           15.91s\n",
      "       172           1.3622           15.89s\n",
      "       173           1.3621           15.86s\n",
      "       174           1.3620           15.84s\n",
      "       175           1.3619           15.82s\n",
      "       176           1.3618           15.80s\n",
      "       177           1.3617           15.78s\n",
      "       178           1.3616           15.77s\n",
      "       179           1.3615           15.75s\n",
      "       180           1.3613           15.73s\n",
      "       181           1.3612           15.71s\n",
      "       182           1.3612           15.69s\n",
      "       183           1.3611           15.67s\n",
      "       184           1.3609           15.65s\n",
      "       185           1.3608           15.63s\n",
      "       186           1.3607           15.61s\n",
      "       187           1.3605           15.59s\n",
      "       188           1.3604           15.57s\n",
      "       189           1.3603           15.55s\n",
      "       190           1.3602           15.53s\n",
      "       191           1.3602           15.51s\n",
      "       192           1.3601           15.49s\n",
      "       193           1.3600           15.47s\n",
      "       194           1.3598           15.45s\n",
      "       195           1.3596           15.43s\n",
      "       196           1.3596           15.41s\n",
      "       197           1.3594           15.39s\n",
      "       198           1.3594           15.37s\n",
      "       199           1.3592           15.35s\n",
      "       200           1.3591           15.33s\n",
      "       201           1.3591           15.31s\n",
      "       202           1.3589           15.29s\n",
      "       203           1.3588           15.27s\n",
      "       204           1.3587           15.25s\n",
      "       205           1.3585           15.24s\n",
      "       206           1.3584           15.22s\n",
      "       207           1.3583           15.20s\n",
      "       208           1.3582           15.19s\n",
      "       209           1.3581           15.17s\n",
      "       210           1.3579           15.14s\n",
      "       211           1.3578           15.13s\n",
      "       212           1.3578           15.11s\n",
      "       213           1.3576           15.09s\n",
      "       214           1.3575           15.07s\n",
      "       215           1.3573           15.05s\n",
      "       216           1.3571           15.03s\n",
      "       217           1.3571           15.01s\n",
      "       218           1.3570           14.99s\n",
      "       219           1.3569           14.97s\n",
      "       220           1.3569           14.95s\n",
      "       221           1.3567           14.93s\n",
      "       222           1.3566           14.91s\n",
      "       223           1.3564           14.89s\n",
      "       224           1.3562           14.87s\n",
      "       225           1.3560           14.85s\n",
      "       226           1.3560           14.83s\n",
      "       227           1.3558           14.81s\n",
      "       228           1.3557           14.79s\n",
      "       229           1.3555           14.77s\n",
      "       230           1.3554           14.75s\n",
      "       231           1.3554           14.73s\n",
      "       232           1.3553           14.71s\n",
      "       233           1.3551           14.69s\n",
      "       234           1.3550           14.67s\n",
      "       235           1.3549           14.65s\n",
      "       236           1.3548           14.64s\n",
      "       237           1.3548           14.62s\n",
      "       238           1.3546           14.60s\n",
      "       239           1.3544           14.58s\n",
      "       240           1.3544           14.56s\n",
      "       241           1.3542           14.54s\n",
      "       242           1.3542           14.52s\n",
      "       243           1.3541           14.50s\n",
      "       244           1.3540           14.48s\n",
      "       245           1.3539           14.46s\n",
      "       246           1.3537           14.44s\n",
      "       247           1.3536           14.42s\n",
      "       248           1.3535           14.40s\n",
      "       249           1.3534           14.38s\n",
      "       250           1.3532           14.36s\n",
      "       251           1.3531           14.34s\n",
      "       252           1.3530           14.33s\n",
      "       253           1.3529           14.31s\n",
      "       254           1.3528           14.29s\n",
      "       255           1.3527           14.27s\n",
      "       256           1.3526           14.25s\n",
      "       257           1.3525           14.23s\n",
      "       258           1.3523           14.21s\n",
      "       259           1.3523           14.19s\n",
      "       260           1.3521           14.17s\n",
      "       261           1.3519           14.15s\n",
      "       262           1.3518           14.13s\n",
      "       263           1.3517           14.11s\n",
      "       264           1.3516           14.09s\n",
      "       265           1.3515           14.07s\n",
      "       266           1.3514           14.06s\n",
      "       267           1.3513           14.04s\n",
      "       268           1.3513           14.02s\n",
      "       269           1.3511           14.00s\n",
      "       270           1.3510           13.98s\n",
      "       271           1.3509           13.96s\n",
      "       272           1.3507           13.94s\n",
      "       273           1.3506           13.92s\n",
      "       274           1.3505           13.90s\n",
      "       275           1.3504           13.88s\n",
      "       276           1.3503           13.86s\n",
      "       277           1.3501           13.84s\n",
      "       278           1.3500           13.82s\n",
      "       279           1.3498           13.80s\n",
      "       280           1.3496           13.79s\n",
      "       281           1.3496           13.77s\n",
      "       282           1.3495           13.75s\n",
      "       283           1.3493           13.73s\n",
      "       284           1.3492           13.71s\n",
      "       285           1.3491           13.69s\n",
      "       286           1.3490           13.67s\n",
      "       287           1.3488           13.65s\n",
      "       288           1.3487           13.63s\n",
      "       289           1.3486           13.61s\n",
      "       290           1.3484           13.59s\n",
      "       291           1.3484           13.57s\n",
      "       292           1.3482           13.55s\n",
      "       293           1.3482           13.53s\n",
      "       294           1.3481           13.51s\n",
      "       295           1.3481           13.50s\n",
      "       296           1.3479           13.48s\n",
      "       297           1.3478           13.46s\n",
      "       298           1.3476           13.44s\n",
      "       299           1.3474           13.42s\n",
      "       300           1.3473           13.40s\n",
      "       301           1.3472           13.38s\n",
      "       302           1.3471           13.36s\n",
      "       303           1.3470           13.34s\n",
      "       304           1.3468           13.32s\n",
      "       305           1.3468           13.30s\n",
      "       306           1.3466           13.28s\n",
      "       307           1.3464           13.26s\n",
      "       308           1.3463           13.25s\n",
      "       309           1.3463           13.23s\n",
      "       310           1.3462           13.21s\n",
      "       311           1.3460           13.19s\n",
      "       312           1.3459           13.17s\n",
      "       313           1.3458           13.15s\n",
      "       314           1.3457           13.13s\n",
      "       315           1.3455           13.11s\n",
      "       316           1.3454           13.09s\n",
      "       317           1.3453           13.07s\n",
      "       318           1.3452           13.05s\n",
      "       319           1.3451           13.03s\n",
      "       320           1.3451           13.01s\n",
      "       321           1.3450           12.99s\n",
      "       322           1.3448           12.97s\n",
      "       323           1.3447           12.95s\n",
      "       324           1.3446           12.93s\n",
      "       325           1.3445           12.91s\n",
      "       326           1.3444           12.89s\n",
      "       327           1.3443           12.87s\n",
      "       328           1.3441           12.85s\n",
      "       329           1.3440           12.83s\n",
      "       330           1.3439           12.82s\n",
      "       331           1.3437           12.80s\n",
      "       332           1.3437           12.78s\n",
      "       333           1.3436           12.76s\n",
      "       334           1.3435           12.74s\n",
      "       335           1.3434           12.72s\n",
      "       336           1.3434           12.70s\n",
      "       337           1.3433           12.68s\n",
      "       338           1.3432           12.66s\n",
      "       339           1.3430           12.64s\n",
      "       340           1.3429           12.62s\n",
      "       341           1.3428           12.60s\n",
      "       342           1.3427           12.58s\n",
      "       343           1.3425           12.56s\n",
      "       344           1.3424           12.54s\n",
      "       345           1.3423           12.52s\n",
      "       346           1.3422           12.50s\n",
      "       347           1.3421           12.48s\n",
      "       348           1.3420           12.46s\n",
      "       349           1.3420           12.45s\n",
      "       350           1.3418           12.43s\n",
      "       351           1.3417           12.41s\n",
      "       352           1.3415           12.39s\n",
      "       353           1.3414           12.37s\n",
      "       354           1.3413           12.35s\n",
      "       355           1.3412           12.33s\n",
      "       356           1.3410           12.31s\n",
      "       357           1.3409           12.29s\n",
      "       358           1.3409           12.27s\n",
      "       359           1.3408           12.25s\n",
      "       360           1.3407           12.23s\n",
      "       361           1.3406           12.21s\n",
      "       362           1.3404           12.19s\n",
      "       363           1.3403           12.17s\n",
      "       364           1.3401           12.16s\n",
      "       365           1.3400           12.14s\n",
      "       366           1.3400           12.12s\n",
      "       367           1.3399           12.10s\n",
      "       368           1.3398           12.08s\n",
      "       369           1.3397           12.06s\n",
      "       370           1.3396           12.04s\n",
      "       371           1.3395           12.02s\n",
      "       372           1.3394           12.00s\n",
      "       373           1.3393           11.98s\n",
      "       374           1.3391           11.96s\n",
      "       375           1.3391           11.94s\n",
      "       376           1.3390           11.92s\n",
      "       377           1.3388           11.90s\n",
      "       378           1.3387           11.89s\n",
      "       379           1.3386           11.86s\n",
      "       380           1.3386           11.85s\n",
      "       381           1.3384           11.83s\n",
      "       382           1.3383           11.81s\n",
      "       383           1.3382           11.79s\n",
      "       384           1.3381           11.77s\n",
      "       385           1.3380           11.75s\n",
      "       386           1.3380           11.73s\n",
      "       387           1.3380           11.71s\n",
      "       388           1.3378           11.69s\n",
      "       389           1.3377           11.67s\n",
      "       390           1.3376           11.65s\n",
      "       391           1.3375           11.63s\n",
      "       392           1.3374           11.61s\n",
      "       393           1.3373           11.59s\n",
      "       394           1.3372           11.58s\n",
      "       395           1.3371           11.56s\n",
      "       396           1.3370           11.54s\n",
      "       397           1.3370           11.52s\n",
      "       398           1.3369           11.50s\n",
      "       399           1.3368           11.48s\n",
      "       400           1.3366           11.46s\n",
      "       401           1.3364           11.44s\n",
      "       402           1.3363           11.42s\n",
      "       403           1.3363           11.40s\n",
      "       404           1.3363           11.38s\n",
      "       405           1.3361           11.36s\n",
      "       406           1.3361           11.34s\n",
      "       407           1.3360           11.33s\n",
      "       408           1.3359           11.31s\n",
      "       409           1.3357           11.29s\n",
      "       410           1.3356           11.27s\n",
      "       411           1.3354           11.25s\n",
      "       412           1.3354           11.23s\n",
      "       413           1.3353           11.21s\n",
      "       414           1.3352           11.19s\n",
      "       415           1.3351           11.17s\n",
      "       416           1.3350           11.15s\n",
      "       417           1.3349           11.14s\n",
      "       418           1.3348           11.12s\n",
      "       419           1.3347           11.10s\n",
      "       420           1.3346           11.08s\n",
      "       421           1.3345           11.06s\n",
      "       422           1.3345           11.04s\n",
      "       423           1.3343           11.02s\n",
      "       424           1.3342           11.00s\n",
      "       425           1.3341           10.98s\n",
      "       426           1.3340           10.96s\n",
      "       427           1.3339           10.94s\n",
      "       428           1.3338           10.92s\n",
      "       429           1.3337           10.90s\n",
      "       430           1.3337           10.89s\n",
      "       431           1.3335           10.87s\n",
      "       432           1.3334           10.85s\n",
      "       433           1.3333           10.83s\n",
      "       434           1.3332           10.81s\n",
      "       435           1.3330           10.79s\n",
      "       436           1.3329           10.77s\n",
      "       437           1.3328           10.75s\n",
      "       438           1.3328           10.73s\n",
      "       439           1.3326           10.71s\n",
      "       440           1.3325           10.69s\n",
      "       441           1.3324           10.67s\n",
      "       442           1.3322           10.65s\n",
      "       443           1.3322           10.63s\n",
      "       444           1.3321           10.62s\n",
      "       445           1.3320           10.60s\n",
      "       446           1.3319           10.58s\n",
      "       447           1.3318           10.56s\n",
      "       448           1.3318           10.54s\n",
      "       449           1.3316           10.52s\n",
      "       450           1.3315           10.50s\n",
      "       451           1.3315           10.48s\n",
      "       452           1.3314           10.46s\n",
      "       453           1.3312           10.44s\n",
      "       454           1.3310           10.42s\n",
      "       455           1.3309           10.40s\n",
      "       456           1.3308           10.38s\n",
      "       457           1.3307           10.36s\n",
      "       458           1.3307           10.35s\n",
      "       459           1.3306           10.33s\n",
      "       460           1.3305           10.31s\n",
      "       461           1.3304           10.29s\n",
      "       462           1.3302           10.27s\n",
      "       463           1.3301           10.25s\n",
      "       464           1.3300           10.23s\n",
      "       465           1.3299           10.21s\n",
      "       466           1.3298           10.19s\n",
      "       467           1.3297           10.17s\n",
      "       468           1.3296           10.15s\n",
      "       469           1.3296           10.13s\n",
      "       470           1.3294           10.11s\n",
      "       471           1.3293           10.09s\n",
      "       472           1.3292           10.08s\n",
      "       473           1.3291           10.06s\n",
      "       474           1.3290           10.04s\n",
      "       475           1.3289           10.02s\n",
      "       476           1.3289           10.00s\n",
      "       477           1.3287            9.98s\n",
      "       478           1.3286            9.96s\n",
      "       479           1.3285            9.94s\n",
      "       480           1.3284            9.92s\n",
      "       481           1.3283            9.90s\n",
      "       482           1.3283            9.88s\n",
      "       483           1.3281            9.87s\n",
      "       484           1.3281            9.85s\n",
      "       485           1.3279            9.83s\n",
      "       486           1.3277            9.81s\n",
      "       487           1.3277            9.79s\n",
      "       488           1.3276            9.77s\n",
      "       489           1.3275            9.75s\n",
      "       490           1.3274            9.73s\n",
      "       491           1.3273            9.71s\n",
      "       492           1.3273            9.69s\n",
      "       493           1.3271            9.68s\n",
      "       494           1.3270            9.66s\n",
      "       495           1.3269            9.64s\n",
      "       496           1.3268            9.62s\n",
      "       497           1.3267            9.60s\n",
      "       498           1.3266            9.58s\n",
      "       499           1.3265            9.56s\n",
      "       500           1.3264            9.54s\n",
      "       501           1.3263            9.52s\n",
      "       502           1.3262            9.50s\n",
      "       503           1.3261            9.48s\n",
      "       504           1.3260            9.47s\n",
      "       505           1.3260            9.45s\n",
      "       506           1.3259            9.43s\n",
      "       507           1.3258            9.41s\n",
      "       508           1.3257            9.39s\n",
      "       509           1.3255            9.37s\n",
      "       510           1.3254            9.35s\n",
      "       511           1.3253            9.33s\n",
      "       512           1.3252            9.32s\n",
      "       513           1.3251            9.30s\n",
      "       514           1.3250            9.28s\n",
      "       515           1.3249            9.26s\n",
      "       516           1.3248            9.24s\n",
      "       517           1.3247            9.22s\n",
      "       518           1.3246            9.20s\n",
      "       519           1.3246            9.18s\n",
      "       520           1.3245            9.16s\n",
      "       521           1.3244            9.15s\n",
      "       522           1.3243            9.13s\n",
      "       523           1.3242            9.11s\n",
      "       524           1.3241            9.09s\n",
      "       525           1.3240            9.07s\n",
      "       526           1.3239            9.05s\n",
      "       527           1.3238            9.03s\n",
      "       528           1.3237            9.01s\n",
      "       529           1.3236            8.99s\n",
      "       530           1.3234            8.97s\n",
      "       531           1.3233            8.95s\n",
      "       532           1.3232            8.93s\n",
      "       533           1.3232            8.91s\n",
      "       534           1.3231            8.90s\n",
      "       535           1.3230            8.88s\n",
      "       536           1.3229            8.86s\n",
      "       537           1.3228            8.84s\n",
      "       538           1.3227            8.82s\n",
      "       539           1.3227            8.80s\n",
      "       540           1.3225            8.78s\n",
      "       541           1.3224            8.76s\n",
      "       542           1.3223            8.74s\n",
      "       543           1.3222            8.72s\n",
      "       544           1.3220            8.70s\n",
      "       545           1.3219            8.68s\n",
      "       546           1.3219            8.67s\n",
      "       547           1.3219            8.65s\n",
      "       548           1.3218            8.63s\n",
      "       549           1.3217            8.61s\n",
      "       550           1.3216            8.59s\n",
      "       551           1.3215            8.57s\n",
      "       552           1.3213            8.55s\n",
      "       553           1.3212            8.53s\n",
      "       554           1.3211            8.51s\n",
      "       555           1.3210            8.49s\n",
      "       556           1.3209            8.48s\n",
      "       557           1.3208            8.46s\n",
      "       558           1.3207            8.44s\n",
      "       559           1.3206            8.42s\n",
      "       560           1.3205            8.40s\n",
      "       561           1.3204            8.38s\n",
      "       562           1.3204            8.36s\n",
      "       563           1.3203            8.34s\n",
      "       564           1.3202            8.32s\n",
      "       565           1.3202            8.30s\n",
      "       566           1.3200            8.28s\n",
      "       567           1.3200            8.26s\n",
      "       568           1.3199            8.25s\n",
      "       569           1.3197            8.23s\n",
      "       570           1.3196            8.21s\n",
      "       571           1.3194            8.19s\n",
      "       572           1.3193            8.17s\n",
      "       573           1.3193            8.15s\n",
      "       574           1.3192            8.13s\n",
      "       575           1.3191            8.11s\n",
      "       576           1.3191            8.09s\n",
      "       577           1.3189            8.07s\n",
      "       578           1.3188            8.05s\n",
      "       579           1.3187            8.03s\n",
      "       580           1.3187            8.02s\n",
      "       581           1.3185            8.00s\n",
      "       582           1.3185            7.98s\n",
      "       583           1.3184            7.96s\n",
      "       584           1.3183            7.94s\n",
      "       585           1.3181            7.92s\n",
      "       586           1.3181            7.90s\n",
      "       587           1.3180            7.88s\n",
      "       588           1.3179            7.86s\n",
      "       589           1.3179            7.84s\n",
      "       590           1.3178            7.82s\n",
      "       591           1.3178            7.80s\n",
      "       592           1.3177            7.79s\n",
      "       593           1.3175            7.77s\n",
      "       594           1.3174            7.75s\n",
      "       595           1.3173            7.73s\n",
      "       596           1.3171            7.71s\n",
      "       597           1.3170            7.69s\n",
      "       598           1.3169            7.67s\n",
      "       599           1.3169            7.65s\n",
      "       600           1.3168            7.63s\n",
      "       601           1.3168            7.61s\n",
      "       602           1.3167            7.59s\n",
      "       603           1.3165            7.57s\n",
      "       604           1.3164            7.56s\n",
      "       605           1.3163            7.54s\n",
      "       606           1.3162            7.52s\n",
      "       607           1.3162            7.50s\n",
      "       608           1.3161            7.48s\n",
      "       609           1.3159            7.46s\n",
      "       610           1.3159            7.44s\n",
      "       611           1.3158            7.42s\n",
      "       612           1.3157            7.40s\n",
      "       613           1.3157            7.39s\n",
      "       614           1.3156            7.37s\n",
      "       615           1.3156            7.35s\n",
      "       616           1.3155            7.33s\n",
      "       617           1.3154            7.31s\n",
      "       618           1.3153            7.29s\n",
      "       619           1.3151            7.27s\n",
      "       620           1.3150            7.25s\n",
      "       621           1.3149            7.23s\n",
      "       622           1.3148            7.21s\n",
      "       623           1.3147            7.19s\n",
      "       624           1.3145            7.18s\n",
      "       625           1.3145            7.16s\n",
      "       626           1.3143            7.14s\n",
      "       627           1.3143            7.12s\n",
      "       628           1.3142            7.10s\n",
      "       629           1.3141            7.08s\n",
      "       630           1.3140            7.06s\n",
      "       631           1.3139            7.04s\n",
      "       632           1.3138            7.02s\n",
      "       633           1.3137            7.00s\n",
      "       634           1.3136            6.99s\n",
      "       635           1.3135            6.97s\n",
      "       636           1.3135            6.95s\n",
      "       637           1.3134            6.93s\n",
      "       638           1.3134            6.91s\n",
      "       639           1.3133            6.89s\n",
      "       640           1.3131            6.87s\n",
      "       641           1.3130            6.85s\n",
      "       642           1.3130            6.83s\n",
      "       643           1.3129            6.82s\n",
      "       644           1.3128            6.80s\n",
      "       645           1.3127            6.78s\n",
      "       646           1.3126            6.76s\n",
      "       647           1.3125            6.74s\n",
      "       648           1.3124            6.72s\n",
      "       649           1.3123            6.70s\n",
      "       650           1.3122            6.68s\n",
      "       651           1.3121            6.66s\n",
      "       652           1.3120            6.64s\n",
      "       653           1.3119            6.62s\n",
      "       654           1.3118            6.61s\n",
      "       655           1.3117            6.59s\n",
      "       656           1.3117            6.57s\n",
      "       657           1.3116            6.55s\n",
      "       658           1.3115            6.53s\n",
      "       659           1.3114            6.51s\n",
      "       660           1.3112            6.49s\n",
      "       661           1.3111            6.47s\n",
      "       662           1.3110            6.45s\n",
      "       663           1.3109            6.43s\n",
      "       664           1.3108            6.41s\n",
      "       665           1.3107            6.40s\n",
      "       666           1.3106            6.38s\n",
      "       667           1.3105            6.36s\n",
      "       668           1.3104            6.34s\n",
      "       669           1.3103            6.32s\n",
      "       670           1.3101            6.30s\n",
      "       671           1.3101            6.28s\n",
      "       672           1.3100            6.26s\n",
      "       673           1.3100            6.24s\n",
      "       674           1.3099            6.22s\n",
      "       675           1.3099            6.20s\n",
      "       676           1.3098            6.19s\n",
      "       677           1.3097            6.17s\n",
      "       678           1.3096            6.15s\n",
      "       679           1.3095            6.13s\n",
      "       680           1.3093            6.11s\n",
      "       681           1.3093            6.09s\n",
      "       682           1.3092            6.07s\n",
      "       683           1.3090            6.05s\n",
      "       684           1.3089            6.03s\n",
      "       685           1.3088            6.02s\n",
      "       686           1.3087            6.00s\n",
      "       687           1.3087            5.98s\n",
      "       688           1.3086            5.96s\n",
      "       689           1.3085            5.94s\n",
      "       690           1.3084            5.92s\n",
      "       691           1.3084            5.90s\n",
      "       692           1.3083            5.88s\n",
      "       693           1.3082            5.86s\n",
      "       694           1.3081            5.85s\n",
      "       695           1.3079            5.83s\n",
      "       696           1.3078            5.81s\n",
      "       697           1.3077            5.79s\n",
      "       698           1.3076            5.77s\n",
      "       699           1.3076            5.75s\n",
      "       700           1.3075            5.73s\n",
      "       701           1.3073            5.71s\n",
      "       702           1.3072            5.69s\n",
      "       703           1.3071            5.68s\n",
      "       704           1.3070            5.66s\n",
      "       705           1.3070            5.64s\n",
      "       706           1.3069            5.62s\n",
      "       707           1.3069            5.60s\n",
      "       708           1.3068            5.58s\n",
      "       709           1.3067            5.56s\n",
      "       710           1.3065            5.54s\n",
      "       711           1.3065            5.52s\n",
      "       712           1.3065            5.50s\n",
      "       713           1.3064            5.48s\n",
      "       714           1.3063            5.47s\n",
      "       715           1.3062            5.45s\n",
      "       716           1.3061            5.43s\n",
      "       717           1.3060            5.41s\n",
      "       718           1.3059            5.39s\n",
      "       719           1.3058            5.37s\n",
      "       720           1.3058            5.35s\n",
      "       721           1.3057            5.33s\n",
      "       722           1.3056            5.31s\n",
      "       723           1.3055            5.29s\n",
      "       724           1.3054            5.27s\n",
      "       725           1.3054            5.25s\n",
      "       726           1.3053            5.24s\n",
      "       727           1.3052            5.22s\n",
      "       728           1.3051            5.20s\n",
      "       729           1.3050            5.18s\n",
      "       730           1.3049            5.16s\n",
      "       731           1.3049            5.14s\n",
      "       732           1.3047            5.12s\n",
      "       733           1.3047            5.10s\n",
      "       734           1.3046            5.08s\n",
      "       735           1.3046            5.06s\n",
      "       736           1.3044            5.04s\n",
      "       737           1.3043            5.03s\n",
      "       738           1.3043            5.01s\n",
      "       739           1.3042            4.99s\n",
      "       740           1.3042            4.97s\n",
      "       741           1.3040            4.95s\n",
      "       742           1.3039            4.93s\n",
      "       743           1.3039            4.91s\n",
      "       744           1.3037            4.89s\n",
      "       745           1.3036            4.87s\n",
      "       746           1.3036            4.85s\n",
      "       747           1.3035            4.83s\n",
      "       748           1.3034            4.82s\n",
      "       749           1.3033            4.80s\n",
      "       750           1.3032            4.78s\n",
      "       751           1.3031            4.76s\n",
      "       752           1.3030            4.74s\n",
      "       753           1.3029            4.72s\n",
      "       754           1.3029            4.70s\n",
      "       755           1.3028            4.68s\n",
      "       756           1.3027            4.66s\n",
      "       757           1.3027            4.64s\n",
      "       758           1.3026            4.62s\n",
      "       759           1.3025            4.61s\n",
      "       760           1.3023            4.59s\n",
      "       761           1.3022            4.57s\n",
      "       762           1.3022            4.55s\n",
      "       763           1.3021            4.53s\n",
      "       764           1.3020            4.51s\n",
      "       765           1.3019            4.49s\n",
      "       766           1.3018            4.47s\n",
      "       767           1.3017            4.45s\n",
      "       768           1.3017            4.43s\n",
      "       769           1.3016            4.41s\n",
      "       770           1.3016            4.40s\n",
      "       771           1.3014            4.38s\n",
      "       772           1.3013            4.36s\n",
      "       773           1.3012            4.34s\n",
      "       774           1.3011            4.32s\n",
      "       775           1.3010            4.30s\n",
      "       776           1.3008            4.28s\n",
      "       777           1.3008            4.26s\n",
      "       778           1.3006            4.24s\n",
      "       779           1.3005            4.22s\n",
      "       780           1.3004            4.20s\n",
      "       781           1.3004            4.19s\n",
      "       782           1.3003            4.17s\n",
      "       783           1.3002            4.15s\n",
      "       784           1.3002            4.13s\n",
      "       785           1.3001            4.11s\n",
      "       786           1.3000            4.09s\n",
      "       787           1.2999            4.07s\n",
      "       788           1.2998            4.05s\n",
      "       789           1.2997            4.03s\n",
      "       790           1.2997            4.01s\n",
      "       791           1.2996            4.00s\n",
      "       792           1.2995            3.98s\n",
      "       793           1.2994            3.96s\n",
      "       794           1.2994            3.94s\n",
      "       795           1.2992            3.92s\n",
      "       796           1.2991            3.90s\n",
      "       797           1.2990            3.88s\n",
      "       798           1.2989            3.86s\n",
      "       799           1.2988            3.84s\n",
      "       800           1.2987            3.82s\n",
      "       801           1.2987            3.80s\n",
      "       802           1.2986            3.78s\n",
      "       803           1.2986            3.77s\n",
      "       804           1.2985            3.75s\n",
      "       805           1.2984            3.73s\n",
      "       806           1.2983            3.71s\n",
      "       807           1.2982            3.69s\n",
      "       808           1.2981            3.67s\n",
      "       809           1.2980            3.65s\n",
      "       810           1.2979            3.63s\n",
      "       811           1.2978            3.61s\n",
      "       812           1.2977            3.59s\n",
      "       813           1.2976            3.57s\n",
      "       814           1.2976            3.56s\n",
      "       815           1.2975            3.54s\n",
      "       816           1.2974            3.52s\n",
      "       817           1.2973            3.50s\n",
      "       818           1.2972            3.48s\n",
      "       819           1.2972            3.46s\n",
      "       820           1.2970            3.44s\n",
      "       821           1.2970            3.42s\n",
      "       822           1.2969            3.40s\n",
      "       823           1.2968            3.38s\n",
      "       824           1.2967            3.36s\n",
      "       825           1.2966            3.35s\n",
      "       826           1.2965            3.33s\n",
      "       827           1.2965            3.31s\n",
      "       828           1.2964            3.29s\n",
      "       829           1.2963            3.27s\n",
      "       830           1.2962            3.25s\n",
      "       831           1.2961            3.23s\n",
      "       832           1.2961            3.21s\n",
      "       833           1.2959            3.19s\n",
      "       834           1.2958            3.17s\n",
      "       835           1.2957            3.15s\n",
      "       836           1.2956            3.13s\n",
      "       837           1.2955            3.12s\n",
      "       838           1.2954            3.10s\n",
      "       839           1.2954            3.08s\n",
      "       840           1.2952            3.06s\n",
      "       841           1.2952            3.04s\n",
      "       842           1.2951            3.02s\n",
      "       843           1.2950            3.00s\n",
      "       844           1.2949            2.98s\n",
      "       845           1.2948            2.96s\n",
      "       846           1.2947            2.94s\n",
      "       847           1.2946            2.92s\n",
      "       848           1.2945            2.91s\n",
      "       849           1.2945            2.89s\n",
      "       850           1.2944            2.87s\n",
      "       851           1.2942            2.85s\n",
      "       852           1.2942            2.83s\n",
      "       853           1.2941            2.81s\n",
      "       854           1.2940            2.79s\n",
      "       855           1.2940            2.77s\n",
      "       856           1.2938            2.75s\n",
      "       857           1.2937            2.73s\n",
      "       858           1.2937            2.71s\n",
      "       859           1.2936            2.69s\n",
      "       860           1.2936            2.68s\n",
      "       861           1.2935            2.66s\n",
      "       862           1.2934            2.64s\n",
      "       863           1.2933            2.62s\n",
      "       864           1.2933            2.60s\n",
      "       865           1.2932            2.58s\n",
      "       866           1.2930            2.56s\n",
      "       867           1.2929            2.54s\n",
      "       868           1.2929            2.52s\n",
      "       869           1.2928            2.50s\n",
      "       870           1.2927            2.49s\n",
      "       871           1.2926            2.47s\n",
      "       872           1.2925            2.45s\n",
      "       873           1.2924            2.43s\n",
      "       874           1.2923            2.41s\n",
      "       875           1.2922            2.39s\n",
      "       876           1.2921            2.37s\n",
      "       877           1.2921            2.35s\n",
      "       878           1.2920            2.33s\n",
      "       879           1.2919            2.31s\n",
      "       880           1.2918            2.29s\n",
      "       881           1.2918            2.27s\n",
      "       882           1.2917            2.26s\n",
      "       883           1.2916            2.24s\n",
      "       884           1.2916            2.22s\n",
      "       885           1.2915            2.20s\n",
      "       886           1.2915            2.18s\n",
      "       887           1.2913            2.16s\n",
      "       888           1.2912            2.14s\n",
      "       889           1.2912            2.12s\n",
      "       890           1.2911            2.10s\n",
      "       891           1.2910            2.08s\n",
      "       892           1.2909            2.06s\n",
      "       893           1.2908            2.05s\n",
      "       894           1.2907            2.03s\n",
      "       895           1.2906            2.01s\n",
      "       896           1.2905            1.99s\n",
      "       897           1.2905            1.97s\n",
      "       898           1.2905            1.95s\n",
      "       899           1.2904            1.93s\n",
      "       900           1.2903            1.91s\n",
      "       901           1.2902            1.89s\n",
      "       902           1.2901            1.87s\n",
      "       903           1.2900            1.85s\n",
      "       904           1.2898            1.83s\n",
      "       905           1.2897            1.82s\n",
      "       906           1.2896            1.80s\n",
      "       907           1.2896            1.78s\n",
      "       908           1.2895            1.76s\n",
      "       909           1.2895            1.74s\n",
      "       910           1.2894            1.72s\n",
      "       911           1.2893            1.70s\n",
      "       912           1.2893            1.68s\n",
      "       913           1.2892            1.66s\n",
      "       914           1.2891            1.64s\n",
      "       915           1.2890            1.62s\n",
      "       916           1.2889            1.61s\n",
      "       917           1.2888            1.59s\n",
      "       918           1.2887            1.57s\n",
      "       919           1.2886            1.55s\n",
      "       920           1.2885            1.53s\n",
      "       921           1.2883            1.51s\n",
      "       922           1.2882            1.49s\n",
      "       923           1.2882            1.47s\n",
      "       924           1.2881            1.45s\n",
      "       925           1.2881            1.43s\n",
      "       926           1.2879            1.41s\n",
      "       927           1.2879            1.40s\n",
      "       928           1.2878            1.38s\n",
      "       929           1.2877            1.36s\n",
      "       930           1.2876            1.34s\n",
      "       931           1.2875            1.32s\n",
      "       932           1.2875            1.30s\n",
      "       933           1.2874            1.28s\n",
      "       934           1.2873            1.26s\n",
      "       935           1.2872            1.24s\n",
      "       936           1.2871            1.22s\n",
      "       937           1.2870            1.20s\n",
      "       938           1.2870            1.18s\n",
      "       939           1.2869            1.17s\n",
      "       940           1.2869            1.15s\n",
      "       941           1.2868            1.13s\n",
      "       942           1.2867            1.11s\n",
      "       943           1.2867            1.09s\n",
      "       944           1.2866            1.07s\n",
      "       945           1.2865            1.05s\n",
      "       946           1.2865            1.03s\n",
      "       947           1.2864            1.01s\n",
      "       948           1.2863            0.99s\n",
      "       949           1.2862            0.97s\n",
      "       950           1.2860            0.96s\n",
      "       951           1.2859            0.94s\n",
      "       952           1.2858            0.92s\n",
      "       953           1.2857            0.90s\n",
      "       954           1.2856            0.88s\n",
      "       955           1.2856            0.86s\n",
      "       956           1.2855            0.84s\n",
      "       957           1.2854            0.82s\n",
      "       958           1.2854            0.80s\n",
      "       959           1.2853            0.78s\n",
      "       960           1.2853            0.76s\n",
      "       961           1.2852            0.75s\n",
      "       962           1.2851            0.73s\n",
      "       963           1.2851            0.71s\n",
      "       964           1.2850            0.69s\n",
      "       965           1.2849            0.67s\n",
      "       966           1.2848            0.65s\n",
      "       967           1.2847            0.63s\n",
      "       968           1.2846            0.61s\n",
      "       969           1.2845            0.59s\n",
      "       970           1.2844            0.57s\n",
      "       971           1.2843            0.55s\n",
      "       972           1.2842            0.54s\n",
      "       973           1.2842            0.52s\n",
      "       974           1.2840            0.50s\n",
      "       975           1.2840            0.48s\n",
      "       976           1.2839            0.46s\n",
      "       977           1.2839            0.44s\n",
      "       978           1.2838            0.42s\n",
      "       979           1.2837            0.40s\n",
      "       980           1.2836            0.38s\n",
      "       981           1.2835            0.36s\n",
      "       982           1.2834            0.34s\n",
      "       983           1.2832            0.32s\n",
      "       984           1.2831            0.31s\n",
      "       985           1.2831            0.29s\n",
      "       986           1.2831            0.27s\n",
      "       987           1.2830            0.25s\n",
      "       988           1.2828            0.23s\n",
      "       989           1.2828            0.21s\n",
      "       990           1.2827            0.19s\n",
      "       991           1.2826            0.17s\n",
      "       992           1.2825            0.15s\n",
      "       993           1.2824            0.13s\n",
      "       994           1.2823            0.11s\n",
      "       995           1.2822            0.10s\n",
      "       996           1.2822            0.08s\n",
      "       997           1.2821            0.06s\n",
      "       998           1.2820            0.04s\n",
      "       999           1.2819            0.02s\n",
      "      1000           1.2818            0.00s\n",
      "Accuracy: 0.51 (+/- 0.03)\n",
      "      Iter       Train Loss   Remaining Time \n",
      "         1           1.3861           24.98s\n",
      "         2           1.3859           24.45s\n",
      "         3           1.3857           23.93s\n",
      "         4           1.3855           23.90s\n",
      "         5           1.3853           23.88s\n",
      "         6           1.3851           24.02s\n",
      "         7           1.3849           24.12s\n",
      "         8           1.3847           24.06s\n",
      "         9           1.3845           24.00s\n",
      "        10           1.3844           23.96s\n",
      "        11           1.3842           23.92s\n",
      "        12           1.3840           23.88s\n",
      "        13           1.3838           23.84s\n",
      "        14           1.3837           23.80s\n",
      "        15           1.3835           23.77s\n",
      "        16           1.3834           23.74s\n",
      "        17           1.3832           23.71s\n",
      "        18           1.3831           23.68s\n",
      "        19           1.3829           23.60s\n",
      "        20           1.3828           23.57s\n",
      "        21           1.3826           23.54s\n",
      "        22           1.3825           23.52s\n",
      "        23           1.3823           23.49s\n",
      "        24           1.3822           23.46s\n",
      "        25           1.3820           23.44s\n",
      "        26           1.3819           23.41s\n",
      "        27           1.3817           23.57s\n",
      "        28           1.3816           23.57s\n",
      "        29           1.3814           23.54s\n",
      "        30           1.3813           23.51s\n",
      "        31           1.3812           23.44s\n",
      "        32           1.3810           23.41s\n",
      "        33           1.3809           23.41s\n",
      "        34           1.3808           23.38s\n",
      "        35           1.3806           23.33s\n",
      "        36           1.3805           23.30s\n",
      "        37           1.3804           23.27s\n",
      "        38           1.3802           23.24s\n",
      "        39           1.3801           23.24s\n",
      "        40           1.3800           23.18s\n",
      "        41           1.3799           23.16s\n",
      "        42           1.3797           23.15s\n",
      "        43           1.3796           23.12s\n",
      "        44           1.3795           23.10s\n",
      "        45           1.3794           23.07s\n",
      "        46           1.3793           23.02s\n",
      "        47           1.3791           22.99s\n",
      "        48           1.3790           22.97s\n",
      "        49           1.3789           22.94s\n",
      "        50           1.3788           22.89s\n",
      "        51           1.3787           22.87s\n",
      "        52           1.3786           22.86s\n",
      "        53           1.3785           22.82s\n",
      "        54           1.3784           22.81s\n",
      "        55           1.3783           22.78s\n",
      "        56           1.3782           22.76s\n",
      "        57           1.3781           22.73s\n",
      "        58           1.3780           22.71s\n",
      "        59           1.3779           22.68s\n",
      "        60           1.3778           22.67s\n",
      "        61           1.3777           22.64s\n",
      "        62           1.3776           22.63s\n",
      "        63           1.3775           22.61s\n",
      "        64           1.3774           22.58s\n",
      "        65           1.3773           22.57s\n",
      "        66           1.3772           22.54s\n",
      "        67           1.3771           22.52s\n",
      "        68           1.3770           22.51s\n",
      "        69           1.3769           22.48s\n",
      "        70           1.3768           22.47s\n",
      "        71           1.3767           22.44s\n",
      "        72           1.3766           22.41s\n",
      "        73           1.3765           22.39s\n",
      "        74           1.3765           22.36s\n",
      "        75           1.3764           22.35s\n",
      "        76           1.3763           22.32s\n",
      "        77           1.3762           22.30s\n",
      "        78           1.3761           22.28s\n",
      "        79           1.3760           22.26s\n",
      "        80           1.3759           22.23s\n",
      "        81           1.3759           22.20s\n",
      "        82           1.3758           22.19s\n",
      "        83           1.3757           22.16s\n",
      "        84           1.3756           22.14s\n",
      "        85           1.3755           22.11s\n",
      "        86           1.3755           22.08s\n",
      "        87           1.3754           22.06s\n",
      "        88           1.3753           22.04s\n",
      "        89           1.3752           22.03s\n",
      "        90           1.3751           22.00s\n",
      "        91           1.3751           22.02s\n",
      "        92           1.3750           22.00s\n",
      "        93           1.3749           21.97s\n",
      "        94           1.3749           21.95s\n",
      "        95           1.3747           21.92s\n",
      "        96           1.3746           21.89s\n",
      "        97           1.3745           21.87s\n",
      "        98           1.3744           21.84s\n",
      "        99           1.3743           21.82s\n",
      "       100           1.3742           21.80s\n",
      "       101           1.3741           21.77s\n",
      "       102           1.3740           21.75s\n",
      "       103           1.3739           21.72s\n",
      "       104           1.3738           21.69s\n",
      "       105           1.3737           21.68s\n",
      "       106           1.3736           21.66s\n",
      "       107           1.3735           21.63s\n",
      "       108           1.3734           21.61s\n",
      "       109           1.3733           21.59s\n",
      "       110           1.3733           21.56s\n",
      "       111           1.3731           21.54s\n",
      "       112           1.3730           21.52s\n",
      "       113           1.3729           21.50s\n",
      "       114           1.3728           21.47s\n",
      "       115           1.3728           21.45s\n",
      "       116           1.3727           21.42s\n",
      "       117           1.3726           21.40s\n",
      "       118           1.3725           21.37s\n",
      "       119           1.3724           21.35s\n",
      "       120           1.3723           21.33s\n",
      "       121           1.3722           21.31s\n",
      "       122           1.3721           21.28s\n",
      "       123           1.3720           21.25s\n",
      "       124           1.3719           21.24s\n",
      "       125           1.3719           21.21s\n",
      "       126           1.3717           21.18s\n",
      "       127           1.3717           21.17s\n",
      "       128           1.3716           21.14s\n",
      "       129           1.3715           21.11s\n",
      "       130           1.3714           21.09s\n",
      "       131           1.3713           21.06s\n",
      "       132           1.3712           21.04s\n",
      "       133           1.3710           21.02s\n",
      "       134           1.3710           20.99s\n",
      "       135           1.3709           20.97s\n",
      "       136           1.3709           20.95s\n",
      "       137           1.3707           20.93s\n",
      "       138           1.3707           20.90s\n",
      "       139           1.3706           20.87s\n",
      "       140           1.3705           20.85s\n",
      "       141           1.3704           20.82s\n",
      "       142           1.3703           20.80s\n",
      "       143           1.3703           20.78s\n",
      "       144           1.3701           20.75s\n",
      "       145           1.3700           20.73s\n",
      "       146           1.3699           20.70s\n",
      "       147           1.3698           20.68s\n",
      "       148           1.3697           20.65s\n",
      "       149           1.3696           20.63s\n",
      "       150           1.3695           20.61s\n",
      "       151           1.3695           20.60s\n",
      "       152           1.3694           20.58s\n",
      "       153           1.3693           20.55s\n",
      "       154           1.3692           20.53s\n",
      "       155           1.3691           20.51s\n",
      "       156           1.3690           20.48s\n",
      "       157           1.3689           20.46s\n",
      "       158           1.3688           20.43s\n",
      "       159           1.3687           20.41s\n",
      "       160           1.3686           20.39s\n",
      "       161           1.3686           20.36s\n",
      "       162           1.3685           20.33s\n",
      "       163           1.3684           20.32s\n",
      "       164           1.3683           20.30s\n",
      "       165           1.3682           20.27s\n",
      "       166           1.3681           20.24s\n",
      "       167           1.3680           20.22s\n",
      "       168           1.3678           20.19s\n",
      "       169           1.3677           20.17s\n",
      "       170           1.3677           20.14s\n",
      "       171           1.3676           20.12s\n",
      "       172           1.3675           20.09s\n",
      "       173           1.3674           20.07s\n",
      "       174           1.3673           20.04s\n",
      "       175           1.3672           20.02s\n",
      "       176           1.3671           19.99s\n",
      "       177           1.3669           19.97s\n",
      "       178           1.3669           19.94s\n",
      "       179           1.3668           19.91s\n",
      "       180           1.3666           19.89s\n",
      "       181           1.3666           19.87s\n",
      "       182           1.3665           19.84s\n",
      "       183           1.3665           19.82s\n",
      "       184           1.3664           19.79s\n",
      "       185           1.3663           19.76s\n",
      "       186           1.3662           19.74s\n",
      "       187           1.3660           19.71s\n",
      "       188           1.3659           19.69s\n",
      "       189           1.3658           19.66s\n",
      "       190           1.3658           19.64s\n",
      "       191           1.3656           19.61s\n",
      "       192           1.3655           19.59s\n",
      "       193           1.3655           19.56s\n",
      "       194           1.3654           19.53s\n",
      "       195           1.3652           19.51s\n",
      "       196           1.3652           19.48s\n",
      "       197           1.3650           19.46s\n",
      "       198           1.3649           19.43s\n",
      "       199           1.3649           19.41s\n",
      "       200           1.3647           19.38s\n",
      "       201           1.3646           19.36s\n",
      "       202           1.3646           19.33s\n",
      "       203           1.3645           19.30s\n",
      "       204           1.3644           19.28s\n",
      "       205           1.3643           19.26s\n",
      "       206           1.3642           19.24s\n",
      "       207           1.3641           19.21s\n",
      "       208           1.3640           19.19s\n",
      "       209           1.3639           19.17s\n",
      "       210           1.3638           19.14s\n",
      "       211           1.3637           19.12s\n",
      "       212           1.3636           19.09s\n",
      "       213           1.3636           19.07s\n",
      "       214           1.3636           19.05s\n",
      "       215           1.3634           19.02s\n",
      "       216           1.3634           19.00s\n",
      "       217           1.3632           18.97s\n",
      "       218           1.3631           18.95s\n",
      "       219           1.3630           18.92s\n",
      "       220           1.3629           18.90s\n",
      "       221           1.3628           18.88s\n",
      "       222           1.3628           18.85s\n",
      "       223           1.3627           18.83s\n",
      "       224           1.3627           18.81s\n",
      "       225           1.3625           18.78s\n",
      "       226           1.3625           18.76s\n",
      "       227           1.3623           18.75s\n",
      "       228           1.3622           18.73s\n",
      "       229           1.3621           18.70s\n",
      "       230           1.3620           18.68s\n",
      "       231           1.3620           18.65s\n",
      "       232           1.3619           18.63s\n",
      "       233           1.3619           18.61s\n",
      "       234           1.3618           18.58s\n",
      "       235           1.3617           18.56s\n",
      "       236           1.3615           18.53s\n",
      "       237           1.3614           18.51s\n",
      "       238           1.3613           18.48s\n",
      "       239           1.3613           18.46s\n",
      "       240           1.3612           18.43s\n",
      "       241           1.3611           18.41s\n",
      "       242           1.3610           18.38s\n",
      "       243           1.3609           18.36s\n",
      "       244           1.3608           18.33s\n",
      "       245           1.3607           18.31s\n",
      "       246           1.3606           18.28s\n",
      "       247           1.3605           18.26s\n",
      "       248           1.3604           18.23s\n",
      "       249           1.3604           18.21s\n",
      "       250           1.3603           18.18s\n",
      "       251           1.3602           18.16s\n",
      "       252           1.3601           18.14s\n",
      "       253           1.3599           18.11s\n",
      "       254           1.3598           18.08s\n",
      "       255           1.3597           18.06s\n",
      "       256           1.3597           18.03s\n",
      "       257           1.3596           18.01s\n",
      "       258           1.3596           17.98s\n",
      "       259           1.3595           17.96s\n",
      "       260           1.3595           17.94s\n",
      "       261           1.3593           17.91s\n",
      "       262           1.3592           17.89s\n",
      "       263           1.3590           17.86s\n",
      "       264           1.3590           17.84s\n",
      "       265           1.3589           17.81s\n",
      "       266           1.3589           17.79s\n",
      "       267           1.3588           17.77s\n",
      "       268           1.3587           17.74s\n",
      "       269           1.3586           17.72s\n",
      "       270           1.3585           17.69s\n",
      "       271           1.3584           17.67s\n",
      "       272           1.3583           17.64s\n",
      "       273           1.3582           17.62s\n",
      "       274           1.3581           17.59s\n",
      "       275           1.3580           17.57s\n",
      "       276           1.3579           17.54s\n",
      "       277           1.3578           17.52s\n",
      "       278           1.3577           17.49s\n",
      "       279           1.3576           17.47s\n",
      "       280           1.3575           17.44s\n",
      "       281           1.3574           17.42s\n",
      "       282           1.3573           17.39s\n",
      "       283           1.3573           17.37s\n",
      "       284           1.3572           17.35s\n",
      "       285           1.3571           17.32s\n",
      "       286           1.3570           17.30s\n",
      "       287           1.3569           17.28s\n",
      "       288           1.3567           17.25s\n",
      "       289           1.3566           17.23s\n",
      "       290           1.3566           17.21s\n",
      "       291           1.3565           17.19s\n",
      "       292           1.3565           17.16s\n",
      "       293           1.3563           17.14s\n",
      "       294           1.3563           17.11s\n",
      "       295           1.3562           17.09s\n",
      "       296           1.3560           17.06s\n",
      "       297           1.3559           17.04s\n",
      "       298           1.3558           17.02s\n",
      "       299           1.3556           16.99s\n",
      "       300           1.3555           16.97s\n",
      "       301           1.3554           16.94s\n",
      "       302           1.3553           16.92s\n",
      "       303           1.3553           16.89s\n",
      "       304           1.3552           16.87s\n",
      "       305           1.3550           16.85s\n",
      "       306           1.3549           16.82s\n",
      "       307           1.3548           16.80s\n",
      "       308           1.3547           16.77s\n",
      "       309           1.3547           16.75s\n",
      "       310           1.3546           16.72s\n",
      "       311           1.3545           16.70s\n",
      "       312           1.3544           16.68s\n",
      "       313           1.3543           16.65s\n",
      "       314           1.3543           16.63s\n",
      "       315           1.3542           16.60s\n",
      "       316           1.3540           16.58s\n",
      "       317           1.3539           16.55s\n",
      "       318           1.3538           16.53s\n",
      "       319           1.3537           16.50s\n",
      "       320           1.3537           16.48s\n",
      "       321           1.3535           16.45s\n",
      "       322           1.3534           16.43s\n",
      "       323           1.3534           16.40s\n",
      "       324           1.3532           16.38s\n",
      "       325           1.3531           16.35s\n",
      "       326           1.3530           16.33s\n",
      "       327           1.3530           16.30s\n",
      "       328           1.3529           16.28s\n",
      "       329           1.3528           16.25s\n",
      "       330           1.3527           16.23s\n",
      "       331           1.3526           16.21s\n",
      "       332           1.3524           16.18s\n",
      "       333           1.3523           16.16s\n",
      "       334           1.3522           16.13s\n",
      "       335           1.3522           16.11s\n",
      "       336           1.3520           16.08s\n",
      "       337           1.3519           16.06s\n",
      "       338           1.3519           16.03s\n",
      "       339           1.3518           16.01s\n",
      "       340           1.3517           15.98s\n",
      "       341           1.3516           15.96s\n",
      "       342           1.3515           15.93s\n",
      "       343           1.3514           15.91s\n",
      "       344           1.3514           15.89s\n",
      "       345           1.3513           15.86s\n",
      "       346           1.3512           15.83s\n",
      "       347           1.3512           15.81s\n",
      "       348           1.3510           15.79s\n",
      "       349           1.3509           15.76s\n",
      "       350           1.3508           15.74s\n",
      "       351           1.3506           15.71s\n",
      "       352           1.3505           15.70s\n",
      "       353           1.3504           15.67s\n",
      "       354           1.3503           15.65s\n",
      "       355           1.3502           15.63s\n",
      "       356           1.3502           15.60s\n",
      "       357           1.3501           15.58s\n",
      "       358           1.3500           15.55s\n",
      "       359           1.3499           15.53s\n",
      "       360           1.3498           15.50s\n",
      "       361           1.3497           15.48s\n",
      "       362           1.3495           15.45s\n",
      "       363           1.3494           15.43s\n",
      "       364           1.3493           15.41s\n",
      "       365           1.3492           15.38s\n",
      "       366           1.3490           15.36s\n",
      "       367           1.3490           15.33s\n",
      "       368           1.3489           15.31s\n",
      "       369           1.3489           15.29s\n",
      "       370           1.3488           15.27s\n",
      "       371           1.3487           15.24s\n",
      "       372           1.3486           15.22s\n",
      "       373           1.3485           15.19s\n",
      "       374           1.3484           15.17s\n",
      "       375           1.3483           15.15s\n",
      "       376           1.3482           15.12s\n",
      "       377           1.3481           15.10s\n",
      "       378           1.3480           15.07s\n",
      "       379           1.3478           15.05s\n",
      "       380           1.3477           15.03s\n",
      "       381           1.3476           15.00s\n",
      "       382           1.3476           14.98s\n",
      "       383           1.3475           14.95s\n",
      "       384           1.3474           14.93s\n",
      "       385           1.3473           14.90s\n",
      "       386           1.3472           14.88s\n",
      "       387           1.3471           14.86s\n",
      "       388           1.3470           14.83s\n",
      "       389           1.3469           14.81s\n",
      "       390           1.3468           14.78s\n",
      "       391           1.3468           14.76s\n",
      "       392           1.3467           14.73s\n",
      "       393           1.3466           14.71s\n",
      "       394           1.3465           14.69s\n",
      "       395           1.3463           14.66s\n",
      "       396           1.3463           14.64s\n",
      "       397           1.3461           14.61s\n",
      "       398           1.3461           14.59s\n",
      "       399           1.3460           14.56s\n",
      "       400           1.3459           14.54s\n",
      "       401           1.3457           14.51s\n",
      "       402           1.3457           14.49s\n",
      "       403           1.3456           14.47s\n",
      "       404           1.3455           14.44s\n",
      "       405           1.3454           14.42s\n",
      "       406           1.3453           14.39s\n",
      "       407           1.3453           14.37s\n",
      "       408           1.3452           14.35s\n",
      "       409           1.3451           14.32s\n",
      "       410           1.3450           14.30s\n",
      "       411           1.3449           14.27s\n",
      "       412           1.3448           14.25s\n",
      "       413           1.3447           14.22s\n",
      "       414           1.3446           14.20s\n",
      "       415           1.3445           14.17s\n",
      "       416           1.3444           14.15s\n",
      "       417           1.3444           14.13s\n",
      "       418           1.3443           14.11s\n",
      "       419           1.3443           14.08s\n",
      "       420           1.3441           14.06s\n",
      "       421           1.3440           14.03s\n",
      "       422           1.3439           14.01s\n",
      "       423           1.3439           13.98s\n",
      "       424           1.3437           13.96s\n",
      "       425           1.3436           13.94s\n",
      "       426           1.3436           13.91s\n",
      "       427           1.3435           13.89s\n",
      "       428           1.3434           13.86s\n",
      "       429           1.3433           13.84s\n",
      "       430           1.3432           13.81s\n",
      "       431           1.3431           13.79s\n",
      "       432           1.3430           13.76s\n",
      "       433           1.3429           13.74s\n",
      "       434           1.3428           13.72s\n",
      "       435           1.3428           13.69s\n",
      "       436           1.3428           13.67s\n",
      "       437           1.3427           13.64s\n",
      "       438           1.3426           13.62s\n",
      "       439           1.3425           13.59s\n",
      "       440           1.3424           13.57s\n",
      "       441           1.3423           13.54s\n",
      "       442           1.3422           13.52s\n",
      "       443           1.3421           13.49s\n",
      "       444           1.3420           13.47s\n",
      "       445           1.3420           13.44s\n",
      "       446           1.3418           13.42s\n",
      "       447           1.3418           13.40s\n",
      "       448           1.3417           13.37s\n",
      "       449           1.3416           13.35s\n",
      "       450           1.3415           13.32s\n",
      "       451           1.3414           13.30s\n",
      "       452           1.3413           13.27s\n",
      "       453           1.3412           13.25s\n",
      "       454           1.3411           13.23s\n",
      "       455           1.3411           13.20s\n",
      "       456           1.3410           13.18s\n",
      "       457           1.3410           13.15s\n",
      "       458           1.3409           13.13s\n",
      "       459           1.3408           13.10s\n",
      "       460           1.3407           13.08s\n",
      "       461           1.3406           13.06s\n",
      "       462           1.3404           13.03s\n",
      "       463           1.3403           13.01s\n",
      "       464           1.3403           12.98s\n",
      "       465           1.3402           12.96s\n",
      "       466           1.3401           12.94s\n",
      "       467           1.3401           12.91s\n",
      "       468           1.3400           12.89s\n",
      "       469           1.3398           12.86s\n",
      "       470           1.3397           12.85s\n",
      "       471           1.3397           12.82s\n",
      "       472           1.3395           12.80s\n",
      "       473           1.3394           12.77s\n",
      "       474           1.3393           12.75s\n",
      "       475           1.3392           12.72s\n",
      "       476           1.3391           12.70s\n",
      "       477           1.3391           12.67s\n",
      "       478           1.3390           12.65s\n",
      "       479           1.3389           12.63s\n",
      "       480           1.3388           12.60s\n",
      "       481           1.3387           12.58s\n",
      "       482           1.3387           12.55s\n",
      "       483           1.3386           12.53s\n",
      "       484           1.3385           12.50s\n",
      "       485           1.3384           12.48s\n",
      "       486           1.3383           12.46s\n",
      "       487           1.3382           12.43s\n",
      "       488           1.3381           12.41s\n",
      "       489           1.3380           12.38s\n",
      "       490           1.3379           12.36s\n",
      "       491           1.3378           12.33s\n",
      "       492           1.3378           12.31s\n",
      "       493           1.3377           12.29s\n",
      "       494           1.3376           12.26s\n",
      "       495           1.3376           12.24s\n",
      "       496           1.3376           12.21s\n",
      "       497           1.3374           12.19s\n",
      "       498           1.3374           12.16s\n",
      "       499           1.3373           12.14s\n",
      "       500           1.3372           12.12s\n",
      "       501           1.3371           12.09s\n",
      "       502           1.3370           12.07s\n",
      "       503           1.3370           12.04s\n",
      "       504           1.3369           12.02s\n",
      "       505           1.3369           11.99s\n",
      "       506           1.3369           11.97s\n",
      "       507           1.3367           11.94s\n",
      "       508           1.3366           11.92s\n",
      "       509           1.3365           11.90s\n",
      "       510           1.3364           11.87s\n",
      "       511           1.3363           11.85s\n",
      "       512           1.3363           11.82s\n",
      "       513           1.3362           11.80s\n",
      "       514           1.3361           11.78s\n",
      "       515           1.3360           11.75s\n",
      "       516           1.3360           11.73s\n",
      "       517           1.3359           11.71s\n",
      "       518           1.3358           11.68s\n",
      "       519           1.3356           11.66s\n",
      "       520           1.3356           11.63s\n",
      "       521           1.3355           11.61s\n",
      "       522           1.3355           11.59s\n",
      "       523           1.3354           11.56s\n",
      "       524           1.3353           11.54s\n",
      "       525           1.3353           11.51s\n",
      "       526           1.3351           11.49s\n",
      "       527           1.3350           11.47s\n",
      "       528           1.3349           11.44s\n",
      "       529           1.3348           11.42s\n",
      "       530           1.3347           11.40s\n",
      "       531           1.3346           11.37s\n",
      "       532           1.3345           11.35s\n",
      "       533           1.3344           11.33s\n",
      "       534           1.3343           11.30s\n",
      "       535           1.3343           11.28s\n",
      "       536           1.3342           11.25s\n",
      "       537           1.3342           11.23s\n",
      "       538           1.3341           11.21s\n",
      "       539           1.3340           11.18s\n",
      "       540           1.3339           11.16s\n",
      "       541           1.3339           11.13s\n",
      "       542           1.3338           11.11s\n",
      "       543           1.3337           11.08s\n",
      "       544           1.3337           11.06s\n",
      "       545           1.3336           11.04s\n",
      "       546           1.3335           11.01s\n",
      "       547           1.3335           10.99s\n",
      "       548           1.3334           10.96s\n",
      "       549           1.3333           10.94s\n",
      "       550           1.3332           10.92s\n",
      "       551           1.3331           10.89s\n",
      "       552           1.3329           10.87s\n",
      "       553           1.3329           10.84s\n",
      "       554           1.3328           10.82s\n",
      "       555           1.3327           10.79s\n",
      "       556           1.3326           10.77s\n",
      "       557           1.3326           10.75s\n",
      "       558           1.3325           10.72s\n",
      "       559           1.3324           10.70s\n",
      "       560           1.3324           10.67s\n",
      "       561           1.3323           10.65s\n",
      "       562           1.3322           10.62s\n",
      "       563           1.3321           10.60s\n",
      "       564           1.3320           10.58s\n",
      "       565           1.3320           10.55s\n",
      "       566           1.3319           10.53s\n",
      "       567           1.3318           10.50s\n",
      "       568           1.3317           10.48s\n",
      "       569           1.3317           10.45s\n",
      "       570           1.3316           10.43s\n",
      "       571           1.3315           10.40s\n",
      "       572           1.3315           10.38s\n",
      "       573           1.3314           10.35s\n",
      "       574           1.3313           10.33s\n",
      "       575           1.3312           10.31s\n",
      "       576           1.3311           10.28s\n",
      "       577           1.3311           10.26s\n",
      "       578           1.3310           10.23s\n",
      "       579           1.3310           10.21s\n",
      "       580           1.3309           10.18s\n",
      "       581           1.3308           10.16s\n",
      "       582           1.3308           10.13s\n",
      "       583           1.3307           10.11s\n",
      "       584           1.3306           10.09s\n",
      "       585           1.3305           10.06s\n",
      "       586           1.3304           10.04s\n",
      "       587           1.3304           10.01s\n",
      "       588           1.3303            9.99s\n",
      "       589           1.3302            9.96s\n",
      "       590           1.3301            9.94s\n",
      "       591           1.3301            9.92s\n",
      "       592           1.3301            9.89s\n",
      "       593           1.3300            9.87s\n",
      "       594           1.3299            9.84s\n",
      "       595           1.3298            9.82s\n",
      "       596           1.3297            9.80s\n",
      "       597           1.3297            9.77s\n",
      "       598           1.3295            9.75s\n",
      "       599           1.3294            9.72s\n",
      "       600           1.3293            9.70s\n",
      "       601           1.3292            9.67s\n",
      "       602           1.3291            9.65s\n",
      "       603           1.3291            9.62s\n",
      "       604           1.3290            9.60s\n",
      "       605           1.3289            9.58s\n",
      "       606           1.3288            9.55s\n",
      "       607           1.3287            9.53s\n",
      "       608           1.3286            9.50s\n",
      "       609           1.3285            9.48s\n",
      "       610           1.3284            9.45s\n",
      "       611           1.3284            9.43s\n",
      "       612           1.3282            9.40s\n",
      "       613           1.3282            9.38s\n",
      "       614           1.3281            9.36s\n",
      "       615           1.3281            9.33s\n",
      "       616           1.3280            9.31s\n",
      "       617           1.3279            9.28s\n",
      "       618           1.3279            9.26s\n",
      "       619           1.3278            9.23s\n",
      "       620           1.3277            9.21s\n",
      "       621           1.3276            9.18s\n",
      "       622           1.3275            9.16s\n",
      "       623           1.3274            9.14s\n",
      "       624           1.3273            9.11s\n",
      "       625           1.3272            9.09s\n",
      "       626           1.3271            9.06s\n",
      "       627           1.3271            9.04s\n",
      "       628           1.3270            9.01s\n",
      "       629           1.3270            8.99s\n",
      "       630           1.3269            8.96s\n",
      "       631           1.3268            8.94s\n",
      "       632           1.3267            8.92s\n",
      "       633           1.3267            8.89s\n",
      "       634           1.3266            8.87s\n",
      "       635           1.3265            8.84s\n",
      "       636           1.3265            8.82s\n",
      "       637           1.3263            8.79s\n",
      "       638           1.3263            8.77s\n",
      "       639           1.3262            8.75s\n",
      "       640           1.3262            8.72s\n",
      "       641           1.3260            8.70s\n",
      "       642           1.3259            8.67s\n",
      "       643           1.3259            8.65s\n",
      "       644           1.3258            8.62s\n",
      "       645           1.3257            8.60s\n",
      "       646           1.3256            8.57s\n",
      "       647           1.3255            8.55s\n",
      "       648           1.3255            8.53s\n",
      "       649           1.3254            8.50s\n",
      "       650           1.3253            8.48s\n",
      "       651           1.3253            8.45s\n",
      "       652           1.3252            8.43s\n",
      "       653           1.3251            8.41s\n",
      "       654           1.3250            8.38s\n",
      "       655           1.3250            8.36s\n",
      "       656           1.3249            8.33s\n",
      "       657           1.3248            8.31s\n",
      "       658           1.3247            8.28s\n",
      "       659           1.3246            8.26s\n",
      "       660           1.3245            8.24s\n",
      "       661           1.3245            8.21s\n",
      "       662           1.3244            8.19s\n",
      "       663           1.3243            8.16s\n",
      "       664           1.3242            8.14s\n",
      "       665           1.3241            8.11s\n",
      "       666           1.3240            8.09s\n",
      "       667           1.3239            8.07s\n",
      "       668           1.3239            8.04s\n",
      "       669           1.3238            8.02s\n",
      "       670           1.3238            7.99s\n",
      "       671           1.3237            7.97s\n",
      "       672           1.3236            7.94s\n",
      "       673           1.3235            7.92s\n",
      "       674           1.3234            7.90s\n",
      "       675           1.3233            7.87s\n",
      "       676           1.3232            7.85s\n",
      "       677           1.3231            7.82s\n",
      "       678           1.3231            7.80s\n",
      "       679           1.3230            7.77s\n",
      "       680           1.3229            7.75s\n",
      "       681           1.3229            7.72s\n",
      "       682           1.3228            7.70s\n",
      "       683           1.3228            7.68s\n",
      "       684           1.3227            7.65s\n",
      "       685           1.3226            7.63s\n",
      "       686           1.3225            7.60s\n",
      "       687           1.3225            7.58s\n",
      "       688           1.3224            7.55s\n",
      "       689           1.3223            7.53s\n",
      "       690           1.3223            7.51s\n",
      "       691           1.3222            7.48s\n",
      "       692           1.3221            7.46s\n",
      "       693           1.3221            7.43s\n",
      "       694           1.3220            7.41s\n",
      "       695           1.3219            7.38s\n",
      "       696           1.3219            7.36s\n",
      "       697           1.3218            7.34s\n",
      "       698           1.3217            7.31s\n",
      "       699           1.3216            7.29s\n",
      "       700           1.3216            7.26s\n",
      "       701           1.3215            7.24s\n",
      "       702           1.3214            7.21s\n",
      "       703           1.3214            7.19s\n",
      "       704           1.3212            7.16s\n",
      "       705           1.3212            7.14s\n",
      "       706           1.3211            7.12s\n",
      "       707           1.3210            7.09s\n",
      "       708           1.3209            7.07s\n",
      "       709           1.3208            7.04s\n",
      "       710           1.3207            7.02s\n",
      "       711           1.3206            6.99s\n",
      "       712           1.3205            6.97s\n",
      "       713           1.3205            6.95s\n",
      "       714           1.3204            6.92s\n",
      "       715           1.3203            6.90s\n",
      "       716           1.3203            6.87s\n",
      "       717           1.3202            6.85s\n",
      "       718           1.3202            6.83s\n",
      "       719           1.3201            6.80s\n",
      "       720           1.3200            6.78s\n",
      "       721           1.3200            6.75s\n",
      "       722           1.3199            6.73s\n",
      "       723           1.3198            6.70s\n",
      "       724           1.3197            6.68s\n",
      "       725           1.3196            6.66s\n",
      "       726           1.3195            6.63s\n",
      "       727           1.3194            6.61s\n",
      "       728           1.3194            6.58s\n",
      "       729           1.3193            6.56s\n",
      "       730           1.3193            6.53s\n",
      "       731           1.3192            6.51s\n",
      "       732           1.3191            6.49s\n",
      "       733           1.3190            6.46s\n",
      "       734           1.3190            6.44s\n",
      "       735           1.3189            6.41s\n",
      "       736           1.3189            6.39s\n",
      "       737           1.3188            6.36s\n",
      "       738           1.3187            6.34s\n",
      "       739           1.3186            6.32s\n",
      "       740           1.3186            6.29s\n",
      "       741           1.3184            6.27s\n",
      "       742           1.3183            6.24s\n",
      "       743           1.3182            6.22s\n",
      "       744           1.3181            6.20s\n",
      "       745           1.3180            6.17s\n",
      "       746           1.3180            6.15s\n",
      "       747           1.3179            6.13s\n",
      "       748           1.3178            6.10s\n",
      "       749           1.3178            6.08s\n",
      "       750           1.3177            6.06s\n",
      "       751           1.3177            6.03s\n",
      "       752           1.3175            6.01s\n",
      "       753           1.3175            5.98s\n",
      "       754           1.3174            5.96s\n",
      "       755           1.3174            5.94s\n",
      "       756           1.3173            5.91s\n",
      "       757           1.3172            5.89s\n",
      "       758           1.3171            5.86s\n",
      "       759           1.3170            5.84s\n",
      "       760           1.3169            5.82s\n",
      "       761           1.3168            5.79s\n",
      "       762           1.3168            5.77s\n",
      "       763           1.3167            5.74s\n",
      "       764           1.3167            5.72s\n",
      "       765           1.3166            5.69s\n",
      "       766           1.3166            5.67s\n",
      "       767           1.3165            5.65s\n",
      "       768           1.3164            5.62s\n",
      "       769           1.3163            5.60s\n",
      "       770           1.3162            5.57s\n",
      "       771           1.3161            5.55s\n",
      "       772           1.3161            5.52s\n",
      "       773           1.3159            5.50s\n",
      "       774           1.3159            5.48s\n",
      "       775           1.3158            5.45s\n",
      "       776           1.3157            5.43s\n",
      "       777           1.3157            5.40s\n",
      "       778           1.3156            5.38s\n",
      "       779           1.3156            5.36s\n",
      "       780           1.3156            5.33s\n",
      "       781           1.3154            5.31s\n",
      "       782           1.3154            5.28s\n",
      "       783           1.3153            5.26s\n",
      "       784           1.3152            5.23s\n",
      "       785           1.3151            5.21s\n",
      "       786           1.3150            5.19s\n",
      "       787           1.3149            5.16s\n",
      "       788           1.3149            5.14s\n",
      "       789           1.3148            5.11s\n",
      "       790           1.3147            5.09s\n",
      "       791           1.3146            5.06s\n",
      "       792           1.3145            5.04s\n",
      "       793           1.3144            5.02s\n",
      "       794           1.3143            4.99s\n",
      "       795           1.3143            4.97s\n",
      "       796           1.3142            4.94s\n",
      "       797           1.3142            4.92s\n",
      "       798           1.3141            4.90s\n",
      "       799           1.3140            4.87s\n",
      "       800           1.3139            4.85s\n",
      "       801           1.3139            4.82s\n",
      "       802           1.3138            4.80s\n",
      "       803           1.3137            4.77s\n",
      "       804           1.3136            4.75s\n",
      "       805           1.3135            4.73s\n",
      "       806           1.3134            4.70s\n",
      "       807           1.3134            4.68s\n",
      "       808           1.3133            4.65s\n",
      "       809           1.3133            4.63s\n",
      "       810           1.3132            4.60s\n",
      "       811           1.3131            4.58s\n",
      "       812           1.3131            4.56s\n",
      "       813           1.3130            4.53s\n",
      "       814           1.3129            4.51s\n",
      "       815           1.3128            4.48s\n",
      "       816           1.3127            4.46s\n",
      "       817           1.3127            4.43s\n",
      "       818           1.3127            4.41s\n",
      "       819           1.3126            4.39s\n",
      "       820           1.3125            4.36s\n",
      "       821           1.3124            4.34s\n",
      "       822           1.3123            4.31s\n",
      "       823           1.3123            4.29s\n",
      "       824           1.3122            4.26s\n",
      "       825           1.3122            4.24s\n",
      "       826           1.3121            4.22s\n",
      "       827           1.3120            4.19s\n",
      "       828           1.3120            4.17s\n",
      "       829           1.3119            4.14s\n",
      "       830           1.3118            4.12s\n",
      "       831           1.3117            4.10s\n",
      "       832           1.3116            4.07s\n",
      "       833           1.3115            4.05s\n",
      "       834           1.3114            4.02s\n",
      "       835           1.3113            4.00s\n",
      "       836           1.3113            3.97s\n",
      "       837           1.3112            3.95s\n",
      "       838           1.3111            3.92s\n",
      "       839           1.3110            3.90s\n",
      "       840           1.3110            3.88s\n",
      "       841           1.3109            3.85s\n",
      "       842           1.3109            3.83s\n",
      "       843           1.3108            3.80s\n",
      "       844           1.3107            3.78s\n",
      "       845           1.3106            3.76s\n",
      "       846           1.3105            3.73s\n",
      "       847           1.3104            3.71s\n",
      "       848           1.3103            3.68s\n",
      "       849           1.3103            3.66s\n",
      "       850           1.3101            3.63s\n",
      "       851           1.3101            3.61s\n",
      "       852           1.3100            3.59s\n",
      "       853           1.3100            3.56s\n",
      "       854           1.3099            3.54s\n",
      "       855           1.3099            3.51s\n",
      "       856           1.3098            3.49s\n",
      "       857           1.3097            3.46s\n",
      "       858           1.3096            3.44s\n",
      "       859           1.3095            3.42s\n",
      "       860           1.3094            3.39s\n",
      "       861           1.3093            3.37s\n",
      "       862           1.3092            3.34s\n",
      "       863           1.3091            3.32s\n",
      "       864           1.3090            3.29s\n",
      "       865           1.3089            3.27s\n",
      "       866           1.3089            3.25s\n",
      "       867           1.3088            3.22s\n",
      "       868           1.3087            3.20s\n",
      "       869           1.3086            3.18s\n",
      "       870           1.3085            3.15s\n",
      "       871           1.3084            3.13s\n",
      "       872           1.3083            3.10s\n",
      "       873           1.3082            3.08s\n",
      "       874           1.3082            3.05s\n",
      "       875           1.3082            3.03s\n",
      "       876           1.3081            3.01s\n",
      "       877           1.3080            2.98s\n",
      "       878           1.3079            2.96s\n",
      "       879           1.3079            2.93s\n",
      "       880           1.3078            2.91s\n",
      "       881           1.3078            2.88s\n",
      "       882           1.3077            2.86s\n",
      "       883           1.3075            2.84s\n",
      "       884           1.3074            2.81s\n",
      "       885           1.3074            2.79s\n",
      "       886           1.3073            2.76s\n",
      "       887           1.3072            2.74s\n",
      "       888           1.3071            2.71s\n",
      "       889           1.3070            2.69s\n",
      "       890           1.3069            2.67s\n",
      "       891           1.3068            2.64s\n",
      "       892           1.3067            2.62s\n",
      "       893           1.3066            2.59s\n",
      "       894           1.3065            2.57s\n",
      "       895           1.3065            2.55s\n",
      "       896           1.3064            2.52s\n",
      "       897           1.3064            2.50s\n",
      "       898           1.3063            2.47s\n",
      "       899           1.3062            2.45s\n",
      "       900           1.3062            2.42s\n",
      "       901           1.3062            2.40s\n",
      "       902           1.3061            2.38s\n",
      "       903           1.3059            2.35s\n",
      "       904           1.3059            2.33s\n",
      "       905           1.3058            2.30s\n",
      "       906           1.3057            2.28s\n",
      "       907           1.3055            2.25s\n",
      "       908           1.3055            2.23s\n",
      "       909           1.3054            2.21s\n",
      "       910           1.3054            2.18s\n",
      "       911           1.3053            2.16s\n",
      "       912           1.3053            2.13s\n",
      "       913           1.3052            2.11s\n",
      "       914           1.3052            2.08s\n",
      "       915           1.3051            2.06s\n",
      "       916           1.3050            2.04s\n",
      "       917           1.3049            2.01s\n",
      "       918           1.3048            1.99s\n",
      "       919           1.3047            1.96s\n",
      "       920           1.3047            1.94s\n",
      "       921           1.3046            1.92s\n",
      "       922           1.3046            1.89s\n",
      "       923           1.3045            1.87s\n",
      "       924           1.3044            1.84s\n",
      "       925           1.3043            1.82s\n",
      "       926           1.3042            1.79s\n",
      "       927           1.3041            1.77s\n",
      "       928           1.3041            1.75s\n",
      "       929           1.3040            1.72s\n",
      "       930           1.3039            1.70s\n",
      "       931           1.3038            1.67s\n",
      "       932           1.3037            1.65s\n",
      "       933           1.3036            1.62s\n",
      "       934           1.3035            1.60s\n",
      "       935           1.3034            1.58s\n",
      "       936           1.3033            1.55s\n",
      "       937           1.3032            1.53s\n",
      "       938           1.3032            1.50s\n",
      "       939           1.3032            1.48s\n",
      "       940           1.3031            1.45s\n",
      "       941           1.3030            1.43s\n",
      "       942           1.3030            1.41s\n",
      "       943           1.3030            1.38s\n",
      "       944           1.3029            1.36s\n",
      "       945           1.3028            1.33s\n",
      "       946           1.3026            1.31s\n",
      "       947           1.3025            1.28s\n",
      "       948           1.3024            1.26s\n",
      "       949           1.3023            1.24s\n",
      "       950           1.3023            1.21s\n",
      "       951           1.3023            1.19s\n",
      "       952           1.3023            1.16s\n",
      "       953           1.3022            1.14s\n",
      "       954           1.3021            1.12s\n",
      "       955           1.3020            1.09s\n",
      "       956           1.3019            1.07s\n",
      "       957           1.3018            1.04s\n",
      "       958           1.3017            1.02s\n",
      "       959           1.3016            0.99s\n",
      "       960           1.3015            0.97s\n",
      "       961           1.3014            0.95s\n",
      "       962           1.3014            0.92s\n",
      "       963           1.3013            0.90s\n",
      "       964           1.3012            0.87s\n",
      "       965           1.3011            0.85s\n",
      "       966           1.3010            0.82s\n",
      "       967           1.3009            0.80s\n",
      "       968           1.3008            0.78s\n",
      "       969           1.3007            0.75s\n",
      "       970           1.3006            0.73s\n",
      "       971           1.3005            0.70s\n",
      "       972           1.3004            0.68s\n",
      "       973           1.3003            0.65s\n",
      "       974           1.3002            0.63s\n",
      "       975           1.3001            0.61s\n",
      "       976           1.3001            0.58s\n",
      "       977           1.3000            0.56s\n",
      "       978           1.2999            0.53s\n",
      "       979           1.2999            0.51s\n",
      "       980           1.2999            0.49s\n",
      "       981           1.2998            0.46s\n",
      "       982           1.2998            0.44s\n",
      "       983           1.2997            0.41s\n",
      "       984           1.2997            0.39s\n",
      "       985           1.2996            0.36s\n",
      "       986           1.2995            0.34s\n",
      "       987           1.2993            0.32s\n",
      "       988           1.2993            0.29s\n",
      "       989           1.2992            0.27s\n",
      "       990           1.2991            0.24s\n",
      "       991           1.2990            0.22s\n",
      "       992           1.2990            0.19s\n",
      "       993           1.2988            0.17s\n",
      "       994           1.2987            0.15s\n",
      "       995           1.2986            0.12s\n",
      "       996           1.2985            0.10s\n",
      "       997           1.2985            0.07s\n",
      "       998           1.2984            0.05s\n",
      "       999           1.2984            0.02s\n",
      "      1000           1.2983            0.00s\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>GradientBoostingClassifier(learning_rate=0.01, n_estimators=1000,\n",
       "                           random_state=0, verbose=3)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">GradientBoostingClassifier</label><div class=\"sk-toggleable__content\"><pre>GradientBoostingClassifier(learning_rate=0.01, n_estimators=1000,\n",
       "                           random_state=0, verbose=3)</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "GradientBoostingClassifier(learning_rate=0.01, n_estimators=1000,\n",
       "                           random_state=0, verbose=3)"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clf = GradientBoostingClassifier(random_state=0, learning_rate=0.01, n_estimators=1000, verbose=3)\n",
    "\n",
    "scores = cross_val_score(clf, X_train, y_train, cv=5)\n",
    "\n",
    "print(\"Accuracy: %0.2f (+/- %0.2f)\" % (scores.mean(), scores.std() * 2))\n",
    "\n",
    "clf.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Testando o Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Testing Accuracy: 0.654855\n"
     ]
    }
   ],
   "source": [
    "accuracy = clf.score(X_train, y_train)\n",
    "print('Testing Accuracy: %f' % accuracy)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "scrolled": true,
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Predict test data\n",
    "\n",
    "pred = clf.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "tags": []
   },
   "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>Open</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Close</th>\n",
       "      <th>Volume</th>\n",
       "      <th>return</th>\n",
       "      <th>Open_oil</th>\n",
       "      <th>High_oil</th>\n",
       "      <th>Low_oil</th>\n",
       "      <th>Close_oil</th>\n",
       "      <th>Volume_oil</th>\n",
       "      <th>year</th>\n",
       "      <th>month</th>\n",
       "      <th>day</th>\n",
       "      <th>hour</th>\n",
       "      <th>label</th>\n",
       "      <th>pred</th>\n",
       "      <th>won</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2018-05-01 03:00:00</th>\n",
       "      <td>1.24915</td>\n",
       "      <td>1.24925</td>\n",
       "      <td>1.24836</td>\n",
       "      <td>1.24875</td>\n",
       "      <td>2.587190e+09</td>\n",
       "      <td>0.558663</td>\n",
       "      <td>67.950</td>\n",
       "      <td>67.975</td>\n",
       "      <td>67.905</td>\n",
       "      <td>67.945</td>\n",
       "      <td>16400.0001</td>\n",
       "      <td>2018</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-05-01 04:00:00</th>\n",
       "      <td>1.24875</td>\n",
       "      <td>1.24889</td>\n",
       "      <td>1.24844</td>\n",
       "      <td>1.24845</td>\n",
       "      <td>2.972600e+09</td>\n",
       "      <td>0.561877</td>\n",
       "      <td>67.950</td>\n",
       "      <td>68.010</td>\n",
       "      <td>67.945</td>\n",
       "      <td>67.995</td>\n",
       "      <td>11629.9996</td>\n",
       "      <td>2018</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-05-01 05:00:00</th>\n",
       "      <td>1.24844</td>\n",
       "      <td>1.24938</td>\n",
       "      <td>1.24844</td>\n",
       "      <td>1.24922</td>\n",
       "      <td>2.884640e+09</td>\n",
       "      <td>0.596593</td>\n",
       "      <td>67.990</td>\n",
       "      <td>68.040</td>\n",
       "      <td>67.965</td>\n",
       "      <td>67.980</td>\n",
       "      <td>27775.9992</td>\n",
       "      <td>2018</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-05-01 06:00:00</th>\n",
       "      <td>1.24923</td>\n",
       "      <td>1.25064</td>\n",
       "      <td>1.24898</td>\n",
       "      <td>1.25047</td>\n",
       "      <td>4.153540e+09</td>\n",
       "      <td>0.611379</td>\n",
       "      <td>67.980</td>\n",
       "      <td>68.010</td>\n",
       "      <td>67.875</td>\n",
       "      <td>67.905</td>\n",
       "      <td>29696.9991</td>\n",
       "      <td>2018</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-05-01 07:00:00</th>\n",
       "      <td>1.25047</td>\n",
       "      <td>1.25075</td>\n",
       "      <td>1.24961</td>\n",
       "      <td>1.25074</td>\n",
       "      <td>5.663920e+09</td>\n",
       "      <td>0.580199</td>\n",
       "      <td>67.910</td>\n",
       "      <td>67.910</td>\n",
       "      <td>67.750</td>\n",
       "      <td>67.775</td>\n",
       "      <td>75609.9969</td>\n",
       "      <td>2018</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-05-31 08:00:00</th>\n",
       "      <td>1.28484</td>\n",
       "      <td>1.28538</td>\n",
       "      <td>1.28356</td>\n",
       "      <td>1.28400</td>\n",
       "      <td>5.385360e+09</td>\n",
       "      <td>0.544519</td>\n",
       "      <td>77.505</td>\n",
       "      <td>77.580</td>\n",
       "      <td>77.285</td>\n",
       "      <td>77.315</td>\n",
       "      <td>260250.0021</td>\n",
       "      <td>2018</td>\n",
       "      <td>5</td>\n",
       "      <td>31</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-05-31 09:00:00</th>\n",
       "      <td>1.28400</td>\n",
       "      <td>1.28539</td>\n",
       "      <td>1.28394</td>\n",
       "      <td>1.28470</td>\n",
       "      <td>5.552970e+09</td>\n",
       "      <td>0.594021</td>\n",
       "      <td>77.346</td>\n",
       "      <td>77.446</td>\n",
       "      <td>77.185</td>\n",
       "      <td>77.215</td>\n",
       "      <td>167003.0057</td>\n",
       "      <td>2018</td>\n",
       "      <td>5</td>\n",
       "      <td>31</td>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-05-31 10:00:00</th>\n",
       "      <td>1.28472</td>\n",
       "      <td>1.28583</td>\n",
       "      <td>1.28180</td>\n",
       "      <td>1.28259</td>\n",
       "      <td>5.881380e+09</td>\n",
       "      <td>0.503054</td>\n",
       "      <td>77.221</td>\n",
       "      <td>77.496</td>\n",
       "      <td>77.165</td>\n",
       "      <td>77.395</td>\n",
       "      <td>133186.9960</td>\n",
       "      <td>2018</td>\n",
       "      <td>5</td>\n",
       "      <td>31</td>\n",
       "      <td>10</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-05-31 11:00:00</th>\n",
       "      <td>1.28264</td>\n",
       "      <td>1.28514</td>\n",
       "      <td>1.28251</td>\n",
       "      <td>1.28474</td>\n",
       "      <td>6.608530e+09</td>\n",
       "      <td>0.639023</td>\n",
       "      <td>77.416</td>\n",
       "      <td>77.891</td>\n",
       "      <td>77.365</td>\n",
       "      <td>77.826</td>\n",
       "      <td>255546.9871</td>\n",
       "      <td>2018</td>\n",
       "      <td>5</td>\n",
       "      <td>31</td>\n",
       "      <td>11</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-05-31 12:00:00</th>\n",
       "      <td>1.28474</td>\n",
       "      <td>1.28987</td>\n",
       "      <td>1.28472</td>\n",
       "      <td>1.28985</td>\n",
       "      <td>1.079586e+10</td>\n",
       "      <td>0.735776</td>\n",
       "      <td>77.821</td>\n",
       "      <td>78.201</td>\n",
       "      <td>77.615</td>\n",
       "      <td>78.121</td>\n",
       "      <td>309336.0066</td>\n",
       "      <td>2018</td>\n",
       "      <td>5</td>\n",
       "      <td>31</td>\n",
       "      <td>12</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>180 rows × 18 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                        Open     High      Low    Close        Volume  \\\n",
       "2018-05-01 03:00:00  1.24915  1.24925  1.24836  1.24875  2.587190e+09   \n",
       "2018-05-01 04:00:00  1.24875  1.24889  1.24844  1.24845  2.972600e+09   \n",
       "2018-05-01 05:00:00  1.24844  1.24938  1.24844  1.24922  2.884640e+09   \n",
       "2018-05-01 06:00:00  1.24923  1.25064  1.24898  1.25047  4.153540e+09   \n",
       "2018-05-01 07:00:00  1.25047  1.25075  1.24961  1.25074  5.663920e+09   \n",
       "...                      ...      ...      ...      ...           ...   \n",
       "2018-05-31 08:00:00  1.28484  1.28538  1.28356  1.28400  5.385360e+09   \n",
       "2018-05-31 09:00:00  1.28400  1.28539  1.28394  1.28470  5.552970e+09   \n",
       "2018-05-31 10:00:00  1.28472  1.28583  1.28180  1.28259  5.881380e+09   \n",
       "2018-05-31 11:00:00  1.28264  1.28514  1.28251  1.28474  6.608530e+09   \n",
       "2018-05-31 12:00:00  1.28474  1.28987  1.28472  1.28985  1.079586e+10   \n",
       "\n",
       "                       return  Open_oil  High_oil  Low_oil  Close_oil  \\\n",
       "2018-05-01 03:00:00  0.558663    67.950    67.975   67.905     67.945   \n",
       "2018-05-01 04:00:00  0.561877    67.950    68.010   67.945     67.995   \n",
       "2018-05-01 05:00:00  0.596593    67.990    68.040   67.965     67.980   \n",
       "2018-05-01 06:00:00  0.611379    67.980    68.010   67.875     67.905   \n",
       "2018-05-01 07:00:00  0.580199    67.910    67.910   67.750     67.775   \n",
       "...                       ...       ...       ...      ...        ...   \n",
       "2018-05-31 08:00:00  0.544519    77.505    77.580   77.285     77.315   \n",
       "2018-05-31 09:00:00  0.594021    77.346    77.446   77.185     77.215   \n",
       "2018-05-31 10:00:00  0.503054    77.221    77.496   77.165     77.395   \n",
       "2018-05-31 11:00:00  0.639023    77.416    77.891   77.365     77.826   \n",
       "2018-05-31 12:00:00  0.735776    77.821    78.201   77.615     78.121   \n",
       "\n",
       "                      Volume_oil  year  month  day  hour  label  pred    won  \n",
       "2018-05-01 03:00:00   16400.0001  2018      5    1     3      0     1  False  \n",
       "2018-05-01 04:00:00   11629.9996  2018      5    1     4      1     1   True  \n",
       "2018-05-01 05:00:00   27775.9992  2018      5    1     5      1     0  False  \n",
       "2018-05-01 06:00:00   29696.9991  2018      5    1     6      1     0  False  \n",
       "2018-05-01 07:00:00   75609.9969  2018      5    1     7      0     1  False  \n",
       "...                          ...   ...    ...  ...   ...    ...   ...    ...  \n",
       "2018-05-31 08:00:00  260250.0021  2018      5   31     8      1     1   True  \n",
       "2018-05-31 09:00:00  167003.0057  2018      5   31     9      0     1  False  \n",
       "2018-05-31 10:00:00  133186.9960  2018      5   31    10      1     1   True  \n",
       "2018-05-31 11:00:00  255546.9871  2018      5   31    11      1     1   True  \n",
       "2018-05-31 12:00:00  309336.0066  2018      5   31    12      1     0  False  \n",
       "\n",
       "[180 rows x 18 columns]"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Comparing predicted and label\n",
    "\n",
    "df_trade = pd.DataFrame(X_test)\n",
    "df_trade['label']  = y_test\n",
    "df_trade['pred']   = pred\n",
    "df_trade['won']    = df_trade['label'] == df_trade['pred']\n",
    "\n",
    "df_trade"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "79\n",
      "101\n"
     ]
    }
   ],
   "source": [
    "# Didnt won and won\n",
    "\n",
    "print(df_trade[df_trade['won'] == False]['won'].count())\n",
    "print(df_trade[df_trade['won'] == True]['won'].count())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.5611111111111111"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# % of won\n",
    "\n",
    "df_trade[df_trade['won'] == True]['won'].count()/df_trade['won'].count()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.10.9"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}