{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Python Crypto Assignment 2: Predict Crypto Price Data using Regeration"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### import modules"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python2.7/dist-packages/h5py/__init__.py:36: FutureWarning: Conversion of the second argument of issubdtype from `float` to `np.floating` is deprecated. In future, it will be treated as `np.float64 == np.dtype(float).type`.\n",
      "  from ._conv import register_converters as _register_converters\n"
     ]
    }
   ],
   "source": [
    "import requests\n",
    "import datetime\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import tensorflow as tf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "def daily_price_historical(symbol, comparison_symbol, all_data=False, limit=1, aggregate=1, exchange=''):\n",
    "    url = 'https://min-api.cryptocompare.com/data/histoday?fsym={}&tsym={}&limit={}&aggregate={}'\\\n",
    "            .format(symbol.upper(), comparison_symbol.upper(), limit, aggregate)\n",
    "    if exchange:\n",
    "        url += '&e={}'.format(exchange)\n",
    "    if all_data:\n",
    "        url += '&allData=true'\n",
    "    page = requests.get(url)\n",
    "    data = page.json()['Data']\n",
    "    df = pd.DataFrame(data)\n",
    "    df['timestamp'] = [datetime.datetime.fromtimestamp(d) for d in df.time]\n",
    "    return df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = daily_price_historical('BTC', 'USD', all_data=True)\n",
    "msk = np.random.rand(len(df)) < 0.8\n",
    "train = df[msk]\n",
    "test = df[~msk]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(2233, 533)\n",
      "(            close         high          low         open        time  \\\n",
      "0         0.04951      0.04951      0.04951      0.04951  1279324800   \n",
      "1         0.08584      0.08585      0.05941      0.04951  1279411200   \n",
      "2         0.08080      0.09307      0.07723      0.08584  1279497600   \n",
      "3         0.07474      0.08181      0.07426      0.08080  1279584000   \n",
      "4         0.07921      0.07921      0.06634      0.07474  1279670400   \n",
      "5         0.05050      0.08181      0.05050      0.07921  1279756800   \n",
      "7         0.05454      0.06161      0.05049      0.06262  1279929600   \n",
      "10        0.06000      0.06050      0.05300      0.05600  1280188800   \n",
      "12        0.06990      0.06990      0.05710      0.05890  1280361600   \n",
      "13        0.06270      0.06980      0.05820      0.06990  1280448000   \n",
      "14        0.06785      0.06889      0.05600      0.06270  1280534400   \n",
      "15        0.06110      0.06500      0.06000      0.06785  1280620800   \n",
      "16        0.06000      0.06330      0.06000      0.06110  1280707200   \n",
      "17        0.06000      0.06500      0.05900      0.06000  1280793600   \n",
      "18        0.05700      0.06231      0.05700      0.06000  1280880000   \n",
      "19        0.06100      0.06100      0.05800      0.05700  1280966400   \n",
      "20        0.06230      0.06240      0.06070      0.06100  1281052800   \n",
      "22        0.06090      0.06100      0.05900      0.05900  1281225600   \n",
      "26        0.07000      0.07000      0.06141      0.06700  1281571200   \n",
      "27        0.06450      0.06800      0.06450      0.07000  1281657600   \n",
      "28        0.06700      0.06950      0.06450      0.06450  1281744000   \n",
      "29        0.06529      0.06700      0.06500      0.06700  1281830400   \n",
      "30        0.06550      0.06790      0.06200      0.06529  1281916800   \n",
      "31        0.07000      0.07690      0.06243      0.06550  1282003200   \n",
      "32        0.06800      0.07330      0.06700      0.07000  1282089600   \n",
      "34        0.06550      0.06670      0.06500      0.06670  1282262400   \n",
      "36        0.06600      0.06640      0.06120      0.06640  1282435200   \n",
      "37        0.06491      0.06689      0.06300      0.06600  1282521600   \n",
      "38        0.06500      0.06650      0.06491      0.06491  1282608000   \n",
      "39        0.06480      0.06650      0.06410      0.06500  1282694400   \n",
      "...           ...          ...          ...          ...         ...   \n",
      "2730  17172.30000  17252.85000  16286.57000  16954.76000  1515196800   \n",
      "2732  14976.17000  16302.92000  13902.31000  16228.26000  1515369600   \n",
      "2733  14468.50000  15390.28000  14221.55000  14976.17000  1515456000   \n",
      "2734  14919.49000  14919.49000  13450.54000  14468.09000  1515542400   \n",
      "2735  13308.06000  14979.96000  12825.95000  14920.36000  1515628800   \n",
      "2736  13841.19000  14129.08000  12851.91000  13308.06000  1515715200   \n",
      "2737  14243.12000  14595.04000  13830.28000  13841.19000  1515801600   \n",
      "2739  13631.98000  14355.82000  13416.71000  13638.63000  1515974400   \n",
      "2740  11282.49000  13648.84000  10032.69000  13634.60000  1516060800   \n",
      "2742  11175.52000  12018.43000  10642.33000  11162.70000  1516233600   \n",
      "2743  11521.76000  11780.49000  10867.18000  11175.52000  1516320000   \n",
      "2744  12783.94000  13031.04000  11502.11000  11521.82000  1516406400   \n",
      "2745  11549.93000  12787.35000  11101.73000  12783.54000  1516492800   \n",
      "2746  10814.52000  11913.74000  10067.76000  11549.98000  1516579200   \n",
      "2747  10858.23000  11388.52000   9980.50000  10814.52000  1516665600   \n",
      "2748  11429.02000  11531.60000  10506.55000  10853.78000  1516752000   \n",
      "2749  11175.87000  11741.92000  10930.34000  11428.11000  1516838400   \n",
      "2751  11459.71000  11638.69000  10879.20000  11104.34000  1517011200   \n",
      "2752  11767.74000  12064.19000  11407.94000  11460.39000  1517097600   \n",
      "2753  11233.95000  11860.29000  11089.52000  11767.74000  1517184000   \n",
      "2754  10107.26000  11263.70000   9871.21000  11234.32000  1517270400   \n",
      "2755  10226.86000  10377.96000   9698.13000  10107.40000  1517356800   \n",
      "2756   9114.72000  10280.84000   8726.95000  10226.86000  1517443200   \n",
      "2757   8870.82000   9147.93000   7786.20000   9114.73000  1517529600   \n",
      "2758   9251.27000   9504.37000   8194.68000   8872.87000  1517616000   \n",
      "2759   8218.05000   9400.99000   7889.83000   9251.27000  1517702400   \n",
      "2760   6937.08000   8391.29000   6627.31000   8218.05000  1517788800   \n",
      "2761   7701.25000   7932.38000   5968.36000   6936.43000  1517875200   \n",
      "2763   8260.69000   8643.94000   7590.48000   7593.78000  1518048000   \n",
      "2764   8696.83000   8743.20000   7775.36000   8259.26000  1518134400   \n",
      "\n",
      "      volumefrom      volumeto  timestamp  \n",
      "0          20.00  9.902000e-01 2010-07-17  \n",
      "1          75.01  5.090000e+00 2010-07-18  \n",
      "2         574.00  4.966000e+01 2010-07-19  \n",
      "3         262.00  2.059000e+01 2010-07-20  \n",
      "4         575.00  4.226000e+01 2010-07-21  \n",
      "5        2160.00  1.297800e+02 2010-07-22  \n",
      "7         496.32  2.673000e+01 2010-07-24  \n",
      "10       3373.69  1.969200e+02 2010-07-27  \n",
      "12       8058.49  5.283200e+02 2010-07-29  \n",
      "13       3020.85  1.985300e+02 2010-07-30  \n",
      "14       4022.25  2.439000e+02 2010-07-31  \n",
      "15       2601.00  1.626500e+02 2010-08-01  \n",
      "16       3599.00  2.212000e+02 2010-08-02  \n",
      "17       9821.46  6.060500e+02 2010-08-03  \n",
      "18       3494.00  2.107700e+02 2010-08-04  \n",
      "19       5034.07  3.036100e+02 2010-08-05  \n",
      "20       1395.00  8.591000e+01 2010-08-06  \n",
      "22       2201.00  1.326000e+02 2010-08-08  \n",
      "26       2062.31  1.344900e+02 2010-08-12  \n",
      "27       3591.77  2.338000e+02 2010-08-13  \n",
      "28       4404.20  2.953100e+02 2010-08-14  \n",
      "29       4462.87  2.949500e+02 2010-08-15  \n",
      "30      10731.47  6.968000e+02 2010-08-16  \n",
      "31      13185.74  9.150000e+02 2010-08-17  \n",
      "32       2953.98  2.038300e+02 2010-08-18  \n",
      "34       4199.75  2.764100e+02 2010-08-20  \n",
      "36      18648.97  1.183640e+03 2010-08-22  \n",
      "37       4297.49  2.818800e+02 2010-08-23  \n",
      "38       6711.76  4.446600e+02 2010-08-24  \n",
      "39       4228.88  2.788600e+02 2010-08-25  \n",
      "...          ...           ...        ...  \n",
      "2730    83927.99  1.412704e+09 2018-01-06  \n",
      "2732   142453.96  2.166367e+09 2018-01-08  \n",
      "2733    99730.65  1.486802e+09 2018-01-09  \n",
      "2734   142303.90  2.025084e+09 2018-01-10  \n",
      "2735   173452.27  2.373494e+09 2018-01-11  \n",
      "2736   102068.02  1.402293e+09 2018-01-12  \n",
      "2737    71300.93  1.021353e+09 2018-01-13  \n",
      "2739    84089.84  1.170463e+09 2018-01-15  \n",
      "2740   325702.79  3.842652e+09 2018-01-16  \n",
      "2742   204918.02  2.357252e+09 2018-01-18  \n",
      "2743   110885.87  1.264876e+09 2018-01-19  \n",
      "2744   119084.84  1.492722e+09 2018-01-20  \n",
      "2745   130427.64  1.552755e+09 2018-01-21  \n",
      "2746   165723.08  1.811992e+09 2018-01-22  \n",
      "2747   158868.59  1.710181e+09 2018-01-23  \n",
      "2748   115804.82  1.286435e+09 2018-01-24  \n",
      "2749    93317.76  1.061064e+09 2018-01-25  \n",
      "2751    90873.81  1.031092e+09 2018-01-27  \n",
      "2752    87917.22  1.038153e+09 2018-01-28  \n",
      "2753    80713.70  9.208362e+08 2018-01-29  \n",
      "2754   164072.93  1.720894e+09 2018-01-30  \n",
      "2755   122260.49  1.230903e+09 2018-01-31  \n",
      "2756   208918.80  1.964309e+09 2018-02-01  \n",
      "2757   322596.22  2.768760e+09 2018-02-02  \n",
      "2758   139226.07  1.253485e+09 2018-02-03  \n",
      "2759   164609.06  1.413207e+09 2018-02-04  \n",
      "2760   341828.54  2.534149e+09 2018-02-05  \n",
      "2761   495883.24  3.397597e+09 2018-02-06  \n",
      "2763   193040.33  1.594674e+09 2018-02-08  \n",
      "2764   162279.68  1.348072e+09 2018-02-09  \n",
      "\n",
      "[2233 rows x 8 columns],             close         high          low         open        time  \\\n",
      "6         0.06262      0.06767      0.05050      0.05050  1279843200   \n",
      "8         0.05050      0.05941      0.05050      0.05454  1280016000   \n",
      "9         0.05600      0.05600      0.05000      0.05050  1280102400   \n",
      "11        0.05890      0.06200      0.05400      0.06000  1280275200   \n",
      "21        0.05900      0.06220      0.05900      0.06230  1281139200   \n",
      "23        0.07100      0.07350      0.05930      0.06090  1281312000   \n",
      "24        0.07000      0.07090      0.06651      0.07100  1281398400   \n",
      "25        0.06700      0.07541      0.06000      0.07000  1281484800   \n",
      "33        0.06670      0.06790      0.06670      0.06800  1282176000   \n",
      "35        0.06640      0.06690      0.06440      0.06550  1282348800   \n",
      "41        0.06500      0.06500      0.06300      0.06400  1282867200   \n",
      "46        0.06290      0.06290      0.05961      0.06000  1283299200   \n",
      "50        0.06160      0.06400      0.06050      0.06238  1283644800   \n",
      "53        0.06200      0.06200      0.06099      0.06100  1283904000   \n",
      "59        0.06199      0.17500      0.06100      0.06219  1284422400   \n",
      "60        0.06040      0.06250      0.06040      0.06199  1284508800   \n",
      "62        0.05900      0.06090      0.05900      0.06190  1284681600   \n",
      "64        0.06270      0.06270      0.06000      0.06100  1284854400   \n",
      "67        0.06220      0.06240      0.06147      0.06265  1285113600   \n",
      "68        0.06231      0.06300      0.06150      0.06220  1285200000   \n",
      "72        0.06220      0.06228      0.06190      0.06200  1285545600   \n",
      "80        0.06140      0.06301      0.06090      0.06130  1286236800   \n",
      "92        0.10200      0.10450      0.10000      0.10100  1287273600   \n",
      "93        0.10240      0.10300      0.09700      0.10200  1287360000   \n",
      "95        0.09900      0.10300      0.09400      0.09700  1287532800   \n",
      "96        0.10700      0.10900      0.09700      0.09900  1287619200   \n",
      "98        0.10550      0.10900      0.10450      0.10250  1287792000   \n",
      "105       0.19890      0.19900      0.18750      0.19000  1288396800   \n",
      "113       0.34000      0.47000      0.28600      0.39000  1289088000   \n",
      "115       0.21000      0.32300      0.19900      0.24300  1289260800   \n",
      "...           ...          ...          ...          ...         ...   \n",
      "2621   3907.96000   4120.71000   3852.22000   4100.28000  1505779200   \n",
      "2622   3882.16000   4053.04000   3846.76000   3907.96000  1505865600   \n",
      "2627   3932.83000   3971.50000   3665.67000   3667.52000  1506297600   \n",
      "2628   3892.70000   3982.31000   3858.75000   3932.83000  1506384000   \n",
      "2630   4195.65000   4274.64000   4112.88000   4212.20000  1506556800   \n",
      "2636   4218.66000   4355.29000   4183.57000   4314.18000  1507075200   \n",
      "2639   4435.81000   4472.92000   4319.48000   4371.94000  1507334400   \n",
      "2644   5432.62000   5432.63000   4814.98000   4824.20000  1507766400   \n",
      "2649   5598.58000   5773.32000   5521.09000   5759.25000  1508198400   \n",
      "2655   5903.61000   6048.98000   5644.22000   5982.86000  1508716800   \n",
      "2663   6451.24000   6467.20000   6090.83000   6124.28000  1509408000   \n",
      "2666   7152.12000   7445.62000   6938.93000   7024.81000  1509667200   \n",
      "2670   7102.75000   7222.37000   6957.80000   6959.27000  1510012800   \n",
      "2673   6565.80000   7318.97000   6406.05000   7129.59000  1510272000   \n",
      "2674   6339.86000   6821.50000   6197.87000   6565.80000  1510358400   \n",
      "2688   8754.69000   8761.98000   8153.70000   8203.45000  1511568000   \n",
      "2694  10861.47000  10942.80000   9420.93000   9947.08000  1512086400   \n",
      "2706  16286.82000  17267.96000  15669.86000  17083.90000  1513123200   \n",
      "2707  16467.91000  16941.08000  16023.64000  16286.82000  1513209600   \n",
      "2708  17604.85000  17987.03000  16442.20000  16467.91000  1513296000   \n",
      "2712  17523.70000  19021.97000  16812.80000  18971.19000  1513641600   \n",
      "2715  13664.97000  15823.72000  10875.71000  15632.12000  1513900800   \n",
      "2727  15156.62000  15435.01000  14579.71000  14754.09000  1514937600   \n",
      "2729  16954.78000  17126.95000  14832.36000  15180.08000  1515110400   \n",
      "2731  16228.16000  17184.81000  15791.13000  17174.50000  1515283200   \n",
      "2738  13638.63000  14415.67000  13031.91000  14244.12000  1515888000   \n",
      "2741  11162.70000  11736.30000   9205.38000  11282.49000  1516147200   \n",
      "2750  11104.20000  11656.54000  10346.86000  11175.87000  1516924800   \n",
      "2762   7592.72000   8572.68000   7208.86000   7701.25000  1517961600   \n",
      "2765   8604.86000   9081.49000   8538.94000   8696.83000  1518220800   \n",
      "\n",
      "      volumefrom      volumeto  timestamp  \n",
      "6        2402.50  1.410700e+02 2010-07-23  \n",
      "8        1551.48  8.506000e+01 2010-07-25  \n",
      "9         877.00  4.691000e+01 2010-07-26  \n",
      "11       4390.29  2.557600e+02 2010-07-28  \n",
      "21       2619.00  1.573400e+02 2010-08-07  \n",
      "23      13631.09  8.869300e+02 2010-08-09  \n",
      "24       1310.39  8.887000e+01 2010-08-10  \n",
      "25      14061.18  1.015640e+03 2010-08-11  \n",
      "33        741.12  5.004000e+01 2010-08-19  \n",
      "35      10443.86  6.885100e+02 2010-08-21  \n",
      "41       9009.97  5.837100e+02 2010-08-27  \n",
      "46       7164.72  4.367300e+02 2010-09-01  \n",
      "50       8459.24  5.279700e+02 2010-09-05  \n",
      "53       2345.41  1.433600e+02 2010-09-08  \n",
      "59      14014.81  8.767600e+02 2010-09-14  \n",
      "60       3651.85  2.232700e+02 2010-09-15  \n",
      "62       7262.00  4.389400e+02 2010-09-17  \n",
      "64      12852.17  7.973600e+02 2010-09-19  \n",
      "67      11560.40  7.173300e+02 2010-09-22  \n",
      "68      15505.33  9.700900e+02 2010-09-23  \n",
      "72      10752.25  6.685300e+02 2010-09-27  \n",
      "80      27526.61  1.699200e+03 2010-10-05  \n",
      "92      18222.19  1.857800e+03 2010-10-17  \n",
      "93      25665.09  2.591640e+03 2010-10-18  \n",
      "95      31855.00  3.221080e+03 2010-10-20  \n",
      "96      44867.00  4.616850e+03 2010-10-21  \n",
      "98       4423.47  4.815200e+02 2010-10-23  \n",
      "105     26708.94  5.112030e+03 2010-10-30  \n",
      "113     77218.72  2.793774e+04 2010-11-07  \n",
      "115     47983.39  1.280830e+04 2010-11-09  \n",
      "...          ...           ...        ...  \n",
      "2621   123214.81  4.888978e+08 2017-09-19  \n",
      "2622    86645.15  3.424381e+08 2017-09-20  \n",
      "2627   100194.70  3.866750e+08 2017-09-25  \n",
      "2628    63252.30  2.484819e+08 2017-09-26  \n",
      "2630    92228.17  3.869095e+08 2017-09-28  \n",
      "2636    64081.96  2.724247e+08 2017-10-04  \n",
      "2639    40202.08  1.763320e+08 2017-10-07  \n",
      "2644   163916.09  8.486420e+08 2017-10-12  \n",
      "2649    65637.43  3.686583e+08 2017-10-17  \n",
      "2655   137140.40  8.011483e+08 2017-10-23  \n",
      "2663   101249.62  6.378388e+08 2017-10-31  \n",
      "2666   154886.75  1.120978e+09 2017-11-03  \n",
      "2670   101044.57  7.167984e+08 2017-11-07  \n",
      "2673   222001.18  1.513421e+09 2017-11-10  \n",
      "2674   135719.07  8.780969e+08 2017-11-11  \n",
      "2688    84670.41  7.184837e+08 2017-11-25  \n",
      "2694   131918.85  1.353670e+09 2017-12-01  \n",
      "2706   155407.35  2.576056e+09 2017-12-13  \n",
      "2707   107918.03  1.773814e+09 2017-12-14  \n",
      "2708   153651.15  2.682351e+09 2017-12-15  \n",
      "2712   174543.37  3.136709e+09 2017-12-19  \n",
      "2715   466980.60  6.245732e+09 2017-12-22  \n",
      "2727   106543.39  1.604207e+09 2018-01-03  \n",
      "2729   141960.42  2.283989e+09 2018-01-05  \n",
      "2731    79006.37  1.309533e+09 2018-01-07  \n",
      "2738    81536.00  1.112591e+09 2018-01-14  \n",
      "2741   348631.94  3.666978e+09 2018-01-17  \n",
      "2750   142350.84  1.560135e+09 2018-01-26  \n",
      "2762   271450.37  2.159765e+09 2018-02-07  \n",
      "2765    83829.28  7.422646e+08 2018-02-10  \n",
      "\n",
      "[533 rows x 8 columns])\n"
     ]
    }
   ],
   "source": [
    "print (len(train), len(test))\n",
    "print (train, test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f2731991890>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(20,10))\n",
    "plt.plot(train.timestamp, train.high)\n",
    "plt.title('BTC' + ' To ' + 'USD', fontsize=24)\n",
    "plt.ylabel('Train Data In ' + 'USD', fontsize=18)\n",
    "plt.xlabel('Year', fontsize=18)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f2731991d10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(20,10))\n",
    "plt.plot(test.timestamp, test.high)\n",
    "plt.title('BTC' + ' To ' + 'USD', fontsize=24)\n",
    "plt.ylabel('Test Data In ' + 'USD', fontsize=18)\n",
    "plt.xlabel('Year', fontsize=18)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "np.set_printoptions(suppress=True)\n",
    "train_X = np.asarray(train.time)\n",
    "train_Y = np.asarray(train.high)\n",
    "# np.set_printoptions(suppress=True)\n",
    "# train_X = np.asarray([float(10000*np.random.random()) for i in xrange(4)]).astype(int)\n",
    "# train_Y = train_X * 1.5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Parameters\n",
    "learning_rate = 0.000000000000001\n",
    "training_epochs = 1\n",
    "display_step = 50"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "# tf Graph Input\n",
    "X = tf.placeholder(\"float\")\n",
    "Y = tf.placeholder(\"float\")\n",
    "\n",
    "# Set model weights\n",
    "W = tf.Variable(0., name=\"weight\")\n",
    "b = tf.Variable(0., name=\"bias\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Construct a linear model\n",
    "pred = tf.add(tf.multiply(X, W), b)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(array([   0.04951,    0.08585,    0.09307, ..., 7932.38   , 8643.94   ,\n",
      "       8743.2    ]), array([1279324800, 1279411200, 1279497600, ..., 1517875200, 1518048000,\n",
      "       1518134400]))\n"
     ]
    }
   ],
   "source": [
    "n_samples = train_X.shape[0]\n",
    "\n",
    "print ( train_Y, train_X)\n",
    "# Mean squared error\n",
    "powa = (pred-Y) * (pred-Y)\n",
    "cost = tf.reduce_sum(powa)/(2*n_samples)\n",
    "# Gradient descent\n",
    "optimizer = tf.train.GradientDescentOptimizer(learning_rate).minimize(cost)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Initialize the variables (i.e. assign their default value)\n",
    "init = tf.global_variables_initializer()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-----------------------------------------\n",
      "('pred', 0.036288235)\n",
      "('W', 2.8365145e-11)\n",
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      "-----------------------------------------\n"
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      "-----------------------------------------\n",
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      "-----------------------------------------\n",
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      "-----------------------------------------\n",
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      "-----------------------------------------\n",
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      "('pred', 0.38742104)\n",
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      "('b', 2.3354186e-19)\n",
      "('y', 0.37)\n",
      "('powa', 0.00030349253)\n",
      "-----------------------------------------\n",
      "-----------------------------------------\n"
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    {
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      "-----------------------------------------\n",
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      "-----------------------------------------\n",
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      "-----------------------------------------\n",
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      "('b', 1.6726329e-18)\n",
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      "('powa', 0.012997161)\n",
      "-----------------------------------------\n",
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      "('pred', 3.623724)\n",
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      "('X', array(1.304208e+09, dtype=float32))\n",
      "('b', 2.135521e-18)\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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      "-----------------------------------------\n",
      "-----------------------------------------\n",
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      "-----------------------------------------\n",
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      "-----------------------------------------\n",
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      "-----------------------------------------\n",
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      "-----------------------------------------\n",
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      "-----------------------------------------\n",
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      "-----------------------------------------\n",
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      "-----------------------------------------\n",
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      "-----------------------------------------\n",
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      "-----------------------------------------\n",
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    {
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      "('powa', 0.0004066355)\n",
      "-----------------------------------------\n",
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      "-----------------------------------------\n",
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      "-----------------------------------------\n",
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      "('powa', 7.522838e-05)\n",
      "-----------------------------------------\n",
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      "('pred', 3.2197597)\n",
      "('W', 2.4318545e-09)\n",
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      "('b', 1.9191486e-18)\n",
      "('y', 3.22)\n",
      "('powa', 5.775655e-08)\n",
      "-----------------------------------------\n",
      "-----------------------------------------\n",
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      "('powa', 0.00028035016)\n",
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      "-----------------------------------------\n",
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      "-----------------------------------------\n",
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      "-----------------------------------------\n"
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    {
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      "-----------------------------------------\n",
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      "-----------------------------------------\n",
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      "-----------------------------------------\n",
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      "-----------------------------------------\n",
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      "-----------------------------------------\n"
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      "-----------------------------------------\n"
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      "('powa', 0.19467112)\n",
      "-----------------------------------------\n",
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      "-----------------------------------------\n"
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      "-----------------------------------------\n",
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      "-----------------------------------------\n",
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      "-----------------------------------------\n",
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      "('powa', 1.1040611)\n",
      "-----------------------------------------\n",
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      "('b', 5.9057195e-17)\n",
      "('y', 108.99)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "('powa', 0.6951355)\n",
      "-----------------------------------------\n",
      "-----------------------------------------\n",
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      "('b', 5.89508e-17)\n",
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      "('powa', 0.0014158795)\n",
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      "('b', 6.01176e-17)\n",
      "('y', 112.25)\n",
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      "-----------------------------------------\n",
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      "('b', 5.942583e-17)\n",
      "('y', 110.3)\n",
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      "-----------------------------------------\n",
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      "('b', 5.680548e-17)\n",
      "('y', 104.7)\n",
      "('powa', 0.8551085)\n",
      "-----------------------------------------\n",
      "-----------------------------------------\n",
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      "-----------------------------------------\n",
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      "-----------------------------------------\n",
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    {
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      "-----------------------------------------\n",
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      "-----------------------------------------\n",
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    {
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      "-----------------------------------------\n"
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      "-----------------------------------------\n",
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    {
     "name": "stdout",
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      "-----------------------------------------\n",
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      "-----------------------------------------\n",
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      "-----------------------------------------\n"
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      "-----------------------------------------\n",
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      "('powa', 0.0032532597)\n",
      "-----------------------------------------\n",
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      "('y', 436.07)\n",
      "('powa', 0.05541909)\n",
      "-----------------------------------------\n",
      "-----------------------------------------\n"
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    {
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      "-----------------------------------------\n",
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      "-----------------------------------------\n",
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      "-----------------------------------------\n"
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      "('y', 16302.92)\n",
      "('powa', 810.63654)\n",
      "-----------------------------------------\n",
      "-----------------------------------------\n",
      "('pred', 15365.068)\n",
      "('W', 1.0138908e-05)\n",
      "('X', array(1.515456e+09, dtype=float32))\n",
      "('b', 6.7491297e-15)\n",
      "('y', 15390.28)\n",
      "('powa', 635.6406)\n",
      "-----------------------------------------\n",
      "-----------------------------------------\n",
      "('pred', 14906.722)\n",
      "('W', 9.835899e-06)\n",
      "('X', array(1.5155424e+09, dtype=float32))\n",
      "('b', 6.5491952e-15)\n",
      "('y', 14919.49)\n",
      "('powa', 163.036)\n",
      "-----------------------------------------\n",
      "-----------------------------------------\n",
      "('pred', 14982.038)\n",
      "('W', 9.885031e-06)\n",
      "('X', array(1.5156288e+09, dtype=float32))\n",
      "('b', 6.581613e-15)\n",
      "('y', 14979.96)\n",
      "('powa', 4.3186035)\n",
      "-----------------------------------------\n",
      "-----------------------------------------\n",
      "('pred', 14104.458)\n",
      "('W', 9.3054805e-06)\n",
      "('X', array(1.5157152e+09, dtype=float32))\n",
      "('b', 6.1992516e-15)\n",
      "('y', 14129.08)\n",
      "('powa', 606.24634)\n",
      "-----------------------------------------\n",
      "-----------------------------------------\n",
      "('pred', 14609.22)\n",
      "('W', 9.63795e-06)\n",
      "('X', array(1.5158016e+09, dtype=float32))\n",
      "('b', 6.418588e-15)\n",
      "('y', 14595.04)\n",
      "('powa', 201.06354)\n",
      "-----------------------------------------\n",
      "-----------------------------------------\n",
      "('pred', 14348.374)\n",
      "('W', 9.464787e-06)\n",
      "('X', array(1.5159744e+09, dtype=float32))\n",
      "('b', 6.3043625e-15)\n",
      "('y', 14355.82)\n",
      "('powa', 55.44722)\n",
      "-----------------------------------------\n",
      "-----------------------------------------\n",
      "('pred', 13628.314)\n",
      "('W', 8.989293e-06)\n",
      "('X', array(1.5160608e+09, dtype=float32))\n",
      "('b', 5.990725e-15)\n",
      "('y', 13648.84)\n",
      "('powa', 421.29166)\n",
      "-----------------------------------------\n",
      "-----------------------------------------\n",
      "('pred', 11970.826)\n",
      "('W', 7.895107e-06)\n",
      "('X', array(1.5162336e+09, dtype=float32))\n",
      "('b', 5.2690773e-15)\n",
      "('y', 12018.43)\n",
      "('powa', 2266.0947)\n",
      "-----------------------------------------\n",
      "-----------------------------------------\n",
      "('pred', 11774.825)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "('W', 7.765396e-06)\n",
      "('X', array(1.51632e+09, dtype=float32))\n",
      "('b', 5.1835337e-15)\n",
      "('y', 11780.49)\n",
      "('powa', 32.092667)\n",
      "-----------------------------------------\n",
      "-----------------------------------------\n",
      "('pred', 13068.425)\n",
      "('W', 8.618023e-06)\n",
      "('X', array(1.5164064e+09, dtype=float32))\n",
      "('b', 5.7458014e-15)\n",
      "('y', 13031.04)\n",
      "('powa', 1397.6207)\n",
      "-----------------------------------------\n",
      "-----------------------------------------\n",
      "('pred', 12778.926)\n",
      "('W', 8.426631e-06)\n",
      "('X', array(1.5164928e+09, dtype=float32))\n",
      "('b', 5.6195948e-15)\n",
      "('y', 12787.35)\n",
      "('powa', 70.96088)\n",
      "-----------------------------------------\n",
      "-----------------------------------------\n",
      "('pred', 11887.755)\n",
      "('W', 7.838532e-06)\n",
      "('X', array(1.5165792e+09, dtype=float32))\n",
      "('b', 5.2318146e-15)\n",
      "('y', 11913.74)\n",
      "('powa', 675.2385)\n",
      "-----------------------------------------\n",
      "-----------------------------------------\n",
      "('pred', 11373.459)\n",
      "('W', 7.498989e-06)\n",
      "('X', array(1.5166656e+09, dtype=float32))\n",
      "('b', 5.00794e-15)\n",
      "('y', 11388.52)\n",
      "('powa', 226.82007)\n",
      "-----------------------------------------\n",
      "-----------------------------------------\n",
      "('pred', 11536.362)\n",
      "('W', 7.605965e-06)\n",
      "('X', array(1.516752e+09, dtype=float32))\n",
      "('b', 5.07847e-15)\n",
      "('y', 11531.6)\n",
      "('powa', 22.683266)\n",
      "-----------------------------------------\n",
      "-----------------------------------------\n",
      "('pred', 11748.142)\n",
      "('W', 7.7451505e-06)\n",
      "('X', array(1.5168384e+09, dtype=float32))\n",
      "('b', 5.17023e-15)\n",
      "('y', 11741.92)\n",
      "('powa', 38.709297)\n",
      "-----------------------------------------\n",
      "-----------------------------------------\n",
      "('pred', 11635.301)\n",
      "('W', 7.669884e-06)\n",
      "('X', array(1.5170112e+09, dtype=float32))\n",
      "('b', 5.120615e-15)\n",
      "('y', 11638.69)\n",
      "('powa', 11.489717)\n",
      "-----------------------------------------\n",
      "-----------------------------------------\n",
      "('pred', 12077.344)\n",
      "('W', 7.960822e-06)\n",
      "('X', array(1.5170976e+09, dtype=float32))\n",
      "('b', 5.3123874e-15)\n",
      "('y', 12064.19)\n",
      "('powa', 173.00984)\n",
      "-----------------------------------------\n",
      "-----------------------------------------\n",
      "('pred', 11853.577)\n",
      "('W', 7.8128805e-06)\n",
      "('X', array(1.517184e+09, dtype=float32))\n",
      "('b', 5.214877e-15)\n",
      "('y', 11860.29)\n",
      "('powa', 45.0629)\n",
      "-----------------------------------------\n",
      "-----------------------------------------\n",
      "('pred', 11245.424)\n",
      "('W', 7.4116147e-06)\n",
      "('X', array(1.5172704e+09, dtype=float32))\n",
      "('b', 4.9504112e-15)\n",
      "('y', 11263.7)\n",
      "('powa', 334.0256)\n",
      "-----------------------------------------\n",
      "-----------------------------------------\n",
      "('pred', 10350.991)\n",
      "('W', 6.8217255e-06)\n",
      "('X', array(1.5173568e+09, dtype=float32))\n",
      "('b', 4.56165e-15)\n",
      "('y', 10377.96)\n",
      "('powa', 727.3135)\n",
      "-----------------------------------------\n",
      "-----------------------------------------\n",
      "('pred', 10278.634)\n",
      "('W', 6.7736532e-06)\n",
      "('X', array(1.5174432e+09, dtype=float32))\n",
      "('b', 4.5299703e-15)\n",
      "('y', 10280.84)\n",
      "('powa', 4.8666773)\n",
      "-----------------------------------------\n",
      "-----------------------------------------\n",
      "('pred', 9112.519)\n",
      "('W', 6.0048374e-06)\n",
      "('X', array(1.5175296e+09, dtype=float32))\n",
      "('b', 4.0233468e-15)\n",
      "('y', 9147.93)\n",
      "('powa', 1253.9484)\n",
      "-----------------------------------------\n",
      "-----------------------------------------\n",
      "('pred', 9516.666)\n",
      "('W', 6.2707995e-06)\n",
      "('X', array(1.517616e+09, dtype=float32))\n",
      "('b', 4.1985966e-15)\n",
      "('y', 9504.37)\n",
      "('powa', 151.18912)\n",
      "-----------------------------------------\n",
      "-----------------------------------------\n",
      "('pred', 9397.325)\n",
      "('W', 6.1918104e-06)\n",
      "('X', array(1.5177024e+09, dtype=float32))\n",
      "('b', 4.1465515e-15)\n",
      "('y', 9400.99)\n",
      "('powa', 13.432511)\n",
      "-----------------------------------------\n",
      "-----------------------------------------\n",
      "('pred', 8359.429)\n",
      "('W', 5.507636e-06)\n",
      "('X', array(1.5177888e+09, dtype=float32))\n",
      "('b', 3.695781e-15)\n",
      "('y', 8391.29)\n",
      "('powa', 1015.1442)\n",
      "-----------------------------------------\n",
      "-----------------------------------------\n",
      "('pred', 7918.797)\n",
      "('W', 5.2170276e-06)\n",
      "('X', array(1.5178752e+09, dtype=float32))\n",
      "('b', 3.5043235e-15)\n",
      "('y', 7932.38)\n",
      "('powa', 184.49811)\n",
      "-----------------------------------------\n",
      "-----------------------------------------\n",
      "('pred', 8667.121)\n",
      "('W', 5.7093853e-06)\n",
      "('X', array(1.518048e+09, dtype=float32))\n",
      "('b', 3.8286596e-15)\n",
      "('y', 8643.94)\n",
      "('powa', 537.3432)\n",
      "-----------------------------------------\n",
      "-----------------------------------------\n",
      "('pred', 8745.629)\n",
      "('W', 5.7607735e-06)\n",
      "('X', array(1.5181344e+09, dtype=float32))\n",
      "('b', 3.862509e-15)\n",
      "('y', 8743.2)\n",
      "('powa', 5.898637)\n",
      "-----------------------------------------\n",
      "Optimization Finished!\n",
      "Training cost= 27992192.0 W= 5.7607735e-06 b= 3.862509e-15 \n",
      "\n"
     ]
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f273142f2d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Start training\n",
    "with tf.Session() as sess:\n",
    "    sess.run(init)\n",
    "\n",
    "    # Fit all training data\n",
    "    for epoch in range(training_epochs):\n",
    "        for (x, y) in zip(train_X, train_Y):\n",
    "            sess.run(optimizer, feed_dict={X: x, Y: y})\n",
    "            print '-----------------------------------------'\n",
    "            print ('pred', sess.run(pred, feed_dict={X: x, Y: y}))\n",
    "            print ('W', sess.run(W))\n",
    "            print ('X', sess.run(X, feed_dict={X: x, Y: y}))\n",
    "            print ('b', sess.run(b))\n",
    "            print ('y', y)\n",
    "            print ('powa', sess.run(powa, feed_dict={X: x, Y: y}))\n",
    "            print '-----------------------------------------'\n",
    "\n",
    "\n",
    "        #Display logs per epoch step\n",
    "        if (epoch+1) % display_step == 0:\n",
    "            c = sess.run(cost, feed_dict={X: train_X, Y: train_Y})\n",
    "            print \"Epoch:\", '%04d' % (epoch+1), \"cost=\", \"{:.9f}\".format(c), \\\n",
    "                \"W=\", sess.run(W), \"b=\", sess.run(b)\n",
    "\n",
    "    print \"Optimization Finished!\"\n",
    "    training_cost = sess.run(cost, feed_dict={X: train_X, Y: train_Y})\n",
    "    print \"Training cost=\", training_cost, \"W=\", sess.run(W), \"b=\", sess.run(b), '\\n'\n",
    "\n",
    "    #Graphic display\n",
    "    plt.plot(train_X, train_Y, 'ro', label='Original data')\n",
    "    plt.plot(train_X, sess.run(W) * train_X + sess.run(b), label='Fitted line')\n",
    "    plt.legend()\n",
    "    plt.show()"
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