From afc05ec8d46aee68813aaa52837b9369f410cf2b Mon Sep 17 00:00:00 2001 From: sidneyriffic <31529542+sidneyriffic@users.noreply.github.com> Date: Sun, 1 Jul 2018 07:58:02 -0700 Subject: [PATCH 1/3] Hackathon deadline submission 2-24 is lowest loss of last model, 4-32 is a much farther epoch version of the last model. Original tri is the trident shaped precursor to the last model. The other "addforward" folders are from previous versions of the current model. --- config.py | 4 +- crypto_predict - pre-recursion.ipynb | 2658 ++++++++++++++++++++++++++ crypto_predict.ipynb | 1813 ++++++++++++------ weights 1-48/.gitignore | 5 + weights 2-24/.gitignore | 5 + weights 3-31/.gitignore | 5 + weights 4-32/.gitignore | 5 + weights addforward/.gitignore | 5 + weights addforward2/.gitignore | 5 + weights origtri/.gitignore | 5 + 10 files changed, 3944 insertions(+), 566 deletions(-) create mode 100644 crypto_predict - pre-recursion.ipynb create mode 100644 weights 1-48/.gitignore create mode 100644 weights 2-24/.gitignore create mode 100644 weights 3-31/.gitignore create mode 100644 weights 4-32/.gitignore create mode 100644 weights addforward/.gitignore create mode 100644 weights addforward2/.gitignore create mode 100644 weights origtri/.gitignore diff --git a/config.py b/config.py index 33a767d..787169c 100644 --- a/config.py +++ b/config.py @@ -1,5 +1,5 @@ # Which colums to use as input for the nerual network -columns = ['Close','Volume','Low','High'] +columns = ['Close', 'Volume', 'High', 'Low'] # Which currency pair are we interested in predicting pair = 'BTC_ETH' # or 'USDT_BTC' @@ -7,7 +7,7 @@ CONFIG = { 'pair': pair, 'period': 300, - 'input_size': 30, + 'input_size': 48, 'output_size': 12, 'lstm_hidden_size': 50, 'columns' : columns, diff --git a/crypto_predict - pre-recursion.ipynb b/crypto_predict - pre-recursion.ipynb new file mode 100644 index 0000000..fc54e9b --- /dev/null +++ b/crypto_predict - pre-recursion.ipynb @@ -0,0 +1,2658 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{'pair': 'BTC_ETH',\n", + " 'period': 300,\n", + " 'input_size': 60,\n", + " 'output_size': 12,\n", + " 'lstm_hidden_size': 50,\n", + " 'columns': ['Close', 'Volume'],\n", + " 'csv_src_file': 'BTC_ETH',\n", + " 'name': 'lstm',\n", + " 'folder': {'data': 'data/', 'weights': 'weights/'},\n", + " 'filename': 'BTC_ETH_lstm_i60_o12_Close_Volume'}" + ] + }, + "execution_count": 1, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from config import CONFIG\n", + "from utils import series_to_supervised\n", + "\n", + "CONFIG" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " Close Timestamp High Low Open Volume\n", + "269507 0.082828 1519862700 0.082856 0.082729 0.082729 4.151247\n", + "269508 0.082609 1519863000 0.082828 0.082606 0.082828 5.551513\n", + "269509 0.082552 1519863300 0.082673 0.082547 0.082609 2.327443\n", + "269510 0.082460 1519863600 0.082625 0.082419 0.082552 1.519736\n", + "269511 0.082455 1519863900 0.082460 0.082418 0.082455 0.552411" + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "import numpy as np\n", + "import pandas as pd\n", + "from sklearn.preprocessing import MinMaxScaler\n", + "from pandas import read_csv\n", + "from pandas import DataFrame\n", + "from pandas import concat\n", + "from matplotlib import pyplot\n", + " \n", + "input_size = 100\n", + "#data file path\n", + "dfp = ''.join([CONFIG['folder']['data'], CONFIG['csv_src_file'], '.csv'])\n", + "\n", + "#Columns of price data to use\n", + "columns = CONFIG['columns']\n", + "# df = pd.read_csv(dfp).dropna().tail(1000000)\n", + "dataset = pd.read_csv(dfp)\n", + "\n", + "# to drop values before 2018 1514764800, March 2018 1519862400, July 2017 1498867200\n", + "dataset = dataset[dataset.Timestamp > 1519862400]\n", + "dataset.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "29582\n" + ] + } + ], + "source": [ + "print(dataset.shape[0])" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "29582\n" + ] + } + ], + "source": [ + "#filter out unwanted columns and convert to numpy.ndarray\n", + "#loc[rows, cols] to get, .values gets values from a dataframe as a numpy.ndarray\n", + "values = dataset.loc[:,columns].values\n", + "print(values.shape[0])" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(60, 2, 12)" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# parameters to prepare the dataset for learning \n", + "n_lag = CONFIG['input_size']\n", + "n_out = CONFIG['output_size']\n", + "n_features = len(columns)\n", + "n_lag,n_features,n_out" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "29582\n" + ] + } + ], + "source": [ + "# scale dataset\n", + "scaler = MinMaxScaler(feature_range=(0, 1))\n", + "scaled = scaler.fit_transform(values)\n", + "print(scaled.shape[0])" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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var1(t-60)var2(t-60)var1(t-59)var2(t-59)var1(t-58)var2(t-58)var1(t-57)var2(t-57)var1(t-56)var2(t-56)...var1(t+7)var2(t+7)var1(t+8)var2(t+8)var1(t+9)var2(t+9)var1(t+10)var2(t+10)var1(t+11)var2(t+11)
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10 rows × 144 columns

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" + ], + "text/plain": [ + " var1(t-60) var2(t-60) var1(t-59) var2(t-59) var1(t-58) var2(t-58) \\\n", + "60 0.911499 0.005383 0.904896 0.007199 0.903185 0.003018 \n", + "61 0.904896 0.007199 0.903185 0.003018 0.900404 0.001971 \n", + "62 0.903185 0.003018 0.900404 0.001971 0.900251 0.000716 \n", + "63 0.900404 0.001971 0.900251 0.000716 0.899950 0.001567 \n", + "64 0.900251 0.000716 0.899950 0.001567 0.900536 0.001585 \n", + "65 0.899950 0.001567 0.900536 0.001585 0.898062 0.002247 \n", + "66 0.900536 0.001585 0.898062 0.002247 0.900530 0.001073 \n", + "67 0.898062 0.002247 0.900530 0.001073 0.898052 0.000990 \n", + "68 0.900530 0.001073 0.898052 0.000990 0.900246 0.001661 \n", + "69 0.898052 0.000990 0.900246 0.001661 0.900536 0.000313 \n", + "\n", + " var1(t-57) var2(t-57) var1(t-56) var2(t-56) ... var1(t+7) \\\n", + "60 0.900404 0.001971 0.900251 0.000716 ... 0.896031 \n", + "61 0.900251 0.000716 0.899950 0.001567 ... 0.900122 \n", + "62 0.899950 0.001567 0.900536 0.001585 ... 0.899499 \n", + "63 0.900536 0.001585 0.898062 0.002247 ... 0.900826 \n", + "64 0.898062 0.002247 0.900530 0.001073 ... 0.902605 \n", + "65 0.900530 0.001073 0.898052 0.000990 ... 0.905607 \n", + "66 0.898052 0.000990 0.900246 0.001661 ... 0.906037 \n", + "67 0.900246 0.001661 0.900536 0.000313 ... 0.903877 \n", + "68 0.900536 0.000313 0.901199 0.001925 ... 0.906416 \n", + "69 0.901199 0.001925 0.901833 0.000689 ... 0.911383 \n", + "\n", + " var2(t+7) var1(t+8) var2(t+8) var1(t+9) var2(t+9) var1(t+10) \\\n", + "60 0.000725 0.900122 0.002147 0.899499 0.002193 0.900826 \n", + "61 0.002147 0.899499 0.002193 0.900826 0.003644 0.902605 \n", + "62 0.002193 0.900826 0.003644 0.902605 0.000667 0.905607 \n", + "63 0.003644 0.902605 0.000667 0.905607 0.001138 0.906037 \n", + "64 0.000667 0.905607 0.001138 0.906037 0.001566 0.903877 \n", + "65 0.001138 0.906037 0.001566 0.903877 0.000476 0.906416 \n", + "66 0.001566 0.903877 0.000476 0.906416 0.003237 0.911383 \n", + "67 0.000476 0.906416 0.003237 0.911383 0.002917 0.912647 \n", + "68 0.003237 0.911383 0.002917 0.912647 0.003534 0.913068 \n", + "69 0.002917 0.912647 0.003534 0.913068 0.005535 0.909149 \n", + "\n", + " var2(t+10) var1(t+11) var2(t+11) \n", + "60 0.003644 0.902605 0.000667 \n", + "61 0.000667 0.905607 0.001138 \n", + "62 0.001138 0.906037 0.001566 \n", + "63 0.001566 0.903877 0.000476 \n", + "64 0.000476 0.906416 0.003237 \n", + "65 0.003237 0.911383 0.002917 \n", + "66 0.002917 0.912647 0.003534 \n", + "67 0.003534 0.913068 0.005535 \n", + "68 0.005535 0.909149 0.004790 \n", + "69 0.004790 0.906146 0.001807 \n", + "\n", + "[10 rows x 144 columns]" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# frame as supervised learning\n", + "reframed = series_to_supervised(scaled, n_lag, n_out)\n", + "reframed.head(10)" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "29511\n" + ] + } + ], + "source": [ + "print(reframed.shape[0])" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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0.001366 \n", + "64 0.898631 0.002870 0.898622 0.001366 0.900645 0.001475 \n", + "\n", + " var1(t-45) var2(t-45) var1(t-44) var2(t-44) var1(t-43) var2(t-43) \\\n", + "60 0.904161 0.001904 0.898631 0.002870 0.898622 0.001366 \n", + "61 0.898631 0.002870 0.898622 0.001366 0.900645 0.001475 \n", + "62 0.898622 0.001366 0.900645 0.001475 0.902122 0.003711 \n", + "63 0.900645 0.001475 0.902122 0.003711 0.901912 0.000501 \n", + "64 0.902122 0.003711 0.901912 0.000501 0.898889 0.001275 \n", + "\n", + " var1(t-42) var2(t-42) var1(t-41) var2(t-41) var1(t-40) var2(t-40) \\\n", + "60 0.900645 0.001475 0.902122 0.003711 0.901912 0.000501 \n", + "61 0.902122 0.003711 0.901912 0.000501 0.898889 0.001275 \n", + "62 0.901912 0.000501 0.898889 0.001275 0.897707 0.006063 \n", + "63 0.898889 0.001275 0.897707 0.006063 0.896295 0.005163 \n", + "64 0.897707 0.006063 0.896295 0.005163 0.901640 0.005457 \n", + "\n", + " var1(t-39) var2(t-39) var1(t-38) var2(t-38) var1(t-37) var2(t-37) \\\n", + "60 0.898889 0.001275 0.897707 0.006063 0.896295 0.005163 \n", + "61 0.897707 0.006063 0.896295 0.005163 0.901640 0.005457 \n", + "62 0.896295 0.005163 0.901640 0.005457 0.894162 0.001330 \n", + "63 0.901640 0.005457 0.894162 0.001330 0.901667 0.004633 \n", + "64 0.894162 0.001330 0.901667 0.004633 0.902515 0.000511 \n", + "\n", + " var1(t-36) var2(t-36) var1(t-35) var2(t-35) var1(t-34) var2(t-34) \\\n", + "60 0.901640 0.005457 0.894162 0.001330 0.901667 0.004633 \n", + "61 0.894162 0.001330 0.901667 0.004633 0.902515 0.000511 \n", + "62 0.901667 0.004633 0.902515 0.000511 0.902515 0.005043 \n", + "63 0.902515 0.000511 0.902515 0.005043 0.903902 0.001018 \n", + "64 0.902515 0.005043 0.903902 0.001018 0.911195 0.004273 \n", + "\n", + " var1(t-33) var2(t-33) var1(t-32) var2(t-32) var1(t-31) var2(t-31) \\\n", + "60 0.902515 0.000511 0.902515 0.005043 0.903902 0.001018 \n", + "61 0.902515 0.005043 0.903902 0.001018 0.911195 0.004273 \n", + "62 0.903902 0.001018 0.911195 0.004273 0.909011 0.000288 \n", + "63 0.911195 0.004273 0.909011 0.000288 0.908729 0.005163 \n", + "64 0.909011 0.000288 0.908729 0.005163 0.903118 0.000774 \n", + "\n", + " var1(t-30) var2(t-30) var1(t-29) var2(t-29) var1(t-28) var2(t-28) \\\n", + "60 0.911195 0.004273 0.909011 0.000288 0.908729 0.005163 \n", + "61 0.909011 0.000288 0.908729 0.005163 0.903118 0.000774 \n", + "62 0.908729 0.005163 0.903118 0.000774 0.904913 0.001256 \n", + "63 0.903118 0.000774 0.904913 0.001256 0.909531 0.000620 \n", + "64 0.904913 0.001256 0.909531 0.000620 0.907403 0.001534 \n", + "\n", + " var1(t-27) var2(t-27) var1(t-26) var2(t-26) var1(t-25) var2(t-25) \\\n", + "60 0.903118 0.000774 0.904913 0.001256 0.909531 0.000620 \n", + "61 0.904913 0.001256 0.909531 0.000620 0.907403 0.001534 \n", + "62 0.909531 0.000620 0.907403 0.001534 0.907393 0.001065 \n", + "63 0.907403 0.001534 0.907393 0.001065 0.907137 0.001018 \n", + "64 0.907393 0.001065 0.907137 0.001018 0.903187 0.000341 \n", + "\n", + " var1(t-24) var2(t-24) var1(t-23) var2(t-23) var1(t-22) var2(t-22) \\\n", + "60 0.907403 0.001534 0.907393 0.001065 0.907137 0.001018 \n", + "61 0.907393 0.001065 0.907137 0.001018 0.903187 0.000341 \n", + "62 0.907137 0.001018 0.903187 0.000341 0.903218 0.000487 \n", + "63 0.903187 0.000341 0.903218 0.000487 0.903810 0.001179 \n", + "64 0.903218 0.000487 0.903810 0.001179 0.909454 0.001097 \n", + "\n", + " var1(t-21) var2(t-21) var1(t-20) var2(t-20) var1(t-19) var2(t-19) \\\n", + "60 0.903187 0.000341 0.903218 0.000487 0.903810 0.001179 \n", + "61 0.903218 0.000487 0.903810 0.001179 0.909454 0.001097 \n", + "62 0.903810 0.001179 0.909454 0.001097 0.907357 0.000555 \n", + "63 0.909454 0.001097 0.907357 0.000555 0.911044 0.007564 \n", + "64 0.907357 0.000555 0.911044 0.007564 0.910657 0.003413 \n", + "\n", + " var1(t-18) var2(t-18) var1(t-17) var2(t-17) var1(t-16) var2(t-16) \\\n", + "60 0.909454 0.001097 0.907357 0.000555 0.911044 0.007564 \n", + "61 0.907357 0.000555 0.911044 0.007564 0.910657 0.003413 \n", + "62 0.911044 0.007564 0.910657 0.003413 0.907522 0.008177 \n", + "63 0.910657 0.003413 0.907522 0.008177 0.900615 0.003681 \n", + "64 0.907522 0.008177 0.900615 0.003681 0.906061 0.001641 \n", + "\n", + " var1(t-15) var2(t-15) var1(t-14) var2(t-14) var1(t-13) var2(t-13) \\\n", + "60 0.910657 0.003413 0.907522 0.008177 0.900615 0.003681 \n", + "61 0.907522 0.008177 0.900615 0.003681 0.906061 0.001641 \n", + "62 0.900615 0.003681 0.906061 0.001641 0.899258 0.016586 \n", + "63 0.906061 0.001641 0.899258 0.016586 0.903952 0.012982 \n", + "64 0.899258 0.016586 0.903952 0.012982 0.899530 0.006698 \n", + "\n", + " var1(t-12) var2(t-12) var1(t-11) var2(t-11) var1(t-10) var2(t-10) \\\n", + "60 0.906061 0.001641 0.899258 0.016586 0.903952 0.012982 \n", + "61 0.899258 0.016586 0.903952 0.012982 0.899530 0.006698 \n", + "62 0.903952 0.012982 0.899530 0.006698 0.899831 0.000562 \n", + "63 0.899530 0.006698 0.899831 0.000562 0.907336 0.004833 \n", + "64 0.899831 0.000562 0.907336 0.004833 0.907291 0.001092 \n", + "\n", + " var1(t-9) var2(t-9) var1(t-8) var2(t-8) var1(t-7) var2(t-7) \\\n", + "60 0.899530 0.006698 0.899831 0.000562 0.907336 0.004833 \n", + "61 0.899831 0.000562 0.907336 0.004833 0.907291 0.001092 \n", + "62 0.907336 0.004833 0.907291 0.001092 0.901101 0.000096 \n", + "63 0.907291 0.001092 0.901101 0.000096 0.902979 0.000749 \n", + "64 0.901101 0.000096 0.902979 0.000749 0.898715 0.003541 \n", + "\n", + " var1(t-6) var2(t-6) var1(t-5) var2(t-5) var1(t-4) var2(t-4) \\\n", + "60 0.907291 0.001092 0.901101 0.000096 0.902979 0.000749 \n", + "61 0.901101 0.000096 0.902979 0.000749 0.898715 0.003541 \n", + "62 0.902979 0.000749 0.898715 0.003541 0.899620 0.003343 \n", + "63 0.898715 0.003541 0.899620 0.003343 0.902619 0.000657 \n", + "64 0.899620 0.003343 0.902619 0.000657 0.898043 0.001283 \n", + "\n", + " var1(t-3) var2(t-3) var1(t-2) var2(t-2) var1(t-1) var2(t-1) \\\n", + "60 0.898715 0.003541 0.899620 0.003343 0.902619 0.000657 \n", + "61 0.899620 0.003343 0.902619 0.000657 0.898043 0.001283 \n", + "62 0.902619 0.000657 0.898043 0.001283 0.899926 0.001236 \n", + "63 0.898043 0.001283 0.899926 0.001236 0.904471 0.001412 \n", + "64 0.899926 0.001236 0.904471 0.001412 0.901639 0.000668 \n", + "\n", + " var1(t) var1(t+1) var1(t+2) var1(t+3) var1(t+4) var1(t+5) \\\n", + "60 0.898043 0.899926 0.904471 0.901639 0.904114 0.894464 \n", + "61 0.899926 0.904471 0.901639 0.904114 0.894464 0.894675 \n", + "62 0.904471 0.901639 0.904114 0.894464 0.894675 0.896031 \n", + "63 0.901639 0.904114 0.894464 0.894675 0.896031 0.900122 \n", + "64 0.904114 0.894464 0.894675 0.896031 0.900122 0.899499 \n", + "\n", + " var1(t+6) var1(t+7) var1(t+8) var1(t+9) var1(t+10) var1(t+11) \n", + "60 0.894675 0.896031 0.900122 0.899499 0.900826 0.902605 \n", + "61 0.896031 0.900122 0.899499 0.900826 0.902605 0.905607 \n", + "62 0.900122 0.899499 0.900826 0.902605 0.905607 0.906037 \n", + "63 0.899499 0.900826 0.902605 0.905607 0.906037 0.903877 \n", + "64 0.900826 0.902605 0.905607 0.906037 0.903877 0.906416 " + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# drop columns we don't want to predict\n", + "# We're only concerned with the estimating the close value,\n", + "# Close should be first in the list of column in the config file\n", + "\n", + "cols_to_drop = []\n", + "\n", + "for i in range (n_out):\n", + " for j in range(1, n_features):\n", + " cols_to_drop.append(reframed.shape[1]-(i*n_features+j))\n", + "\n", + "reframed.drop(reframed.columns[cols_to_drop], axis=1, inplace=True)\n", + "\n", + "pd.set_option('display.max_columns', 500)\n", + "reframed.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "29511\n" + ] + } + ], + "source": [ + "#ndarray.shape holds array dimensions in tuple form\n", + "print(reframed.values.shape[0])" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [], + "source": [ + "reframed_values = reframed.values\n", + "# split into train and test sets\n", + "training_size = int(0.8* reframed_values.shape[0])\n", + "train = reframed_values[:training_size, :]\n", + "test = reframed_values[training_size:, :]" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(23608, 60, 2) (23608, 12, 1) (5903, 60, 2) (5903, 12, 1)\n" + ] + } + ], + "source": [ + "# split into input and outputs\n", + "# n_obs is the length of input columns in our dataframe\n", + "n_obs = n_lag * n_features\n", + "\n", + "# We're only concerned with the estimating the close value,\n", + "# Close should be first in the list of column in the config file\n", + "\n", + "n_outputs = n_out * n_features\n", + "train_x, train_y = train[:, :n_obs], train[:, -n_out:]\n", + "test_x, test_y = test[:, :n_obs], test[:, -n_out:]\n", + "\n", + "# reshape input to be 3D [samples, timesteps, features]\n", + "train_x = train_x.reshape((train_x.shape[0], n_lag, n_features))\n", + "test_x = test_x.reshape((test_x.shape[0], n_lag, n_features))\n", + "\n", + "# reshape output to be 3D [samples, timesteps, features]\n", + "train_y = train_y.reshape(-1, n_out, 1)\n", + "test_y = test_y.reshape(-1, n_out, 1)\n", + "\n", + "print(train_x.shape, train_y.shape, test_x.shape, test_y.shape)" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [], + "source": [ + "model_name=''.join([CONFIG['folder']['weights'], CONFIG['filename'], '_model', '.json'])\n", + "model_weights_name=''.join([CONFIG['folder']['weights'], CONFIG['filename'], '_model_weights', '.h5'])" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [], + "source": [ + "epochs=25\n", + "batch_size=4096" + ] + }, + { + "cell_type": "code", + "execution_count": 110, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "__________________________________________________________________________________________________\n", + "Layer (type) Output Shape Param # Connected to \n", + "==================================================================================================\n", + "input_38 (InputLayer) (None, 60, 2) 0 \n", + "__________________________________________________________________________________________________\n", + "conv1d_1935 (Conv1D) (None, 59, 51) 255 input_38[0][0] \n", + "__________________________________________________________________________________________________\n", + "conv1d_1936 (Conv1D) (None, 58, 51) 5253 conv1d_1935[0][0] \n", + "__________________________________________________________________________________________________\n", + "conv1d_1937 (Conv1D) (None, 57, 50) 5150 conv1d_1936[0][0] \n", + "__________________________________________________________________________________________________\n", + "conv1d_1938 (Conv1D) (None, 56, 49) 4949 conv1d_1937[0][0] \n", + "__________________________________________________________________________________________________\n", + "conv1d_1939 (Conv1D) (None, 55, 48) 4752 conv1d_1938[0][0] \n", + "__________________________________________________________________________________________________\n", + "conv1d_1940 (Conv1D) (None, 54, 47) 4559 conv1d_1939[0][0] \n", + "__________________________________________________________________________________________________\n", + "conv1d_1941 (Conv1D) (None, 53, 46) 4370 conv1d_1940[0][0] \n", + "__________________________________________________________________________________________________\n", + "conv1d_1942 (Conv1D) (None, 52, 45) 4185 conv1d_1941[0][0] \n", + "__________________________________________________________________________________________________\n", + "conv1d_1943 (Conv1D) (None, 51, 44) 4004 conv1d_1942[0][0] \n", + "__________________________________________________________________________________________________\n", + "conv1d_1944 (Conv1D) (None, 50, 43) 3827 conv1d_1943[0][0] \n", + "__________________________________________________________________________________________________\n", + "conv1d_1945 (Conv1D) (None, 49, 42) 3654 conv1d_1944[0][0] \n", + "__________________________________________________________________________________________________\n", + "conv1d_1946 (Conv1D) (None, 48, 41) 3485 conv1d_1945[0][0] \n", + "__________________________________________________________________________________________________\n", + "conv1d_1947 (Conv1D) (None, 47, 40) 3320 conv1d_1946[0][0] \n", + "__________________________________________________________________________________________________\n", + "conv1d_1948 (Conv1D) (None, 46, 39) 3159 conv1d_1947[0][0] \n", + "__________________________________________________________________________________________________\n", + "conv1d_1949 (Conv1D) (None, 45, 38) 3002 conv1d_1948[0][0] \n", + "__________________________________________________________________________________________________\n", + "conv1d_1950 (Conv1D) (None, 44, 37) 2849 conv1d_1949[0][0] \n", + "__________________________________________________________________________________________________\n", + "conv1d_1951 (Conv1D) (None, 43, 36) 2700 conv1d_1950[0][0] \n", + "__________________________________________________________________________________________________\n", + "conv1d_1952 (Conv1D) (None, 42, 35) 2555 conv1d_1951[0][0] \n", + "__________________________________________________________________________________________________\n", + "conv1d_1953 (Conv1D) (None, 41, 34) 2414 conv1d_1952[0][0] \n", + "__________________________________________________________________________________________________\n", + "conv1d_1954 (Conv1D) (None, 40, 33) 2277 conv1d_1953[0][0] \n", + "__________________________________________________________________________________________________\n", + "conv1d_1955 (Conv1D) (None, 39, 32) 2144 conv1d_1954[0][0] \n", + "__________________________________________________________________________________________________\n", + "conv1d_1956 (Conv1D) (None, 38, 31) 2015 conv1d_1955[0][0] \n", + "__________________________________________________________________________________________________\n", + "conv1d_1957 (Conv1D) (None, 37, 30) 1890 conv1d_1956[0][0] \n", + "__________________________________________________________________________________________________\n", + "conv1d_1958 (Conv1D) (None, 36, 29) 1769 conv1d_1957[0][0] \n", + "__________________________________________________________________________________________________\n", + "conv1d_1959 (Conv1D) (None, 35, 28) 1652 conv1d_1958[0][0] \n", + "__________________________________________________________________________________________________\n", + "conv1d_1960 (Conv1D) (None, 34, 27) 1539 conv1d_1959[0][0] \n", + "__________________________________________________________________________________________________\n", + "conv1d_1961 (Conv1D) (None, 33, 26) 1430 conv1d_1960[0][0] \n", + "__________________________________________________________________________________________________\n", + "conv1d_1962 (Conv1D) (None, 32, 25) 1325 conv1d_1961[0][0] \n", + "__________________________________________________________________________________________________\n", + "conv1d_1963 (Conv1D) (None, 31, 24) 1224 conv1d_1962[0][0] \n", + "__________________________________________________________________________________________________\n", + "conv1d_1964 (Conv1D) (None, 30, 23) 1127 conv1d_1963[0][0] \n", + "__________________________________________________________________________________________________\n", + "conv1d_1965 (Conv1D) (None, 29, 22) 1034 conv1d_1964[0][0] \n", + "__________________________________________________________________________________________________\n", + "conv1d_1966 (Conv1D) (None, 28, 21) 945 conv1d_1965[0][0] \n", + "__________________________________________________________________________________________________\n", + "conv1d_1967 (Conv1D) (None, 27, 20) 860 conv1d_1966[0][0] \n", + "__________________________________________________________________________________________________\n", + "conv1d_1968 (Conv1D) (None, 26, 19) 779 conv1d_1967[0][0] \n", + "__________________________________________________________________________________________________\n", + "conv1d_1969 (Conv1D) (None, 25, 18) 702 conv1d_1968[0][0] \n", + "__________________________________________________________________________________________________\n", + "conv1d_1970 (Conv1D) (None, 24, 17) 629 conv1d_1969[0][0] \n", + "__________________________________________________________________________________________________\n", + "conv1d_1971 (Conv1D) (None, 23, 16) 560 conv1d_1970[0][0] \n", + "__________________________________________________________________________________________________\n", + "conv1d_1972 (Conv1D) (None, 22, 15) 495 conv1d_1971[0][0] \n", + "__________________________________________________________________________________________________\n", + "conv1d_1973 (Conv1D) (None, 21, 14) 434 conv1d_1972[0][0] \n", + "__________________________________________________________________________________________________\n", + "conv1d_1974 (Conv1D) (None, 20, 13) 377 conv1d_1973[0][0] \n", + "__________________________________________________________________________________________________\n", + "conv1d_1975 (Conv1D) (None, 19, 12) 324 conv1d_1974[0][0] \n", + "__________________________________________________________________________________________________\n", + "conv1d_1976 (Conv1D) (None, 18, 11) 275 conv1d_1975[0][0] \n", + "__________________________________________________________________________________________________\n", + "conv1d_1977 (Conv1D) (None, 17, 10) 230 conv1d_1976[0][0] \n", + "__________________________________________________________________________________________________\n", + "conv1d_1978 (Conv1D) (None, 16, 9) 189 conv1d_1977[0][0] \n", + "__________________________________________________________________________________________________\n", + "conv1d_1979 (Conv1D) (None, 15, 8) 152 conv1d_1978[0][0] \n", + "__________________________________________________________________________________________________\n", + "conv1d_1983 (Conv1D) (None, 49, 50) 1250 input_38[0][0] \n", + "__________________________________________________________________________________________________\n", + "conv1d_1980 (Conv1D) (None, 14, 7) 119 conv1d_1979[0][0] \n", + "__________________________________________________________________________________________________\n", + "lstm_363 (LSTM) (None, 15) 1080 input_38[0][0] \n", + "__________________________________________________________________________________________________\n", + "lstm_364 (LSTM) (None, 15) 1080 input_38[0][0] \n", + "__________________________________________________________________________________________________\n", + "lstm_365 (LSTM) (None, 15) 1080 input_38[0][0] \n", + "__________________________________________________________________________________________________\n", + "lstm_366 (LSTM) (None, 15) 1080 input_38[0][0] \n", + "__________________________________________________________________________________________________\n", + "lstm_367 (LSTM) (None, 15) 1080 input_38[0][0] \n", + "__________________________________________________________________________________________________\n", + "lstm_368 (LSTM) (None, 15) 1080 input_38[0][0] \n", + "__________________________________________________________________________________________________\n", + "lstm_369 (LSTM) (None, 15) 1080 input_38[0][0] \n", + "__________________________________________________________________________________________________\n", + "lstm_370 (LSTM) (None, 15) 1080 input_38[0][0] \n", + "__________________________________________________________________________________________________\n", + "lstm_371 (LSTM) (None, 15) 1080 input_38[0][0] \n", + "__________________________________________________________________________________________________\n", + "lstm_372 (LSTM) (None, 15) 1080 input_38[0][0] \n", + "__________________________________________________________________________________________________\n", + "lstm_373 (LSTM) (None, 15) 1080 input_38[0][0] \n", + "__________________________________________________________________________________________________\n", + "lstm_374 (LSTM) (None, 15) 1080 input_38[0][0] \n", + "__________________________________________________________________________________________________\n", + "lstm_375 (LSTM) (None, 10) 2440 conv1d_1983[0][0] \n", + "__________________________________________________________________________________________________\n", + "lstm_376 (LSTM) (None, 10) 2440 conv1d_1983[0][0] \n", + "__________________________________________________________________________________________________\n", + "lstm_377 (LSTM) (None, 10) 2440 conv1d_1983[0][0] \n", + "__________________________________________________________________________________________________\n", + "lstm_378 (LSTM) (None, 10) 2440 conv1d_1983[0][0] \n", + "__________________________________________________________________________________________________\n", + "lstm_379 (LSTM) (None, 10) 2440 conv1d_1983[0][0] \n", + "__________________________________________________________________________________________________\n", + "lstm_380 (LSTM) (None, 10) 2440 conv1d_1983[0][0] \n", + "__________________________________________________________________________________________________\n", + "lstm_381 (LSTM) (None, 10) 2440 conv1d_1983[0][0] \n", + "__________________________________________________________________________________________________\n", + "lstm_382 (LSTM) (None, 10) 2440 conv1d_1983[0][0] \n", + "__________________________________________________________________________________________________\n", + "lstm_383 (LSTM) (None, 10) 2440 conv1d_1983[0][0] \n", + "__________________________________________________________________________________________________\n", + "lstm_384 (LSTM) (None, 10) 2440 conv1d_1983[0][0] \n", + "__________________________________________________________________________________________________\n", + "lstm_385 (LSTM) (None, 10) 2440 conv1d_1983[0][0] \n", + "__________________________________________________________________________________________________\n", + "lstm_386 (LSTM) (None, 10) 2440 conv1d_1983[0][0] \n", + "__________________________________________________________________________________________________\n", + "conv1d_1981 (Conv1D) (None, 13, 6) 90 conv1d_1980[0][0] \n", + "__________________________________________________________________________________________________\n", + "concatenate_46 (Concatenate) (None, 180) 0 lstm_363[0][0] \n", + " lstm_364[0][0] \n", + " lstm_365[0][0] \n", + " lstm_366[0][0] \n", + " lstm_367[0][0] \n", + " lstm_368[0][0] \n", + " lstm_369[0][0] \n", + " lstm_370[0][0] \n", + " lstm_371[0][0] \n", + " lstm_372[0][0] \n", + " lstm_373[0][0] \n", + " lstm_374[0][0] \n", + "__________________________________________________________________________________________________\n", + "concatenate_47 (Concatenate) (None, 120) 0 lstm_375[0][0] \n", + " lstm_376[0][0] \n", + " lstm_377[0][0] \n", + " lstm_378[0][0] \n", + " lstm_379[0][0] \n", + " lstm_380[0][0] \n", + " lstm_381[0][0] \n", + " lstm_382[0][0] \n", + " lstm_383[0][0] \n", + " lstm_384[0][0] \n", + " lstm_385[0][0] \n", + " lstm_386[0][0] \n", + "__________________________________________________________________________________________________\n", + "conv1d_1982 (Conv1D) (None, 12, 5) 65 conv1d_1981[0][0] \n", + "__________________________________________________________________________________________________\n", + "reshape_10 (Reshape) (None, 12, 15) 0 concatenate_46[0][0] \n", + "__________________________________________________________________________________________________\n", + "reshape_11 (Reshape) (None, 12, 10) 0 concatenate_47[0][0] \n", + "__________________________________________________________________________________________________\n", + "concatenate_48 (Concatenate) (None, 12, 30) 0 conv1d_1982[0][0] \n", + " reshape_10[0][0] \n", + " reshape_11[0][0] \n", + "__________________________________________________________________________________________________\n", + "time_distributed_12 (TimeDistri (None, 12, 1) 31 concatenate_48[0][0] \n", + "==================================================================================================\n", + "Total params: 138,614\n", + "Trainable params: 138,614\n", + "Non-trainable params: 0\n", + "__________________________________________________________________________________________________\n" + ] + } + ], + "source": [ + "from keras.models import Sequential\n", + "from keras.layers import Dense, concatenate\n", + "from keras.layers import Input\n", + "from keras.layers import LSTM, CuDNNLSTM, GRU,CuDNNGRU\n", + "from keras.layers import Conv1D, AveragePooling1D, MaxPooling1D\n", + "from keras.layers import Dropout, Flatten\n", + "from keras.layers import Activation, BatchNormalization\n", + "from keras.layers import TimeDistributed\n", + "from keras.layers import Bidirectional\n", + "from keras.layers import RepeatVector\n", + "from keras.callbacks import ModelCheckpoint\n", + "import keras\n", + "\n", + "units= CONFIG['lstm_hidden_size']\n", + "dropout = .1\n", + "\n", + "# design network\n", + "inputs = Input(shape=(60, 2))\n", + "convmodel = Conv1D(51, 2)(inputs)\n", + "for i in range(1, 48):\n", + " convmodel = Conv1D(52-i, 2)(convmodel)\n", + "\n", + "rnnlist = []\n", + "for i in range(12):\n", + " rnnmodel = LSTM(15)(inputs)\n", + " rnnlist.append(rnnmodel)\n", + "rnnmodel = concatenate(rnnlist)\n", + "rnnmodel = keras.layers.Reshape((12, 15))(rnnmodel)\n", + "\n", + "convrnnprep = Conv1D(50, 12)(inputs)\n", + "convrnnlist = []\n", + "for i in range(12):\n", + " convrnnmodel = LSTM(10)(convrnnprep)\n", + " convrnnlist.append(convrnnmodel)\n", + "convrnn = concatenate(convrnnlist)\n", + "convrnn = keras.layers.Reshape((12, 10))(convrnn)\n", + "\n", + "combined = concatenate([convmodel, rnnmodel, convrnn])\n", + "combined = TimeDistributed(Dense(1))(combined)\n", + "#model.add(LSTM(100, input_shape=(train_x.shape[1], train_x.shape[2]), activation=None))\n", + "#model.add(RepeatVector(n_out))\n", + "\n", + "#model.add(LSTM(60))\n", + "#model.add(Dropout(.1))\n", + "\n", + "\n", + "\n", + "# We're only concerned with the estimating the close value,\n", + "# otherwise use n_outputs instead of 1\n", + "# Dense(n_outputs, ...\n", + "#model.add(TimeDistributed(Dense(1)))\n", + "model = keras.Model(inputs=inputs, outputs=combined)\n", + "model.compile(loss='mse', optimizer='adam')\n", + "\n", + "# store model\n", + "# serialize model to JSON\n", + "model_json = model.to_json()\n", + "with open(model_name, \"w\") as json_file:\n", + " json_file.write(model_json)\n", + "\n", + "model.summary()" + ] + }, + { + "cell_type": "code", + "execution_count": 111, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Train on 23608 samples, validate on 5903 samples\n", + "Epoch 1/25\n", + "23608/23608 [==============================] - 56s 2ms/step - loss: 0.2893 - val_loss: 0.2751\n", + "\n", + "Epoch 00001: val_loss improved from inf to 0.27510, saving model to weights/BTC_ETH_lstm_i60_o12_Close_Volume_model_weights.h5\n", + "Epoch 2/25\n", + "23608/23608 [==============================] - 20s 863us/step - loss: 0.0583 - val_loss: 0.0236\n", + "\n", + "Epoch 00002: val_loss improved from 0.27510 to 0.02358, saving model to weights/BTC_ETH_lstm_i60_o12_Close_Volume_model_weights.h5\n", + "Epoch 3/25\n", + "23608/23608 [==============================] - 21s 878us/step - loss: 0.0478 - val_loss: 0.0072\n", + "\n", + "Epoch 00003: val_loss improved from 0.02358 to 0.00718, saving model to weights/BTC_ETH_lstm_i60_o12_Close_Volume_model_weights.h5\n", + "Epoch 4/25\n", + "23608/23608 [==============================] - 21s 873us/step - loss: 0.0348 - val_loss: 0.0123\n", + "\n", + "Epoch 00004: val_loss did not improve from 0.00718\n", + "Epoch 5/25\n", + "23608/23608 [==============================] - 21s 877us/step - loss: 0.0063 - val_loss: 0.0036\n", + "\n", + "Epoch 00005: val_loss improved from 0.00718 to 0.00358, saving model to weights/BTC_ETH_lstm_i60_o12_Close_Volume_model_weights.h5\n", + "Epoch 6/25\n", + "23608/23608 [==============================] - 21s 883us/step - loss: 0.0036 - val_loss: 0.0078\n", + "\n", + "Epoch 00006: val_loss did not improve from 0.00358\n", + "Epoch 7/25\n", + "23608/23608 [==============================] - 21s 885us/step - loss: 0.0049 - val_loss: 0.0040\n", + "\n", + "Epoch 00007: val_loss did not improve from 0.00358\n", + "Epoch 8/25\n", + "23608/23608 [==============================] - 21s 888us/step - loss: 0.0085 - val_loss: 0.0055\n", + "\n", + "Epoch 00008: val_loss did not improve from 0.00358\n", + "Epoch 9/25\n", + "23608/23608 [==============================] - 21s 879us/step - loss: 0.0067 - val_loss: 9.5548e-04\n", + "\n", + "Epoch 00009: val_loss improved from 0.00358 to 0.00096, saving model to weights/BTC_ETH_lstm_i60_o12_Close_Volume_model_weights.h5\n", + "Epoch 10/25\n", + "23608/23608 [==============================] - 21s 880us/step - loss: 0.0082 - val_loss: 0.0132\n", + "\n", + "Epoch 00010: val_loss did not improve from 0.00096\n", + "Epoch 11/25\n", + "23608/23608 [==============================] - 20s 853us/step - loss: 0.0060 - val_loss: 0.0017\n", + "\n", + "Epoch 00011: val_loss did not improve from 0.00096\n", + "Epoch 12/25\n", + "23608/23608 [==============================] - 20s 853us/step - loss: 0.0045 - val_loss: 0.0015\n", + "\n", + "Epoch 00012: val_loss did not improve from 0.00096\n", + "Epoch 13/25\n", + "23608/23608 [==============================] - 20s 853us/step - loss: 0.0040 - val_loss: 3.3025e-04\n", + "\n", + "Epoch 00013: val_loss improved from 0.00096 to 0.00033, saving model to weights/BTC_ETH_lstm_i60_o12_Close_Volume_model_weights.h5\n", + "Epoch 14/25\n", + "23608/23608 [==============================] - 20s 866us/step - loss: 0.0013 - val_loss: 8.8446e-04\n", + "\n", + "Epoch 00014: val_loss did not improve from 0.00033\n", + "Epoch 15/25\n", + "23608/23608 [==============================] - 20s 850us/step - loss: 6.4106e-04 - val_loss: 7.3872e-04\n", + "\n", + "Epoch 00015: val_loss did not improve from 0.00033\n", + "Epoch 16/25\n", + "23608/23608 [==============================] - 20s 849us/step - loss: 6.9955e-04 - val_loss: 6.5034e-04\n", + "\n", + "Epoch 00016: val_loss did not improve from 0.00033\n", + "Epoch 17/25\n", + "23608/23608 [==============================] - 20s 851us/step - loss: 5.6615e-04 - val_loss: 2.7856e-04\n", + "\n", + "Epoch 00017: val_loss improved from 0.00033 to 0.00028, saving model to weights/BTC_ETH_lstm_i60_o12_Close_Volume_model_weights.h5\n", + "Epoch 18/25\n", + "23608/23608 [==============================] - 20s 856us/step - loss: 3.5684e-04 - val_loss: 2.4057e-04\n", + "\n", + "Epoch 00018: val_loss improved from 0.00028 to 0.00024, saving model to weights/BTC_ETH_lstm_i60_o12_Close_Volume_model_weights.h5\n", + "Epoch 19/25\n", + "23608/23608 [==============================] - 21s 869us/step - loss: 3.4699e-04 - val_loss: 3.5079e-04\n", + "\n", + "Epoch 00019: val_loss did not improve from 0.00024\n", + "Epoch 20/25\n", + "23608/23608 [==============================] - 20s 853us/step - loss: 3.4400e-04 - val_loss: 2.2245e-04\n", + "\n", + "Epoch 00020: val_loss improved from 0.00024 to 0.00022, saving model to weights/BTC_ETH_lstm_i60_o12_Close_Volume_model_weights.h5\n", + "Epoch 21/25\n", + "23608/23608 [==============================] - 20s 848us/step - loss: 3.0070e-04 - val_loss: 2.4665e-04\n", + "\n", + "Epoch 00021: val_loss did not improve from 0.00022\n", + "Epoch 22/25\n", + "23608/23608 [==============================] - 20s 862us/step - loss: 2.8406e-04 - val_loss: 2.9794e-04\n", + "\n", + "Epoch 00022: val_loss did not improve from 0.00022\n", + "Epoch 23/25\n", + "23608/23608 [==============================] - 20s 858us/step - loss: 2.9385e-04 - val_loss: 2.2953e-04\n", + "\n", + "Epoch 00023: val_loss did not improve from 0.00022\n", + "Epoch 24/25\n", + "23608/23608 [==============================] - 20s 860us/step - loss: 2.6628e-04 - val_loss: 2.3367e-04\n", + "\n", + "Epoch 00024: val_loss did not improve from 0.00022\n", + "Epoch 25/25\n", + "23608/23608 [==============================] - 20s 856us/step - loss: 2.5383e-04 - val_loss: 2.5620e-04\n", + "\n", + "Epoch 00025: val_loss did not improve from 0.00022\n" + ] + } + ], + "source": [ + "# fit network\n", + "history = model.fit(train_x, train_y, epochs=epochs, batch_size=batch_size,\n", + " validation_data=(test_x, test_y), verbose=1, shuffle=False,\n", + " callbacks=[ModelCheckpoint(model_weights_name, monitor='val_loss', verbose=1,save_best_only='true',\n", + " save_weights_only=True)])" + ] + }, + { + "cell_type": "code", + "execution_count": 112, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "__________________________________________________________________________________________________\n", + "Layer (type) Output Shape Param # Connected to \n", + "==================================================================================================\n", + "input_38 (InputLayer) (None, 60, 2) 0 \n", + "__________________________________________________________________________________________________\n", + "conv1d_1935 (Conv1D) (None, 59, 51) 255 input_38[0][0] \n", + "__________________________________________________________________________________________________\n", + "conv1d_1936 (Conv1D) (None, 58, 51) 5253 conv1d_1935[0][0] \n", + "__________________________________________________________________________________________________\n", + "conv1d_1937 (Conv1D) (None, 57, 50) 5150 conv1d_1936[0][0] \n", + "__________________________________________________________________________________________________\n", + "conv1d_1938 (Conv1D) (None, 56, 49) 4949 conv1d_1937[0][0] \n", + "__________________________________________________________________________________________________\n", + "conv1d_1939 (Conv1D) (None, 55, 48) 4752 conv1d_1938[0][0] \n", + "__________________________________________________________________________________________________\n", + "conv1d_1940 (Conv1D) (None, 54, 47) 4559 conv1d_1939[0][0] \n", + "__________________________________________________________________________________________________\n", + "conv1d_1941 (Conv1D) (None, 53, 46) 4370 conv1d_1940[0][0] \n", + "__________________________________________________________________________________________________\n", + "conv1d_1942 (Conv1D) (None, 52, 45) 4185 conv1d_1941[0][0] \n", + "__________________________________________________________________________________________________\n", + "conv1d_1943 (Conv1D) (None, 51, 44) 4004 conv1d_1942[0][0] \n", + "__________________________________________________________________________________________________\n", + "conv1d_1944 (Conv1D) (None, 50, 43) 3827 conv1d_1943[0][0] \n", + "__________________________________________________________________________________________________\n", + "conv1d_1945 (Conv1D) (None, 49, 42) 3654 conv1d_1944[0][0] \n", + "__________________________________________________________________________________________________\n", + "conv1d_1946 (Conv1D) (None, 48, 41) 3485 conv1d_1945[0][0] \n", + "__________________________________________________________________________________________________\n", + "conv1d_1947 (Conv1D) (None, 47, 40) 3320 conv1d_1946[0][0] \n", + "__________________________________________________________________________________________________\n", + "conv1d_1948 (Conv1D) (None, 46, 39) 3159 conv1d_1947[0][0] \n", + "__________________________________________________________________________________________________\n", + "conv1d_1949 (Conv1D) (None, 45, 38) 3002 conv1d_1948[0][0] \n", + "__________________________________________________________________________________________________\n", + "conv1d_1950 (Conv1D) (None, 44, 37) 2849 conv1d_1949[0][0] \n", + "__________________________________________________________________________________________________\n", + "conv1d_1951 (Conv1D) (None, 43, 36) 2700 conv1d_1950[0][0] \n", + "__________________________________________________________________________________________________\n", + "conv1d_1952 (Conv1D) (None, 42, 35) 2555 conv1d_1951[0][0] \n", + "__________________________________________________________________________________________________\n", + "conv1d_1953 (Conv1D) (None, 41, 34) 2414 conv1d_1952[0][0] \n", + "__________________________________________________________________________________________________\n", + "conv1d_1954 (Conv1D) (None, 40, 33) 2277 conv1d_1953[0][0] \n", + "__________________________________________________________________________________________________\n", + "conv1d_1955 (Conv1D) (None, 39, 32) 2144 conv1d_1954[0][0] \n", + "__________________________________________________________________________________________________\n", + "conv1d_1956 (Conv1D) (None, 38, 31) 2015 conv1d_1955[0][0] \n", + "__________________________________________________________________________________________________\n", + "conv1d_1957 (Conv1D) (None, 37, 30) 1890 conv1d_1956[0][0] \n", + "__________________________________________________________________________________________________\n", + "conv1d_1958 (Conv1D) (None, 36, 29) 1769 conv1d_1957[0][0] \n", + "__________________________________________________________________________________________________\n", + "conv1d_1959 (Conv1D) (None, 35, 28) 1652 conv1d_1958[0][0] \n", + "__________________________________________________________________________________________________\n", + "conv1d_1960 (Conv1D) (None, 34, 27) 1539 conv1d_1959[0][0] \n", + "__________________________________________________________________________________________________\n", + "conv1d_1961 (Conv1D) (None, 33, 26) 1430 conv1d_1960[0][0] \n", + "__________________________________________________________________________________________________\n", + "conv1d_1962 (Conv1D) (None, 32, 25) 1325 conv1d_1961[0][0] \n", + "__________________________________________________________________________________________________\n", + "conv1d_1963 (Conv1D) (None, 31, 24) 1224 conv1d_1962[0][0] \n", + "__________________________________________________________________________________________________\n", + "conv1d_1964 (Conv1D) (None, 30, 23) 1127 conv1d_1963[0][0] \n", + "__________________________________________________________________________________________________\n", + "conv1d_1965 (Conv1D) (None, 29, 22) 1034 conv1d_1964[0][0] \n", + "__________________________________________________________________________________________________\n", + "conv1d_1966 (Conv1D) (None, 28, 21) 945 conv1d_1965[0][0] \n", + "__________________________________________________________________________________________________\n", + "conv1d_1967 (Conv1D) (None, 27, 20) 860 conv1d_1966[0][0] \n", + "__________________________________________________________________________________________________\n", + "conv1d_1968 (Conv1D) (None, 26, 19) 779 conv1d_1967[0][0] \n", + "__________________________________________________________________________________________________\n", + "conv1d_1969 (Conv1D) (None, 25, 18) 702 conv1d_1968[0][0] \n", + "__________________________________________________________________________________________________\n", + "conv1d_1970 (Conv1D) (None, 24, 17) 629 conv1d_1969[0][0] \n", + "__________________________________________________________________________________________________\n", + "conv1d_1971 (Conv1D) (None, 23, 16) 560 conv1d_1970[0][0] \n", + "__________________________________________________________________________________________________\n", + "conv1d_1972 (Conv1D) (None, 22, 15) 495 conv1d_1971[0][0] \n", + "__________________________________________________________________________________________________\n", + "conv1d_1973 (Conv1D) (None, 21, 14) 434 conv1d_1972[0][0] \n", + "__________________________________________________________________________________________________\n", + "conv1d_1974 (Conv1D) (None, 20, 13) 377 conv1d_1973[0][0] \n", + "__________________________________________________________________________________________________\n", + "conv1d_1975 (Conv1D) (None, 19, 12) 324 conv1d_1974[0][0] \n", + "__________________________________________________________________________________________________\n", + "conv1d_1976 (Conv1D) (None, 18, 11) 275 conv1d_1975[0][0] \n", + "__________________________________________________________________________________________________\n", + "conv1d_1977 (Conv1D) (None, 17, 10) 230 conv1d_1976[0][0] \n", + "__________________________________________________________________________________________________\n", + "conv1d_1978 (Conv1D) (None, 16, 9) 189 conv1d_1977[0][0] \n", + "__________________________________________________________________________________________________\n", + "conv1d_1979 (Conv1D) (None, 15, 8) 152 conv1d_1978[0][0] \n", + "__________________________________________________________________________________________________\n", + "conv1d_1983 (Conv1D) (None, 49, 50) 1250 input_38[0][0] \n", + "__________________________________________________________________________________________________\n", + "conv1d_1980 (Conv1D) (None, 14, 7) 119 conv1d_1979[0][0] \n", + "__________________________________________________________________________________________________\n", + "lstm_363 (LSTM) (None, 15) 1080 input_38[0][0] \n", + "__________________________________________________________________________________________________\n", + "lstm_364 (LSTM) (None, 15) 1080 input_38[0][0] \n", + "__________________________________________________________________________________________________\n", + "lstm_365 (LSTM) (None, 15) 1080 input_38[0][0] \n", + "__________________________________________________________________________________________________\n", + "lstm_366 (LSTM) (None, 15) 1080 input_38[0][0] \n", + "__________________________________________________________________________________________________\n", + "lstm_367 (LSTM) (None, 15) 1080 input_38[0][0] \n", + "__________________________________________________________________________________________________\n", + "lstm_368 (LSTM) (None, 15) 1080 input_38[0][0] \n", + "__________________________________________________________________________________________________\n", + "lstm_369 (LSTM) (None, 15) 1080 input_38[0][0] \n", + "__________________________________________________________________________________________________\n", + "lstm_370 (LSTM) (None, 15) 1080 input_38[0][0] \n", + "__________________________________________________________________________________________________\n", + "lstm_371 (LSTM) (None, 15) 1080 input_38[0][0] \n", + "__________________________________________________________________________________________________\n", + "lstm_372 (LSTM) (None, 15) 1080 input_38[0][0] \n", + "__________________________________________________________________________________________________\n", + "lstm_373 (LSTM) (None, 15) 1080 input_38[0][0] \n", + "__________________________________________________________________________________________________\n", + "lstm_374 (LSTM) (None, 15) 1080 input_38[0][0] \n", + "__________________________________________________________________________________________________\n", + "lstm_375 (LSTM) (None, 10) 2440 conv1d_1983[0][0] \n", + "__________________________________________________________________________________________________\n", + "lstm_376 (LSTM) (None, 10) 2440 conv1d_1983[0][0] \n", + "__________________________________________________________________________________________________\n", + "lstm_377 (LSTM) (None, 10) 2440 conv1d_1983[0][0] \n", + "__________________________________________________________________________________________________\n", + "lstm_378 (LSTM) (None, 10) 2440 conv1d_1983[0][0] \n", + "__________________________________________________________________________________________________\n", + "lstm_379 (LSTM) (None, 10) 2440 conv1d_1983[0][0] \n", + "__________________________________________________________________________________________________\n", + "lstm_380 (LSTM) (None, 10) 2440 conv1d_1983[0][0] \n", + "__________________________________________________________________________________________________\n", + "lstm_381 (LSTM) (None, 10) 2440 conv1d_1983[0][0] \n", + "__________________________________________________________________________________________________\n", + "lstm_382 (LSTM) (None, 10) 2440 conv1d_1983[0][0] \n", + "__________________________________________________________________________________________________\n", + "lstm_383 (LSTM) (None, 10) 2440 conv1d_1983[0][0] \n", + "__________________________________________________________________________________________________\n", + "lstm_384 (LSTM) (None, 10) 2440 conv1d_1983[0][0] \n", + "__________________________________________________________________________________________________\n", + "lstm_385 (LSTM) (None, 10) 2440 conv1d_1983[0][0] \n", + "__________________________________________________________________________________________________\n", + "lstm_386 (LSTM) (None, 10) 2440 conv1d_1983[0][0] \n", + "__________________________________________________________________________________________________\n", + "conv1d_1981 (Conv1D) (None, 13, 6) 90 conv1d_1980[0][0] \n", + "__________________________________________________________________________________________________\n", + "concatenate_46 (Concatenate) (None, 180) 0 lstm_363[0][0] \n", + " lstm_364[0][0] \n", + " lstm_365[0][0] \n", + " lstm_366[0][0] \n", + " lstm_367[0][0] \n", + " lstm_368[0][0] \n", + " lstm_369[0][0] \n", + " lstm_370[0][0] \n", + " lstm_371[0][0] \n", + " lstm_372[0][0] \n", + " lstm_373[0][0] \n", + " lstm_374[0][0] \n", + "__________________________________________________________________________________________________\n", + "concatenate_47 (Concatenate) (None, 120) 0 lstm_375[0][0] \n", + " lstm_376[0][0] \n", + " lstm_377[0][0] \n", + " lstm_378[0][0] \n", + " lstm_379[0][0] \n", + " lstm_380[0][0] \n", + " lstm_381[0][0] \n", + " lstm_382[0][0] \n", + " lstm_383[0][0] \n", + " lstm_384[0][0] \n", + " lstm_385[0][0] \n", + " lstm_386[0][0] \n", + "__________________________________________________________________________________________________\n", + "conv1d_1982 (Conv1D) (None, 12, 5) 65 conv1d_1981[0][0] \n", + "__________________________________________________________________________________________________\n", + "reshape_10 (Reshape) (None, 12, 15) 0 concatenate_46[0][0] \n", + "__________________________________________________________________________________________________\n", + "reshape_11 (Reshape) (None, 12, 10) 0 concatenate_47[0][0] \n", + "__________________________________________________________________________________________________\n", + "concatenate_48 (Concatenate) (None, 12, 30) 0 conv1d_1982[0][0] \n", + " reshape_10[0][0] \n", + " reshape_11[0][0] \n", + "__________________________________________________________________________________________________\n", + "time_distributed_12 (TimeDistri (None, 12, 1) 31 concatenate_48[0][0] \n", + "==================================================================================================\n", + "Total params: 138,614\n", + "Trainable params: 138,614\n", + "Non-trainable params: 0\n", + "__________________________________________________________________________________________________\n" + ] + } + ], + "source": [ + "model.summary()" + ] + }, + { + "cell_type": "code", + "execution_count": 113, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "5903/5903 [==============================] - 123s 21ms/step\n" + ] + }, + { + "data": { + "text/plain": [ + "0.00022245472163341716" + ] + }, + "execution_count": 113, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Load the best weights\n", + "model.load_weights(model_weights_name)\n", + "model.compile(loss='mse', optimizer='adam')\n", + "model.evaluate(test_x, test_y)" + ] + }, + { + "cell_type": "code", + "execution_count": 114, + "metadata": {}, + "outputs": [], + "source": [ + "from matplotlib import pyplot" + ] + }, + { + "cell_type": "code", + "execution_count": 115, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# plot history\n", + "pyplot.plot(history.history['loss'], label='train')\n", + "pyplot.plot(history.history['val_loss'], label='test')\n", + "pyplot.legend()\n", + "pyplot.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 116, + "metadata": {}, + "outputs": [], + "source": [ + "# Test the prediction of test data\n", + "y = model.predict(test_x)" + ] + }, + { + "cell_type": "code", + "execution_count": 117, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[[[0.89370847]\n", + " [0.8941554 ]\n", + " [0.8921354 ]\n", + " ...\n", + " [0.9068522 ]\n", + " [0.90129775]\n", + " [0.9033782 ]]\n", + "\n", + " [[0.89269084]\n", + " [0.89432865]\n", + " [0.8898143 ]\n", + " ...\n", + " [0.906184 ]\n", + " [0.90072817]\n", + " [0.9034941 ]]\n", + "\n", + " [[0.89296615]\n", + " [0.89224124]\n", + " [0.89111066]\n", + " ...\n", + " [0.90465367]\n", + " [0.90097696]\n", + " [0.9023254 ]]\n", + "\n", + " ...\n", + "\n", + " [[0.7624873 ]\n", + " [0.76231253]\n", + " [0.75495976]\n", + " ...\n", + " [0.75079954]\n", + " [0.75208455]\n", + " [0.7541791 ]]\n", + "\n", + " [[0.76177305]\n", + " [0.7606786 ]\n", + " [0.7531638 ]\n", + " ...\n", + " [0.75130105]\n", + " [0.7530452 ]\n", + " [0.7575472 ]]\n", + "\n", + " [[0.75973284]\n", + " [0.75883037]\n", + " [0.7512839 ]\n", + " ...\n", + " [0.750189 ]\n", + " [0.7578398 ]\n", + " [0.75663 ]]]\n" + ] + } + ], + "source": [ + "print(y)" + ] + }, + { + "cell_type": "code", + "execution_count": 118, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "a = test_y[:,0]\n", + "b = y[:,0]\n", + "c = np.append(b, y[-1], axis=0)\n", + "\n", + "pyplot.rcParams[\"figure.figsize\"] = [16,7]\n", + "# Show how the model fits the test data\n", + "pyplot.plot(a[:500], label='original')\n", + "pyplot.plot(b[:500], label='model')\n", + "pyplot.legend()\n", + "pyplot.show()\n", + "\n", + "# Show how the model predicts data\n", + "pos = int(a.shape[0]-n_out*4)\n", + "pyplot.plot(a[pos:], label='original')\n", + "pyplot.plot(c[pos:], label='model')\n", + "pyplot.legend()\n", + "pyplot.show()\n" + ] + }, + { + "cell_type": "code", + "execution_count": 104, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[[0.91679865]\n", + " [0.9129281 ]\n", + " [0.916991 ]\n", + " ...\n", + " [0.7308158 ]\n", + " [0.7361067 ]\n", + " [0.7354168 ]]\n" + ] + } + ], + "source": [ + "print(c)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "#Prediction on public data!\n", + "period = CONFIG['period']\n", + "import time\n", + "import urllib\n", + "import json\n", + "\n", + "# Download a live bitcoin price data set\n", + "def dl_X(now = None, points = n_lag, period = period, pair=CONFIG['pair']):\n", + " if now == None:\n", + " now = time.time() \n", + " end = now - now % period\n", + " #print end, time.strftime(\"%a, %d %b %Y %H:%M:%S +0000\", time.gmtime(end))\n", + " start = end - points*period\n", + " #print start, time.strftime(\"%a, %d %b %Y %H:%M:%S +0000\", time.gmtime(start))\n", + " url = \"https://poloniex.com/public?command=returnChartData¤cyPair=%s&start=%d&end=%d&period=%d\" % (pair, start, end, period)\n", + " openUrl = urllib.request.urlopen(url)\n", + " r = openUrl.read()\n", + " openUrl.close()\n", + " d = json.loads(r.decode())[-n_lag:]\n", + " df = pd.DataFrame(d)\n", + " original_columns=[u'close', u'date', u'high', u'low', u'open',u'volume']\n", + " new_columns = ['Close','Timestamp','High','Low','Open','Volume']\n", + " df = df.loc[:,original_columns]\n", + " df.columns = new_columns\n", + " \n", + " return df" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "def predict(when=None):\n", + " rt_df = dl_X(when)\n", + " rt_values = rt_df.loc[:,columns].values\n", + " rt_scaled = scaler.transform(rt_values)\n", + " rt_x = rt_scaled.reshape((1, n_lag, n_features))\n", + " print(rt_x.shape)\n", + " return rt_scaled, model.predict(rt_x)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# do some now & past predictions\n", + "for t in [0, 100, 200, 300, 500, 1000, 2000]:\n", + " rt_x, prediction = predict(time.time()-t*period)\n", + "\n", + " current = rt_x[:,0]\n", + " prediction = prediction[0]\n", + "\n", + " pyplot.plot(current, label='current')\n", + "\n", + " # shift train predictions for plotting\n", + " predictPlot = np.empty_like(current)\n", + " predictPlot[:] = np.nan\n", + " predictPlot = np.append(predictPlot, prediction)\n", + "\n", + " pyplot.plot(predictPlot, label='prediction')\n", + " pyplot.legend()\n", + " pyplot.show()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "#Prediction on live data!\n", + "\"\"\"\n", + "starttime=time.time()\n", + "while True:\n", + " now = time.time() \n", + " end = now - now % period\n", + " print(time.strftime(\"%a, %d %b %Y %H:%M:%S +0000\", time.gmtime(end)))\n", + " rt_x, prediction = predict()\n", + "\n", + " current = rt_x[:,0]\n", + " prediction = prediction[0]\n", + "\n", + " pyplot.plot(current, label='current')\n", + "\n", + " # shift train predictions for plotting\n", + " predictPlot = np.empty_like(current)\n", + " predictPlot[:] = np.nan\n", + " predictPlot = np.append(predictPlot, prediction)\n", + "\n", + " pyplot.plot(predictPlot, label='prediction')\n", + " pyplot.legend()\n", + " pyplot.show()\n", + "\n", + " time.sleep(period - ((time.time() - starttime) % period))\"\"\"" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.6" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/crypto_predict.ipynb b/crypto_predict.ipynb index fd57577..8159e03 100644 --- a/crypto_predict.ipynb +++ b/crypto_predict.ipynb @@ -8,16 +8,16 @@ { "data": { "text/plain": [ - "{'columns': ['Close', 'Volume', 'Low', 'High'],\n", - " 'csv_src_file': 'BTC_ETH',\n", - " 'filename': 'BTC_ETH_lstm_i30_o12_Close_Volume_Low_High',\n", - " 'folder': {'data': 'data/', 'weights': 'weights/'},\n", - " 'input_size': 30,\n", + "{'pair': 'BTC_ETH',\n", + " 'period': 300,\n", + " 'input_size': 48,\n", + " 'output_size': 12,\n", " 'lstm_hidden_size': 50,\n", + " 'columns': ['Close', 'Volume', 'High', 'Low'],\n", + " 'csv_src_file': 'BTC_ETH',\n", " 'name': 'lstm',\n", - " 'output_size': 12,\n", - " 'pair': 'BTC_ETH',\n", - " 'period': 300}" + " 'folder': {'data': 'data/', 'weights': 'weights/'},\n", + " 'filename': 'BTC_ETH_lstm_i48_o12_Close_Volume_High_Low'}" ] }, "execution_count": 1, @@ -138,7 +138,7 @@ "from pandas import DataFrame\n", "from pandas import concat\n", "from matplotlib import pyplot\n", - " \n", + "\n", "#data file path\n", "dfp = ''.join([CONFIG['folder']['data'], CONFIG['csv_src_file'], '.csv'])\n", "\n", @@ -156,23 +156,51 @@ "cell_type": "code", "execution_count": 3, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "29582\n" + ] + } + ], "source": [ - "values = dataset.loc[:,columns].values" + "print(dataset.shape[0])" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "29582\n" + ] + } + ], + "source": [ + "#filter out unwanted columns and convert to numpy.ndarray\n", + "#loc[rows, cols] to get, .values gets values from a dataframe as a numpy.ndarray\n", + "values = dataset.loc[:,columns].values\n", + "print(values.shape[0])" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "(30, 4, 12)" + "(48, 4, 12)" ] }, - "execution_count": 4, + "execution_count": 5, "metadata": {}, "output_type": "execute_result" } @@ -187,18 +215,27 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 6, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "29582\n" + ] + } + ], "source": [ "# scale dataset\n", "scaler = MinMaxScaler(feature_range=(0, 1))\n", - "scaled = scaler.fit_transform(values)" + "scaled = scaler.fit_transform(values)\n", + "print(scaled.shape[0])" ] }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 7, "metadata": {}, "outputs": [ { @@ -222,16 +259,16 @@ " \n", " \n", " \n", - " var1(t-30)\n", - " var2(t-30)\n", - " var3(t-30)\n", - " var4(t-30)\n", - " var1(t-29)\n", - " var2(t-29)\n", - " var3(t-29)\n", - " var4(t-29)\n", - " var1(t-28)\n", - " var2(t-28)\n", + " var1(t-48)\n", + " var2(t-48)\n", + " var3(t-48)\n", + " var4(t-48)\n", + " var1(t-47)\n", + " var2(t-47)\n", + " var3(t-47)\n", + " var4(t-47)\n", + " var1(t-46)\n", + " var2(t-46)\n", " ...\n", " var3(t+9)\n", " var4(t+9)\n", @@ -247,303 +284,303 @@ " \n", " \n", " \n", - " 30\n", + " 48\n", " 0.911499\n", " 0.005383\n", - " 0.909966\n", " 0.888220\n", + " 0.909966\n", " 0.904896\n", " 0.007199\n", - " 0.906295\n", " 0.887362\n", + " 0.906295\n", " 0.903185\n", " 0.003018\n", " ...\n", - " 0.902730\n", - " 0.882924\n", - " 0.903218\n", - " 0.000487\n", - " 0.902156\n", - " 0.882912\n", - " 0.903810\n", - " 0.001179\n", - " 0.902319\n", - " 0.882907\n", + " 0.878685\n", + " 0.897946\n", + " 0.899620\n", + " 0.003343\n", + " 0.875276\n", + " 0.896600\n", + " 0.902619\n", + " 0.000657\n", + " 0.878343\n", + " 0.901145\n", " \n", " \n", - " 31\n", + " 49\n", " 0.904896\n", " 0.007199\n", - " 0.906295\n", " 0.887362\n", + " 0.906295\n", " 0.903185\n", " 0.003018\n", - " 0.904533\n", " 0.882607\n", + " 0.904533\n", " 0.900404\n", " 0.001971\n", " ...\n", - " 0.902156\n", - " 0.882912\n", - " 0.903810\n", - " 0.001179\n", - " 0.902319\n", - " 0.882907\n", - " 0.909454\n", - " 0.001097\n", - " 0.905317\n", - " 0.885319\n", + " 0.875276\n", + " 0.896600\n", + " 0.902619\n", + " 0.000657\n", + " 0.878343\n", + " 0.901145\n", + " 0.898043\n", + " 0.001283\n", + " 0.877999\n", + " 0.896614\n", " \n", " \n", - " 32\n", + " 50\n", " 0.903185\n", " 0.003018\n", - " 0.904533\n", " 0.882607\n", + " 0.904533\n", " 0.900404\n", " 0.001971\n", - " 0.900683\n", " 0.881134\n", + " 0.900683\n", " 0.900251\n", " 0.000716\n", " ...\n", - " 0.902319\n", - " 0.882907\n", - " 0.909454\n", - " 0.001097\n", - " 0.905317\n", - " 0.885319\n", - " 0.907357\n", - " 0.000555\n", - " 0.908805\n", - " 0.885254\n", + " 0.878343\n", + " 0.901145\n", + " 0.898043\n", + " 0.001283\n", + " 0.877999\n", + " 0.896614\n", + " 0.899926\n", + " 0.001236\n", + " 0.875713\n", + " 0.898274\n", " \n", " \n", - " 33\n", + " 51\n", " 0.900404\n", " 0.001971\n", - " 0.900683\n", " 0.881134\n", + " 0.900683\n", " 0.900251\n", " 0.000716\n", - " 0.900682\n", " 0.876073\n", + " 0.900682\n", " 0.899950\n", " 0.001567\n", " ...\n", - " 0.905317\n", - " 0.885319\n", - " 0.907357\n", - " 0.000555\n", - " 0.908805\n", - " 0.885254\n", - " 0.911044\n", - " 0.007564\n", - " 0.906050\n", - " 0.887050\n", + " 0.877999\n", + " 0.896614\n", + " 0.899926\n", + " 0.001236\n", + " 0.875713\n", + " 0.898274\n", + " 0.904471\n", + " 0.001412\n", + " 0.880367\n", + " 0.901454\n", " \n", " \n", - " 34\n", + " 52\n", " 0.900251\n", " 0.000716\n", - " 0.900682\n", " 0.876073\n", + " 0.900682\n", " 0.899950\n", " 0.001567\n", - " 0.899785\n", " 0.879122\n", + " 0.899785\n", " 0.900536\n", " 0.001585\n", " ...\n", - " 0.908805\n", - " 0.885254\n", - " 0.911044\n", - " 0.007564\n", - " 0.906050\n", - " 0.887050\n", - " 0.910657\n", - " 0.003413\n", - " 0.911403\n", - " 0.887051\n", + " 0.875713\n", + " 0.898274\n", + " 0.904471\n", + " 0.001412\n", + " 0.880367\n", + " 0.901454\n", + " 0.901639\n", + " 0.000668\n", + " 0.880193\n", + " 0.903124\n", " \n", " \n", - " 35\n", + " 53\n", " 0.899950\n", " 0.001567\n", - " 0.899785\n", " 0.879122\n", + " 0.899785\n", " 0.900536\n", " 0.001585\n", - " 0.899590\n", " 0.876207\n", + " 0.899590\n", " 0.898062\n", " 0.002247\n", " ...\n", - " 0.906050\n", - " 0.887050\n", - " 0.910657\n", - " 0.003413\n", - " 0.911403\n", - " 0.887051\n", - " 0.907522\n", - " 0.008177\n", - " 0.907607\n", - " 0.887051\n", + " 0.880367\n", + " 0.901454\n", + " 0.901639\n", + " 0.000668\n", + " 0.880193\n", + " 0.903124\n", + " 0.904114\n", + " 0.002218\n", + " 0.879847\n", + " 0.900555\n", " \n", " \n", - " 36\n", + " 54\n", " 0.900536\n", " 0.001585\n", - " 0.899590\n", " 0.876207\n", + " 0.899590\n", " 0.898062\n", " 0.002247\n", - " 0.899590\n", " 0.877975\n", + " 0.899590\n", " 0.900530\n", " 0.001073\n", " ...\n", - " 0.911403\n", - " 0.887051\n", - " 0.907522\n", - " 0.008177\n", - " 0.907607\n", - " 0.887051\n", - " 0.900615\n", - " 0.003681\n", - " 0.902132\n", - " 0.887033\n", + " 0.880193\n", + " 0.903124\n", + " 0.904114\n", + " 0.002218\n", + " 0.879847\n", + " 0.900555\n", + " 0.894464\n", + " 0.006162\n", + " 0.879831\n", + " 0.896032\n", " \n", " \n", - " 37\n", + " 55\n", " 0.898062\n", " 0.002247\n", - " 0.899590\n", " 0.877975\n", + " 0.899590\n", " 0.900530\n", " 0.001073\n", - " 0.899590\n", " 0.876218\n", + " 0.899590\n", " 0.898052\n", " 0.000990\n", " ...\n", - " 0.907607\n", - " 0.887051\n", - " 0.900615\n", - " 0.003681\n", - " 0.902132\n", - " 0.887033\n", - " 0.906061\n", - " 0.001641\n", - " 0.901759\n", - " 0.881837\n", + " 0.879847\n", + " 0.900555\n", + " 0.894464\n", + " 0.006162\n", + " 0.879831\n", + " 0.896032\n", + " 0.894675\n", + " 0.001078\n", + " 0.875920\n", + " 0.896032\n", " \n", " \n", - " 38\n", + " 56\n", " 0.900530\n", " 0.001073\n", - " 0.899590\n", " 0.876218\n", + " 0.899590\n", " 0.898052\n", " 0.000990\n", - " 0.899590\n", " 0.877294\n", + " 0.899590\n", " 0.900246\n", " 0.001661\n", " ...\n", - " 0.902132\n", - " 0.887033\n", - " 0.906061\n", - " 0.001641\n", - " 0.901759\n", - " 0.881837\n", - " 0.899258\n", - " 0.016586\n", - " 0.895942\n", - " 0.881826\n", + " 0.879831\n", + " 0.896032\n", + " 0.894675\n", + " 0.001078\n", + " 0.875920\n", + " 0.896032\n", + " 0.896031\n", + " 0.000725\n", + " 0.874229\n", + " 0.896032\n", " \n", " \n", - " 39\n", + " 57\n", " 0.898052\n", " 0.000990\n", - " 0.899590\n", " 0.877294\n", + " 0.899590\n", " 0.900246\n", " 0.001661\n", - " 0.899590\n", " 0.876158\n", + " 0.899590\n", " 0.900536\n", " 0.000313\n", " ...\n", - " 0.901759\n", - " 0.881837\n", - " 0.899258\n", - " 0.016586\n", - " 0.895942\n", - " 0.881826\n", - " 0.903952\n", - " 0.012982\n", - " 0.899591\n", - " 0.883312\n", + " 0.875920\n", + " 0.896032\n", + " 0.896031\n", + " 0.000725\n", + " 0.874229\n", + " 0.896032\n", + " 0.900122\n", + " 0.002147\n", + " 0.875948\n", + " 0.896151\n", " \n", " \n", "\n", - "

10 rows × 168 columns

\n", + "

10 rows × 240 columns

\n", "" ], "text/plain": [ - " var1(t-30) var2(t-30) var3(t-30) var4(t-30) var1(t-29) var2(t-29) \\\n", - "30 0.911499 0.005383 0.909966 0.888220 0.904896 0.007199 \n", - "31 0.904896 0.007199 0.906295 0.887362 0.903185 0.003018 \n", - "32 0.903185 0.003018 0.904533 0.882607 0.900404 0.001971 \n", - "33 0.900404 0.001971 0.900683 0.881134 0.900251 0.000716 \n", - "34 0.900251 0.000716 0.900682 0.876073 0.899950 0.001567 \n", - "35 0.899950 0.001567 0.899785 0.879122 0.900536 0.001585 \n", - "36 0.900536 0.001585 0.899590 0.876207 0.898062 0.002247 \n", - "37 0.898062 0.002247 0.899590 0.877975 0.900530 0.001073 \n", - "38 0.900530 0.001073 0.899590 0.876218 0.898052 0.000990 \n", - "39 0.898052 0.000990 0.899590 0.877294 0.900246 0.001661 \n", + " var1(t-48) var2(t-48) var3(t-48) var4(t-48) var1(t-47) var2(t-47) \\\n", + "48 0.911499 0.005383 0.888220 0.909966 0.904896 0.007199 \n", + "49 0.904896 0.007199 0.887362 0.906295 0.903185 0.003018 \n", + "50 0.903185 0.003018 0.882607 0.904533 0.900404 0.001971 \n", + "51 0.900404 0.001971 0.881134 0.900683 0.900251 0.000716 \n", + "52 0.900251 0.000716 0.876073 0.900682 0.899950 0.001567 \n", + "53 0.899950 0.001567 0.879122 0.899785 0.900536 0.001585 \n", + "54 0.900536 0.001585 0.876207 0.899590 0.898062 0.002247 \n", + "55 0.898062 0.002247 0.877975 0.899590 0.900530 0.001073 \n", + "56 0.900530 0.001073 0.876218 0.899590 0.898052 0.000990 \n", + "57 0.898052 0.000990 0.877294 0.899590 0.900246 0.001661 \n", "\n", - " var3(t-29) var4(t-29) var1(t-28) var2(t-28) ... var3(t+9) \\\n", - "30 0.906295 0.887362 0.903185 0.003018 ... 0.902730 \n", - "31 0.904533 0.882607 0.900404 0.001971 ... 0.902156 \n", - "32 0.900683 0.881134 0.900251 0.000716 ... 0.902319 \n", - "33 0.900682 0.876073 0.899950 0.001567 ... 0.905317 \n", - "34 0.899785 0.879122 0.900536 0.001585 ... 0.908805 \n", - "35 0.899590 0.876207 0.898062 0.002247 ... 0.906050 \n", - "36 0.899590 0.877975 0.900530 0.001073 ... 0.911403 \n", - "37 0.899590 0.876218 0.898052 0.000990 ... 0.907607 \n", - "38 0.899590 0.877294 0.900246 0.001661 ... 0.902132 \n", - "39 0.899590 0.876158 0.900536 0.000313 ... 0.901759 \n", + " var3(t-47) var4(t-47) var1(t-46) var2(t-46) ... var3(t+9) \\\n", + "48 0.887362 0.906295 0.903185 0.003018 ... 0.878685 \n", + "49 0.882607 0.904533 0.900404 0.001971 ... 0.875276 \n", + "50 0.881134 0.900683 0.900251 0.000716 ... 0.878343 \n", + "51 0.876073 0.900682 0.899950 0.001567 ... 0.877999 \n", + "52 0.879122 0.899785 0.900536 0.001585 ... 0.875713 \n", + "53 0.876207 0.899590 0.898062 0.002247 ... 0.880367 \n", + "54 0.877975 0.899590 0.900530 0.001073 ... 0.880193 \n", + "55 0.876218 0.899590 0.898052 0.000990 ... 0.879847 \n", + "56 0.877294 0.899590 0.900246 0.001661 ... 0.879831 \n", + "57 0.876158 0.899590 0.900536 0.000313 ... 0.875920 \n", "\n", " var4(t+9) var1(t+10) var2(t+10) var3(t+10) var4(t+10) var1(t+11) \\\n", - "30 0.882924 0.903218 0.000487 0.902156 0.882912 0.903810 \n", - "31 0.882912 0.903810 0.001179 0.902319 0.882907 0.909454 \n", - "32 0.882907 0.909454 0.001097 0.905317 0.885319 0.907357 \n", - "33 0.885319 0.907357 0.000555 0.908805 0.885254 0.911044 \n", - "34 0.885254 0.911044 0.007564 0.906050 0.887050 0.910657 \n", - "35 0.887050 0.910657 0.003413 0.911403 0.887051 0.907522 \n", - "36 0.887051 0.907522 0.008177 0.907607 0.887051 0.900615 \n", - "37 0.887051 0.900615 0.003681 0.902132 0.887033 0.906061 \n", - "38 0.887033 0.906061 0.001641 0.901759 0.881837 0.899258 \n", - "39 0.881837 0.899258 0.016586 0.895942 0.881826 0.903952 \n", + "48 0.897946 0.899620 0.003343 0.875276 0.896600 0.902619 \n", + "49 0.896600 0.902619 0.000657 0.878343 0.901145 0.898043 \n", + "50 0.901145 0.898043 0.001283 0.877999 0.896614 0.899926 \n", + "51 0.896614 0.899926 0.001236 0.875713 0.898274 0.904471 \n", + "52 0.898274 0.904471 0.001412 0.880367 0.901454 0.901639 \n", + "53 0.901454 0.901639 0.000668 0.880193 0.903124 0.904114 \n", + "54 0.903124 0.904114 0.002218 0.879847 0.900555 0.894464 \n", + "55 0.900555 0.894464 0.006162 0.879831 0.896032 0.894675 \n", + "56 0.896032 0.894675 0.001078 0.875920 0.896032 0.896031 \n", + "57 0.896032 0.896031 0.000725 0.874229 0.896032 0.900122 \n", "\n", " var2(t+11) var3(t+11) var4(t+11) \n", - "30 0.001179 0.902319 0.882907 \n", - "31 0.001097 0.905317 0.885319 \n", - "32 0.000555 0.908805 0.885254 \n", - "33 0.007564 0.906050 0.887050 \n", - "34 0.003413 0.911403 0.887051 \n", - "35 0.008177 0.907607 0.887051 \n", - "36 0.003681 0.902132 0.887033 \n", - "37 0.001641 0.901759 0.881837 \n", - "38 0.016586 0.895942 0.881826 \n", - "39 0.012982 0.899591 0.883312 \n", + "48 0.000657 0.878343 0.901145 \n", + "49 0.001283 0.877999 0.896614 \n", + "50 0.001236 0.875713 0.898274 \n", + "51 0.001412 0.880367 0.901454 \n", + "52 0.000668 0.880193 0.903124 \n", + "53 0.002218 0.879847 0.900555 \n", + "54 0.006162 0.879831 0.896032 \n", + "55 0.001078 0.875920 0.896032 \n", + "56 0.000725 0.874229 0.896032 \n", + "57 0.002147 0.875948 0.896151 \n", "\n", - "[10 rows x 168 columns]" + "[10 rows x 240 columns]" ] }, - "execution_count": 6, + "execution_count": 7, "metadata": {}, "output_type": "execute_result" } @@ -556,8 +593,27 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 8, "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "29523\n" + ] + } + ], + "source": [ + "print(reframed.shape[0])" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": { + "scrolled": false + }, "outputs": [ { "data": { @@ -580,188 +636,188 @@ " \n", " \n", " \n", - " var1(t-30)\n", - " var2(t-30)\n", - " var3(t-30)\n", - " var4(t-30)\n", - " var1(t-29)\n", - " var2(t-29)\n", - " var3(t-29)\n", - " var4(t-29)\n", - " var1(t-28)\n", - " var2(t-28)\n", + " var1(t-48)\n", + " var2(t-48)\n", + " var3(t-48)\n", + " var4(t-48)\n", + " var1(t-47)\n", + " var2(t-47)\n", + " var3(t-47)\n", + " var4(t-47)\n", + " var1(t-46)\n", + " var2(t-46)\n", " ...\n", - " var1(t+2)\n", - " var1(t+3)\n", - " var1(t+4)\n", - " var1(t+5)\n", - " var1(t+6)\n", - " var1(t+7)\n", - " var1(t+8)\n", - " var1(t+9)\n", + " var3(t+9)\n", + " var4(t+9)\n", " var1(t+10)\n", + " var2(t+10)\n", + " var3(t+10)\n", + " var4(t+10)\n", " var1(t+11)\n", + " var2(t+11)\n", + " var3(t+11)\n", + " var4(t+11)\n", " \n", " \n", " \n", " \n", - " 30\n", + " 48\n", " 0.911499\n", " 0.005383\n", - " 0.909966\n", " 0.888220\n", + " 0.909966\n", " 0.904896\n", " 0.007199\n", - " 0.906295\n", " 0.887362\n", + " 0.906295\n", " 0.903185\n", " 0.003018\n", " ...\n", - " 0.908729\n", - " 0.903118\n", - " 0.904913\n", - " 0.909531\n", - " 0.907403\n", - " 0.907393\n", - " 0.907137\n", - " 0.903187\n", - " 0.903218\n", - " 0.903810\n", + " 0.878685\n", + " 0.897946\n", + " 0.899620\n", + " 0.003343\n", + " 0.875276\n", + " 0.896600\n", + " 0.902619\n", + " 0.000657\n", + " 0.878343\n", + " 0.901145\n", " \n", " \n", - " 31\n", + " 49\n", " 0.904896\n", " 0.007199\n", - " 0.906295\n", " 0.887362\n", + " 0.906295\n", " 0.903185\n", " 0.003018\n", - " 0.904533\n", " 0.882607\n", + " 0.904533\n", " 0.900404\n", " 0.001971\n", " ...\n", - " 0.903118\n", - " 0.904913\n", - " 0.909531\n", - " 0.907403\n", - " 0.907393\n", - " 0.907137\n", - " 0.903187\n", - " 0.903218\n", - " 0.903810\n", - " 0.909454\n", + " 0.875276\n", + " 0.896600\n", + " 0.902619\n", + " 0.000657\n", + " 0.878343\n", + " 0.901145\n", + " 0.898043\n", + " 0.001283\n", + " 0.877999\n", + " 0.896614\n", " \n", " \n", - " 32\n", + " 50\n", " 0.903185\n", " 0.003018\n", - " 0.904533\n", " 0.882607\n", + " 0.904533\n", " 0.900404\n", " 0.001971\n", - " 0.900683\n", " 0.881134\n", + " 0.900683\n", " 0.900251\n", " 0.000716\n", " ...\n", - " 0.904913\n", - " 0.909531\n", - " 0.907403\n", - " 0.907393\n", - " 0.907137\n", - " 0.903187\n", - " 0.903218\n", - " 0.903810\n", - " 0.909454\n", - " 0.907357\n", + " 0.878343\n", + " 0.901145\n", + " 0.898043\n", + " 0.001283\n", + " 0.877999\n", + " 0.896614\n", + " 0.899926\n", + " 0.001236\n", + " 0.875713\n", + " 0.898274\n", " \n", " \n", - " 33\n", + " 51\n", " 0.900404\n", " 0.001971\n", - " 0.900683\n", " 0.881134\n", + " 0.900683\n", " 0.900251\n", " 0.000716\n", - " 0.900682\n", " 0.876073\n", + " 0.900682\n", " 0.899950\n", " 0.001567\n", " ...\n", - " 0.909531\n", - " 0.907403\n", - " 0.907393\n", - " 0.907137\n", - " 0.903187\n", - " 0.903218\n", - " 0.903810\n", - " 0.909454\n", - " 0.907357\n", - " 0.911044\n", + " 0.877999\n", + " 0.896614\n", + " 0.899926\n", + " 0.001236\n", + " 0.875713\n", + " 0.898274\n", + " 0.904471\n", + " 0.001412\n", + " 0.880367\n", + " 0.901454\n", " \n", " \n", - " 34\n", + " 52\n", " 0.900251\n", " 0.000716\n", - " 0.900682\n", " 0.876073\n", + " 0.900682\n", " 0.899950\n", " 0.001567\n", - " 0.899785\n", " 0.879122\n", + " 0.899785\n", " 0.900536\n", " 0.001585\n", " ...\n", - " 0.907403\n", - " 0.907393\n", - " 0.907137\n", - " 0.903187\n", - " 0.903218\n", - " 0.903810\n", - " 0.909454\n", - " 0.907357\n", - " 0.911044\n", - " 0.910657\n", + " 0.875713\n", + " 0.898274\n", + " 0.904471\n", + " 0.001412\n", + " 0.880367\n", + " 0.901454\n", + " 0.901639\n", + " 0.000668\n", + " 0.880193\n", + " 0.903124\n", " \n", " \n", "\n", - "

5 rows × 132 columns

\n", + "

5 rows × 240 columns

\n", "" ], "text/plain": [ - " var1(t-30) var2(t-30) var3(t-30) var4(t-30) var1(t-29) var2(t-29) \\\n", - "30 0.911499 0.005383 0.909966 0.888220 0.904896 0.007199 \n", - "31 0.904896 0.007199 0.906295 0.887362 0.903185 0.003018 \n", - "32 0.903185 0.003018 0.904533 0.882607 0.900404 0.001971 \n", - "33 0.900404 0.001971 0.900683 0.881134 0.900251 0.000716 \n", - "34 0.900251 0.000716 0.900682 0.876073 0.899950 0.001567 \n", + " var1(t-48) var2(t-48) var3(t-48) var4(t-48) var1(t-47) var2(t-47) \\\n", + "48 0.911499 0.005383 0.888220 0.909966 0.904896 0.007199 \n", + "49 0.904896 0.007199 0.887362 0.906295 0.903185 0.003018 \n", + "50 0.903185 0.003018 0.882607 0.904533 0.900404 0.001971 \n", + "51 0.900404 0.001971 0.881134 0.900683 0.900251 0.000716 \n", + "52 0.900251 0.000716 0.876073 0.900682 0.899950 0.001567 \n", "\n", - " var3(t-29) var4(t-29) var1(t-28) var2(t-28) ... var1(t+2) \\\n", - "30 0.906295 0.887362 0.903185 0.003018 ... 0.908729 \n", - "31 0.904533 0.882607 0.900404 0.001971 ... 0.903118 \n", - "32 0.900683 0.881134 0.900251 0.000716 ... 0.904913 \n", - "33 0.900682 0.876073 0.899950 0.001567 ... 0.909531 \n", - "34 0.899785 0.879122 0.900536 0.001585 ... 0.907403 \n", + " var3(t-47) var4(t-47) var1(t-46) var2(t-46) ... var3(t+9) \\\n", + "48 0.887362 0.906295 0.903185 0.003018 ... 0.878685 \n", + "49 0.882607 0.904533 0.900404 0.001971 ... 0.875276 \n", + "50 0.881134 0.900683 0.900251 0.000716 ... 0.878343 \n", + "51 0.876073 0.900682 0.899950 0.001567 ... 0.877999 \n", + "52 0.879122 0.899785 0.900536 0.001585 ... 0.875713 \n", "\n", - " var1(t+3) var1(t+4) var1(t+5) var1(t+6) var1(t+7) var1(t+8) \\\n", - "30 0.903118 0.904913 0.909531 0.907403 0.907393 0.907137 \n", - "31 0.904913 0.909531 0.907403 0.907393 0.907137 0.903187 \n", - "32 0.909531 0.907403 0.907393 0.907137 0.903187 0.903218 \n", - "33 0.907403 0.907393 0.907137 0.903187 0.903218 0.903810 \n", - "34 0.907393 0.907137 0.903187 0.903218 0.903810 0.909454 \n", + " var4(t+9) var1(t+10) var2(t+10) var3(t+10) var4(t+10) var1(t+11) \\\n", + "48 0.897946 0.899620 0.003343 0.875276 0.896600 0.902619 \n", + "49 0.896600 0.902619 0.000657 0.878343 0.901145 0.898043 \n", + "50 0.901145 0.898043 0.001283 0.877999 0.896614 0.899926 \n", + "51 0.896614 0.899926 0.001236 0.875713 0.898274 0.904471 \n", + "52 0.898274 0.904471 0.001412 0.880367 0.901454 0.901639 \n", "\n", - " var1(t+9) var1(t+10) var1(t+11) \n", - "30 0.903187 0.903218 0.903810 \n", - "31 0.903218 0.903810 0.909454 \n", - "32 0.903810 0.909454 0.907357 \n", - "33 0.909454 0.907357 0.911044 \n", - "34 0.907357 0.911044 0.910657 \n", + " var2(t+11) var3(t+11) var4(t+11) \n", + "48 0.000657 0.878343 0.901145 \n", + "49 0.001283 0.877999 0.896614 \n", + "50 0.001236 0.875713 0.898274 \n", + "51 0.001412 0.880367 0.901454 \n", + "52 0.000668 0.880193 0.903124 \n", "\n", - "[5 rows x 132 columns]" + "[5 rows x 240 columns]" ] }, - "execution_count": 7, + "execution_count": 9, "metadata": {}, "output_type": "execute_result" } @@ -770,7 +826,7 @@ "# drop columns we don't want to predict\n", "# We're only concerned with the estimating the close value,\n", "# Close should be first in the list of column in the config file\n", - "\n", + "\"\"\"\n", "cols_to_drop = []\n", "\n", "for i in range (n_out):\n", @@ -779,12 +835,31 @@ "\n", "reframed.drop(reframed.columns[cols_to_drop], axis=1, inplace=True)\n", "\n", + "pd.set_option('display.max_columns', 500)\"\"\"\n", "reframed.head()" ] }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "29523\n" + ] + } + ], + "source": [ + "#ndarray.shape holds array dimensions in tuple form\n", + "print(reframed.values.shape[0])" + ] + }, + { + "cell_type": "code", + "execution_count": 11, "metadata": {}, "outputs": [], "source": [ @@ -797,42 +872,43 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 12, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "((23632, 30, 4), (23632, 12, 1), (5909, 30, 4), (5909, 12, 1))\n" + "(23618, 48, 4) (23618, 12, 4) (5905, 48, 4) (5905, 12, 4)\n" ] } ], "source": [ "# split into input and outputs\n", + "# n_obs is the length of input columns in our dataframe\n", "n_obs = n_lag * n_features\n", "\n", "# We're only concerned with the estimating the close value,\n", "# Close should be first in the list of column in the config file\n", "\n", "n_outputs = n_out * n_features\n", - "train_x, train_y = train[:, :n_obs], train[:, -n_out:]\n", - "test_x, test_y = test[:, :n_obs], test[:, -n_out:]\n", + "train_x, train_y = train[:, :n_obs], train[:, -n_outputs:]\n", + "test_x, test_y = test[:, :n_obs], test[:, -n_outputs:]\n", "\n", "# reshape input to be 3D [samples, timesteps, features]\n", "train_x = train_x.reshape((train_x.shape[0], n_lag, n_features))\n", "test_x = test_x.reshape((test_x.shape[0], n_lag, n_features))\n", "\n", "# reshape output to be 3D [samples, timesteps, features]\n", - "train_y = train_y.reshape(-1, n_out, 1)\n", - "test_y = test_y.reshape(-1, n_out, 1)\n", + "train_y = train_y.reshape(-1, n_out, n_features)\n", + "test_y = test_y.reshape(-1, n_out, n_features)\n", "\n", "print(train_x.shape, train_y.shape, test_x.shape, test_y.shape)" ] }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 13, "metadata": {}, "outputs": [], "source": [ @@ -842,15 +918,23 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 14, + "metadata": {}, + "outputs": [], + "source": [ + "epochs=10\n", + "batch_size=3250" + ] + }, + { + "cell_type": "code", + "execution_count": 15, "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", "Using TensorFlow backend.\n" ] }, @@ -858,31 +942,14 @@ "name": "stdout", "output_type": "stream", "text": [ - "_________________________________________________________________\n", - "Layer (type) Output Shape Param # \n", - "=================================================================\n", - "bidirectional_1 (Bidirection (None, 100) 22000 \n", - "_________________________________________________________________\n", - "dropout_1 (Dropout) (None, 100) 0 \n", - "_________________________________________________________________\n", - "repeat_vector_1 (RepeatVecto (None, 12, 100) 0 \n", - "_________________________________________________________________\n", - "lstm_2 (LSTM) (None, 12, 25) 12600 \n", - "_________________________________________________________________\n", - "dropout_2 (Dropout) (None, 12, 25) 0 \n", - "_________________________________________________________________\n", - "time_distributed_1 (TimeDist (None, 12, 1) 26 \n", - "=================================================================\n", - "Total params: 34,626\n", - "Trainable params: 34,626\n", - "Non-trainable params: 0\n", - "_________________________________________________________________\n" + "Tensor(\"concatenate_35/concat:0\", shape=(?, 60, 4), dtype=float32)\n", + "Tensor(\"concatenate_34/concat:0\", shape=(?, 12, 4), dtype=float32)\n" ] } ], "source": [ "from keras.models import Sequential\n", - "from keras.layers import Dense\n", + "from keras.layers import Dense, concatenate\n", "from keras.layers import Input\n", "from keras.layers import LSTM, CuDNNLSTM, GRU,CuDNNGRU\n", "from keras.layers import Conv1D, AveragePooling1D, MaxPooling1D\n", @@ -892,25 +959,396 @@ "from keras.layers import Bidirectional\n", "from keras.layers import RepeatVector\n", "from keras.callbacks import ModelCheckpoint\n", + "import keras\n", "\n", "units= CONFIG['lstm_hidden_size']\n", "dropout = .1\n", "\n", "# design network\n", - "model = Sequential()\n", - "model.add(Bidirectional(LSTM(units), input_shape=(train_x.shape[1], train_x.shape[2])))\n", - "model.add(Dropout(dropout))\n", + "inputs = Input(shape=(48, 4))\n", + "firstconv = Conv1D(30, 3)(inputs)\n", + "firstconv = LSTM(30)(firstconv)\n", + "firstconv = Dropout(dropout)(firstconv)\n", + "firstrnn = LSTM(30)(inputs)\n", + "firstrnn = Dropout(dropout)(firstrnn)\n", + "firstcat = concatenate([firstconv, firstrnn])\n", + "results = Dense(60)(firstcat)\n", + "results = Dropout(dropout)(results)\n", + "results = Dense(4)(results)\n", + "results = keras.layers.Reshape((1, 4))(results)\n", + "\n", + "innerputs = concatenate([inputs, results], axis=1)\n", + "for i in range(1, 12):\n", + " \n", + " innerconv = Conv1D(30, 3)(innerputs)\n", + " innerconv = LSTM(30)(innerconv)\n", + " innerconv = Dropout(dropout)(innerconv)\n", + " innerrnn = LSTM(30, input_shape=(48 + i, 4))(innerputs)\n", + " innerrnn = Dropout(dropout)(innerrnn)\n", + " innercat = concatenate([innerconv, innerrnn])\n", + " innerres = Dense(60)(innercat)\n", + " innerres = Dropout(dropout)(innerres)\n", + " innerres = Dense(4)(innerres)\n", + " innerres = keras.layers.Reshape((1, 4))(innerres)\n", + " results = concatenate([results, innerres], axis = 1)\n", + " innerputs = concatenate([innerputs, innerres], axis=1)\n", + "\n", + "print(innerputs, results, sep=\"\\n\")" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "__________________________________________________________________________________________________\n", + "Layer (type) Output Shape Param # Connected to \n", + "==================================================================================================\n", + "input_1 (InputLayer) (None, 48, 4) 0 \n", + "__________________________________________________________________________________________________\n", + "conv1d_1 (Conv1D) (None, 46, 30) 390 input_1[0][0] \n", + "__________________________________________________________________________________________________\n", + "lstm_1 (LSTM) (None, 30) 7320 conv1d_1[0][0] \n", + "__________________________________________________________________________________________________\n", + "lstm_2 (LSTM) (None, 30) 4200 input_1[0][0] \n", + "__________________________________________________________________________________________________\n", + "dropout_1 (Dropout) (None, 30) 0 lstm_1[0][0] \n", + "__________________________________________________________________________________________________\n", + "dropout_2 (Dropout) (None, 30) 0 lstm_2[0][0] \n", + "__________________________________________________________________________________________________\n", + "concatenate_1 (Concatenate) (None, 60) 0 dropout_1[0][0] \n", + " dropout_2[0][0] \n", + "__________________________________________________________________________________________________\n", + "dense_1 (Dense) (None, 60) 3660 concatenate_1[0][0] \n", + "__________________________________________________________________________________________________\n", + "dropout_3 (Dropout) (None, 60) 0 dense_1[0][0] \n", + "__________________________________________________________________________________________________\n", + "dense_2 (Dense) (None, 4) 244 dropout_3[0][0] \n", + "__________________________________________________________________________________________________\n", + "reshape_1 (Reshape) (None, 1, 4) 0 dense_2[0][0] \n", + "__________________________________________________________________________________________________\n", + "concatenate_2 (Concatenate) (None, 49, 4) 0 input_1[0][0] \n", + " reshape_1[0][0] \n", + "__________________________________________________________________________________________________\n", + "conv1d_2 (Conv1D) (None, 47, 30) 390 concatenate_2[0][0] \n", + "__________________________________________________________________________________________________\n", + "lstm_3 (LSTM) (None, 30) 7320 conv1d_2[0][0] \n", + "__________________________________________________________________________________________________\n", + "lstm_4 (LSTM) (None, 30) 4200 concatenate_2[0][0] \n", + "__________________________________________________________________________________________________\n", + "dropout_4 (Dropout) (None, 30) 0 lstm_3[0][0] \n", + "__________________________________________________________________________________________________\n", + "dropout_5 (Dropout) (None, 30) 0 lstm_4[0][0] \n", + "__________________________________________________________________________________________________\n", + "concatenate_3 (Concatenate) (None, 60) 0 dropout_4[0][0] \n", + " dropout_5[0][0] \n", + "__________________________________________________________________________________________________\n", + "dense_3 (Dense) (None, 60) 3660 concatenate_3[0][0] \n", + "__________________________________________________________________________________________________\n", + "dropout_6 (Dropout) (None, 60) 0 dense_3[0][0] \n", + "__________________________________________________________________________________________________\n", + "dense_4 (Dense) (None, 4) 244 dropout_6[0][0] \n", + "__________________________________________________________________________________________________\n", + "reshape_2 (Reshape) (None, 1, 4) 0 dense_4[0][0] \n", + "__________________________________________________________________________________________________\n", + "concatenate_5 (Concatenate) (None, 50, 4) 0 concatenate_2[0][0] \n", + " reshape_2[0][0] \n", + "__________________________________________________________________________________________________\n", + "conv1d_3 (Conv1D) (None, 48, 30) 390 concatenate_5[0][0] \n", + "__________________________________________________________________________________________________\n", + "lstm_5 (LSTM) (None, 30) 7320 conv1d_3[0][0] \n", + "__________________________________________________________________________________________________\n", + "lstm_6 (LSTM) (None, 30) 4200 concatenate_5[0][0] \n", + "__________________________________________________________________________________________________\n", + "dropout_7 (Dropout) (None, 30) 0 lstm_5[0][0] \n", + "__________________________________________________________________________________________________\n", + "dropout_8 (Dropout) (None, 30) 0 lstm_6[0][0] \n", + "__________________________________________________________________________________________________\n", + "concatenate_6 (Concatenate) (None, 60) 0 dropout_7[0][0] \n", + " dropout_8[0][0] \n", + "__________________________________________________________________________________________________\n", + "dense_5 (Dense) (None, 60) 3660 concatenate_6[0][0] \n", + "__________________________________________________________________________________________________\n", + "dropout_9 (Dropout) (None, 60) 0 dense_5[0][0] \n", + "__________________________________________________________________________________________________\n", + "dense_6 (Dense) (None, 4) 244 dropout_9[0][0] \n", + "__________________________________________________________________________________________________\n", + "reshape_3 (Reshape) (None, 1, 4) 0 dense_6[0][0] \n", + "__________________________________________________________________________________________________\n", + "concatenate_8 (Concatenate) (None, 51, 4) 0 concatenate_5[0][0] \n", + " reshape_3[0][0] \n", + "__________________________________________________________________________________________________\n", + "conv1d_4 (Conv1D) (None, 49, 30) 390 concatenate_8[0][0] \n", + "__________________________________________________________________________________________________\n", + "lstm_7 (LSTM) (None, 30) 7320 conv1d_4[0][0] \n", + "__________________________________________________________________________________________________\n", + "lstm_8 (LSTM) (None, 30) 4200 concatenate_8[0][0] \n", + "__________________________________________________________________________________________________\n", + "dropout_10 (Dropout) (None, 30) 0 lstm_7[0][0] \n", + "__________________________________________________________________________________________________\n", + "dropout_11 (Dropout) (None, 30) 0 lstm_8[0][0] \n", + "__________________________________________________________________________________________________\n", + "concatenate_9 (Concatenate) (None, 60) 0 dropout_10[0][0] \n", + " dropout_11[0][0] \n", + "__________________________________________________________________________________________________\n", + "dense_7 (Dense) (None, 60) 3660 concatenate_9[0][0] \n", + "__________________________________________________________________________________________________\n", + "dropout_12 (Dropout) (None, 60) 0 dense_7[0][0] \n", + "__________________________________________________________________________________________________\n", + "dense_8 (Dense) (None, 4) 244 dropout_12[0][0] \n", + "__________________________________________________________________________________________________\n", + "reshape_4 (Reshape) (None, 1, 4) 0 dense_8[0][0] \n", + "__________________________________________________________________________________________________\n", + "concatenate_11 (Concatenate) (None, 52, 4) 0 concatenate_8[0][0] \n", + " reshape_4[0][0] \n", + "__________________________________________________________________________________________________\n", + "conv1d_5 (Conv1D) (None, 50, 30) 390 concatenate_11[0][0] \n", + "__________________________________________________________________________________________________\n", + "lstm_9 (LSTM) (None, 30) 7320 conv1d_5[0][0] \n", + "__________________________________________________________________________________________________\n", + "lstm_10 (LSTM) (None, 30) 4200 concatenate_11[0][0] \n", + "__________________________________________________________________________________________________\n", + "dropout_13 (Dropout) (None, 30) 0 lstm_9[0][0] \n", + "__________________________________________________________________________________________________\n", + "dropout_14 (Dropout) (None, 30) 0 lstm_10[0][0] \n", + "__________________________________________________________________________________________________\n", + "concatenate_12 (Concatenate) (None, 60) 0 dropout_13[0][0] \n", + " dropout_14[0][0] \n", + "__________________________________________________________________________________________________\n", + "dense_9 (Dense) (None, 60) 3660 concatenate_12[0][0] \n", + "__________________________________________________________________________________________________\n", + "dropout_15 (Dropout) (None, 60) 0 dense_9[0][0] \n", + "__________________________________________________________________________________________________\n", + "dense_10 (Dense) (None, 4) 244 dropout_15[0][0] \n", + "__________________________________________________________________________________________________\n", + "reshape_5 (Reshape) (None, 1, 4) 0 dense_10[0][0] \n", + "__________________________________________________________________________________________________\n", + "concatenate_14 (Concatenate) (None, 53, 4) 0 concatenate_11[0][0] \n", + " reshape_5[0][0] \n", + "__________________________________________________________________________________________________\n", + "conv1d_6 (Conv1D) (None, 51, 30) 390 concatenate_14[0][0] \n", + "__________________________________________________________________________________________________\n", + "lstm_11 (LSTM) (None, 30) 7320 conv1d_6[0][0] \n", + "__________________________________________________________________________________________________\n", + "lstm_12 (LSTM) (None, 30) 4200 concatenate_14[0][0] \n", + "__________________________________________________________________________________________________\n", + "dropout_16 (Dropout) (None, 30) 0 lstm_11[0][0] \n", + "__________________________________________________________________________________________________\n", + "dropout_17 (Dropout) (None, 30) 0 lstm_12[0][0] \n", + "__________________________________________________________________________________________________\n", + "concatenate_15 (Concatenate) (None, 60) 0 dropout_16[0][0] \n", + " dropout_17[0][0] \n", + "__________________________________________________________________________________________________\n", + "dense_11 (Dense) (None, 60) 3660 concatenate_15[0][0] \n", + "__________________________________________________________________________________________________\n", + "dropout_18 (Dropout) (None, 60) 0 dense_11[0][0] \n", + "__________________________________________________________________________________________________\n", + "dense_12 (Dense) (None, 4) 244 dropout_18[0][0] \n", + "__________________________________________________________________________________________________\n", + "reshape_6 (Reshape) (None, 1, 4) 0 dense_12[0][0] \n", + "__________________________________________________________________________________________________\n", + "concatenate_17 (Concatenate) (None, 54, 4) 0 concatenate_14[0][0] \n", + " reshape_6[0][0] \n", + "__________________________________________________________________________________________________\n", + "conv1d_7 (Conv1D) (None, 52, 30) 390 concatenate_17[0][0] \n", + "__________________________________________________________________________________________________\n", + "lstm_13 (LSTM) (None, 30) 7320 conv1d_7[0][0] \n", + "__________________________________________________________________________________________________\n", + "lstm_14 (LSTM) (None, 30) 4200 concatenate_17[0][0] \n", + "__________________________________________________________________________________________________\n", + "dropout_19 (Dropout) (None, 30) 0 lstm_13[0][0] \n", + "__________________________________________________________________________________________________\n", + "dropout_20 (Dropout) (None, 30) 0 lstm_14[0][0] \n", + "__________________________________________________________________________________________________\n", + "concatenate_18 (Concatenate) (None, 60) 0 dropout_19[0][0] \n", + " dropout_20[0][0] \n", + "__________________________________________________________________________________________________\n", + "dense_13 (Dense) (None, 60) 3660 concatenate_18[0][0] \n", + "__________________________________________________________________________________________________\n", + "dropout_21 (Dropout) (None, 60) 0 dense_13[0][0] \n", + "__________________________________________________________________________________________________\n", + "dense_14 (Dense) (None, 4) 244 dropout_21[0][0] \n", + "__________________________________________________________________________________________________\n", + "reshape_7 (Reshape) (None, 1, 4) 0 dense_14[0][0] \n", + "__________________________________________________________________________________________________\n", + "concatenate_20 (Concatenate) (None, 55, 4) 0 concatenate_17[0][0] \n", + " reshape_7[0][0] \n", + "__________________________________________________________________________________________________\n", + "conv1d_8 (Conv1D) (None, 53, 30) 390 concatenate_20[0][0] \n", + "__________________________________________________________________________________________________\n", + "lstm_15 (LSTM) (None, 30) 7320 conv1d_8[0][0] \n", + "__________________________________________________________________________________________________\n", + "lstm_16 (LSTM) (None, 30) 4200 concatenate_20[0][0] \n", + "__________________________________________________________________________________________________\n", + "dropout_22 (Dropout) (None, 30) 0 lstm_15[0][0] \n", + "__________________________________________________________________________________________________\n", + "dropout_23 (Dropout) (None, 30) 0 lstm_16[0][0] \n", + "__________________________________________________________________________________________________\n", + "concatenate_21 (Concatenate) (None, 60) 0 dropout_22[0][0] \n", + " dropout_23[0][0] \n", + "__________________________________________________________________________________________________\n", + "dense_15 (Dense) (None, 60) 3660 concatenate_21[0][0] \n", + "__________________________________________________________________________________________________\n", + "dropout_24 (Dropout) (None, 60) 0 dense_15[0][0] \n", + "__________________________________________________________________________________________________\n", + "dense_16 (Dense) (None, 4) 244 dropout_24[0][0] \n", + "__________________________________________________________________________________________________\n", + "reshape_8 (Reshape) (None, 1, 4) 0 dense_16[0][0] \n", + "__________________________________________________________________________________________________\n", + "concatenate_23 (Concatenate) (None, 56, 4) 0 concatenate_20[0][0] \n", + " reshape_8[0][0] \n", + "__________________________________________________________________________________________________\n", + "conv1d_9 (Conv1D) (None, 54, 30) 390 concatenate_23[0][0] \n", + "__________________________________________________________________________________________________\n", + "lstm_17 (LSTM) (None, 30) 7320 conv1d_9[0][0] \n", + "__________________________________________________________________________________________________\n", + "lstm_18 (LSTM) (None, 30) 4200 concatenate_23[0][0] \n", + "__________________________________________________________________________________________________\n", + "dropout_25 (Dropout) (None, 30) 0 lstm_17[0][0] \n", + "__________________________________________________________________________________________________\n", + "dropout_26 (Dropout) (None, 30) 0 lstm_18[0][0] \n", + "__________________________________________________________________________________________________\n", + "concatenate_24 (Concatenate) (None, 60) 0 dropout_25[0][0] \n", + " dropout_26[0][0] \n", + "__________________________________________________________________________________________________\n", + "dense_17 (Dense) (None, 60) 3660 concatenate_24[0][0] \n", + "__________________________________________________________________________________________________\n", + "dropout_27 (Dropout) (None, 60) 0 dense_17[0][0] \n", + "__________________________________________________________________________________________________\n", + "dense_18 (Dense) (None, 4) 244 dropout_27[0][0] \n", + "__________________________________________________________________________________________________\n", + "reshape_9 (Reshape) (None, 1, 4) 0 dense_18[0][0] \n", + "__________________________________________________________________________________________________\n", + "concatenate_26 (Concatenate) (None, 57, 4) 0 concatenate_23[0][0] \n", + " reshape_9[0][0] \n", + "__________________________________________________________________________________________________\n", + "conv1d_10 (Conv1D) (None, 55, 30) 390 concatenate_26[0][0] \n", + "__________________________________________________________________________________________________\n", + "lstm_19 (LSTM) (None, 30) 7320 conv1d_10[0][0] \n", + "__________________________________________________________________________________________________\n", + "lstm_20 (LSTM) (None, 30) 4200 concatenate_26[0][0] \n", + "__________________________________________________________________________________________________\n", + "dropout_28 (Dropout) (None, 30) 0 lstm_19[0][0] \n", + "__________________________________________________________________________________________________\n", + "dropout_29 (Dropout) (None, 30) 0 lstm_20[0][0] \n", + "__________________________________________________________________________________________________\n", + "concatenate_27 (Concatenate) (None, 60) 0 dropout_28[0][0] \n", + " dropout_29[0][0] \n", + "__________________________________________________________________________________________________\n", + "dense_19 (Dense) (None, 60) 3660 concatenate_27[0][0] \n", + "__________________________________________________________________________________________________\n", + "dropout_30 (Dropout) (None, 60) 0 dense_19[0][0] \n", + "__________________________________________________________________________________________________\n", + "dense_20 (Dense) (None, 4) 244 dropout_30[0][0] \n", + "__________________________________________________________________________________________________\n", + "reshape_10 (Reshape) (None, 1, 4) 0 dense_20[0][0] \n", + "__________________________________________________________________________________________________\n", + "concatenate_29 (Concatenate) (None, 58, 4) 0 concatenate_26[0][0] \n", + " reshape_10[0][0] \n", + "__________________________________________________________________________________________________\n", + "conv1d_11 (Conv1D) (None, 56, 30) 390 concatenate_29[0][0] \n", + "__________________________________________________________________________________________________\n", + "lstm_21 (LSTM) (None, 30) 7320 conv1d_11[0][0] \n", + "__________________________________________________________________________________________________\n", + "lstm_22 (LSTM) (None, 30) 4200 concatenate_29[0][0] \n", + "__________________________________________________________________________________________________\n", + "dropout_31 (Dropout) (None, 30) 0 lstm_21[0][0] \n", + "__________________________________________________________________________________________________\n", + "dropout_32 (Dropout) (None, 30) 0 lstm_22[0][0] \n", + "__________________________________________________________________________________________________\n", + "concatenate_30 (Concatenate) (None, 60) 0 dropout_31[0][0] \n", + " dropout_32[0][0] \n", + "__________________________________________________________________________________________________\n", + "dense_21 (Dense) (None, 60) 3660 concatenate_30[0][0] \n", + "__________________________________________________________________________________________________\n", + "dropout_33 (Dropout) (None, 60) 0 dense_21[0][0] \n", + "__________________________________________________________________________________________________\n", + "dense_22 (Dense) (None, 4) 244 dropout_33[0][0] \n", + "__________________________________________________________________________________________________\n", + "concatenate_4 (Concatenate) (None, 2, 4) 0 reshape_1[0][0] \n", + " reshape_2[0][0] \n", + "__________________________________________________________________________________________________\n", + "reshape_11 (Reshape) (None, 1, 4) 0 dense_22[0][0] \n", + "__________________________________________________________________________________________________\n", + "concatenate_7 (Concatenate) (None, 3, 4) 0 concatenate_4[0][0] \n", + " reshape_3[0][0] \n", + "__________________________________________________________________________________________________\n", + "concatenate_32 (Concatenate) (None, 59, 4) 0 concatenate_29[0][0] \n", + " reshape_11[0][0] \n", + "__________________________________________________________________________________________________\n", + "concatenate_10 (Concatenate) (None, 4, 4) 0 concatenate_7[0][0] \n", + " reshape_4[0][0] \n", + "__________________________________________________________________________________________________\n", + "conv1d_12 (Conv1D) (None, 57, 30) 390 concatenate_32[0][0] \n", + "__________________________________________________________________________________________________\n", + "concatenate_13 (Concatenate) (None, 5, 4) 0 concatenate_10[0][0] \n", + " reshape_5[0][0] \n", + "__________________________________________________________________________________________________\n", + "lstm_23 (LSTM) (None, 30) 7320 conv1d_12[0][0] \n", + "__________________________________________________________________________________________________\n", + "lstm_24 (LSTM) (None, 30) 4200 concatenate_32[0][0] \n", + "__________________________________________________________________________________________________\n", + "concatenate_16 (Concatenate) (None, 6, 4) 0 concatenate_13[0][0] \n", + " reshape_6[0][0] \n", + "__________________________________________________________________________________________________\n", + "dropout_34 (Dropout) (None, 30) 0 lstm_23[0][0] \n", + "__________________________________________________________________________________________________\n", + "dropout_35 (Dropout) (None, 30) 0 lstm_24[0][0] \n", + "__________________________________________________________________________________________________\n", + "concatenate_19 (Concatenate) (None, 7, 4) 0 concatenate_16[0][0] \n", + " reshape_7[0][0] \n", + "__________________________________________________________________________________________________\n", + "concatenate_33 (Concatenate) (None, 60) 0 dropout_34[0][0] \n", + " dropout_35[0][0] \n", + "__________________________________________________________________________________________________\n", + "concatenate_22 (Concatenate) (None, 8, 4) 0 concatenate_19[0][0] \n", + " reshape_8[0][0] \n", + "__________________________________________________________________________________________________\n", + "dense_23 (Dense) (None, 60) 3660 concatenate_33[0][0] \n", + "__________________________________________________________________________________________________\n", + "concatenate_25 (Concatenate) (None, 9, 4) 0 concatenate_22[0][0] \n", + " reshape_9[0][0] \n", + "__________________________________________________________________________________________________\n", + "dropout_36 (Dropout) (None, 60) 0 dense_23[0][0] \n", + "__________________________________________________________________________________________________\n", + "concatenate_28 (Concatenate) (None, 10, 4) 0 concatenate_25[0][0] \n", + " reshape_10[0][0] \n", + "__________________________________________________________________________________________________\n", + "dense_24 (Dense) (None, 4) 244 dropout_36[0][0] \n", + "__________________________________________________________________________________________________\n", + "concatenate_31 (Concatenate) (None, 11, 4) 0 concatenate_28[0][0] \n", + " reshape_11[0][0] \n", + "__________________________________________________________________________________________________\n", + "reshape_12 (Reshape) (None, 1, 4) 0 dense_24[0][0] \n", + "__________________________________________________________________________________________________\n", + "concatenate_34 (Concatenate) (None, 12, 4) 0 concatenate_31[0][0] \n", + " reshape_12[0][0] \n", + "==================================================================================================\n", + "Total params: 189,768\n", + "Trainable params: 189,768\n", + "Non-trainable params: 0\n", + "__________________________________________________________________________________________________\n" + ] + } + ], + "source": [ + "#model.add(LSTM(100, input_shape=(train_x.shape[1], train_x.shape[2]), activation=None))\n", + "#model.add(RepeatVector(n_out))\n", + "\n", + "#model.add(LSTM(60))\n", + "#model.add(Dropout(.1))\n", "\n", - "model.add(RepeatVector(n_out))\n", "\n", - "model.add(LSTM(units/2, return_sequences=True))\n", - "model.add(Dropout(dropout))\n", "\n", "# We're only concerned with the estimating the close value,\n", "# otherwise use n_outputs instead of 1\n", "# Dense(n_outputs, ...\n", - "model.add(TimeDistributed(Dense(1, activation='relu')))\n", - "\n", + "#model.add(TimeDistributed(Dense(1)))\n", + "model = keras.Model(inputs=inputs, outputs=results)\n", "model.compile(loss='mse', optimizer='adam')\n", "\n", "# store model\n", @@ -924,124 +1362,56 @@ }, { "cell_type": "code", - "execution_count": 12, - "metadata": {}, - "outputs": [], - "source": [ - "epochs=25\n", - "batch_size=1024" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "metadata": {}, + "execution_count": 52, + "metadata": { + "scrolled": false + }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Train on 23632 samples, validate on 5909 samples\n", - "Epoch 1/25\n", - "23632/23632 [==============================] - 3s 109us/step - loss: 0.1261 - val_loss: 0.0294\n", + "Train on 23618 samples, validate on 5905 samples\n", + "Epoch 1/10\n", + "23618/23618 [==============================] - 27s 1ms/step - loss: 0.0026 - val_loss: 0.0010\n", "\n", - "Epoch 00001: val_loss improved from inf to 0.02943, saving model to weights/BTC_ETH_lstm_i30_o12_Close_Volume_Low_High_model_weights.h5\n", - "Epoch 2/25\n", - "23632/23632 [==============================] - 1s 51us/step - loss: 0.0244 - val_loss: 0.0490\n", + "Epoch 00001: val_loss improved from inf to 0.00101, saving model to weights/BTC_ETH_lstm_i48_o12_Close_Volume_High_Low_model_weights.h5\n", + "Epoch 2/10\n", + "23618/23618 [==============================] - 26s 1ms/step - loss: 0.0025 - val_loss: 7.9232e-04\n", "\n", - "Epoch 00002: val_loss did not improve from 0.02943\n", - "Epoch 3/25\n", - "23632/23632 [==============================] - 1s 50us/step - loss: 0.0460 - val_loss: 0.0171\n", + "Epoch 00002: val_loss improved from 0.00101 to 0.00079, saving model to weights/BTC_ETH_lstm_i48_o12_Close_Volume_High_Low_model_weights.h5\n", + "Epoch 3/10\n", + "23618/23618 [==============================] - 25s 1ms/step - loss: 0.0022 - val_loss: 7.3845e-04\n", "\n", - "Epoch 00003: val_loss improved from 0.02943 to 0.01709, saving model to weights/BTC_ETH_lstm_i30_o12_Close_Volume_Low_High_model_weights.h5\n", - "Epoch 4/25\n", - "23632/23632 [==============================] - 1s 51us/step - loss: 0.0376 - val_loss: 0.0115\n", + "Epoch 00003: val_loss improved from 0.00079 to 0.00074, saving model to weights/BTC_ETH_lstm_i48_o12_Close_Volume_High_Low_model_weights.h5\n", + "Epoch 4/10\n", + "23618/23618 [==============================] - 26s 1ms/step - loss: 0.0021 - val_loss: 5.1478e-04\n", "\n", - "Epoch 00004: val_loss improved from 0.01709 to 0.01148, saving model to weights/BTC_ETH_lstm_i30_o12_Close_Volume_Low_High_model_weights.h5\n", - "Epoch 5/25\n", - "23632/23632 [==============================] - 1s 51us/step - loss: 0.0152 - val_loss: 0.0101\n", + "Epoch 00004: val_loss improved from 0.00074 to 0.00051, saving model to weights/BTC_ETH_lstm_i48_o12_Close_Volume_High_Low_model_weights.h5\n", + "Epoch 5/10\n", + "23618/23618 [==============================] - 26s 1ms/step - loss: 0.0022 - val_loss: 4.8799e-04\n", "\n", - "Epoch 00005: val_loss improved from 0.01148 to 0.01012, saving model to weights/BTC_ETH_lstm_i30_o12_Close_Volume_Low_High_model_weights.h5\n", - "Epoch 6/25\n", - "23632/23632 [==============================] - 1s 51us/step - loss: 0.0095 - val_loss: 0.0074\n", + "Epoch 00005: val_loss improved from 0.00051 to 0.00049, saving model to weights/BTC_ETH_lstm_i48_o12_Close_Volume_High_Low_model_weights.h5\n", + "Epoch 6/10\n", + "23618/23618 [==============================] - 27s 1ms/step - loss: 0.0021 - val_loss: 6.6165e-04\n", "\n", - "Epoch 00006: val_loss improved from 0.01012 to 0.00737, saving model to weights/BTC_ETH_lstm_i30_o12_Close_Volume_Low_High_model_weights.h5\n", - "Epoch 7/25\n", - "23632/23632 [==============================] - 1s 50us/step - loss: 0.0076 - val_loss: 0.0054\n", + "Epoch 00006: val_loss did not improve from 0.00049\n", + "Epoch 7/10\n", + "23618/23618 [==============================] - 29s 1ms/step - loss: 0.0021 - val_loss: 3.1704e-04\n", "\n", - "Epoch 00007: val_loss improved from 0.00737 to 0.00537, saving model to weights/BTC_ETH_lstm_i30_o12_Close_Volume_Low_High_model_weights.h5\n", - "Epoch 8/25\n", - "23632/23632 [==============================] - 1s 50us/step - loss: 0.0064 - val_loss: 0.0065\n", + "Epoch 00007: val_loss improved from 0.00049 to 0.00032, saving model to weights/BTC_ETH_lstm_i48_o12_Close_Volume_High_Low_model_weights.h5\n", + "Epoch 8/10\n", + "23618/23618 [==============================] - 28s 1ms/step - loss: 0.0021 - val_loss: 7.2353e-04\n", "\n", - "Epoch 00008: val_loss did not improve from 0.00537\n", - "Epoch 9/25\n", - "23632/23632 [==============================] - 1s 50us/step - loss: 0.0073 - val_loss: 0.0123\n", + "Epoch 00008: val_loss did not improve from 0.00032\n", + "Epoch 9/10\n", + "23618/23618 [==============================] - 27s 1ms/step - loss: 0.0024 - val_loss: 4.8849e-04\n", "\n", - "Epoch 00009: val_loss did not improve from 0.00537\n", - "Epoch 10/25\n", - "23632/23632 [==============================] - 1s 51us/step - loss: 0.0116 - val_loss: 0.0130\n", + "Epoch 00009: val_loss did not improve from 0.00032\n", + "Epoch 10/10\n", + "23618/23618 [==============================] - 30s 1ms/step - loss: 0.0022 - val_loss: 8.4974e-04\n", "\n", - "Epoch 00010: val_loss did not improve from 0.00537\n", - "Epoch 11/25\n", - "23632/23632 [==============================] - 1s 51us/step - loss: 0.0138 - val_loss: 0.0053\n", - "\n", - "Epoch 00011: val_loss improved from 0.00537 to 0.00531, saving model to weights/BTC_ETH_lstm_i30_o12_Close_Volume_Low_High_model_weights.h5\n", - "Epoch 12/25\n", - "23632/23632 [==============================] - 1s 50us/step - loss: 0.0087 - val_loss: 0.0030\n", - "\n", - "Epoch 00012: val_loss improved from 0.00531 to 0.00302, saving model to weights/BTC_ETH_lstm_i30_o12_Close_Volume_Low_High_model_weights.h5\n", - "Epoch 13/25\n", - "23632/23632 [==============================] - 1s 50us/step - loss: 0.0053 - val_loss: 0.0023\n", - "\n", - "Epoch 00013: val_loss improved from 0.00302 to 0.00225, saving model to weights/BTC_ETH_lstm_i30_o12_Close_Volume_Low_High_model_weights.h5\n", - "Epoch 14/25\n", - "23632/23632 [==============================] - 1s 50us/step - loss: 0.0043 - val_loss: 0.0023\n", - "\n", - "Epoch 00014: val_loss did not improve from 0.00225\n", - "Epoch 15/25\n", - "23632/23632 [==============================] - 1s 51us/step - loss: 0.0042 - val_loss: 0.0034\n", - "\n", - "Epoch 00015: val_loss did not improve from 0.00225\n", - "Epoch 16/25\n", - "23632/23632 [==============================] - 1s 50us/step - loss: 0.0048 - val_loss: 0.0057\n", - "\n", - "Epoch 00016: val_loss did not improve from 0.00225\n", - "Epoch 17/25\n", - "23632/23632 [==============================] - 1s 51us/step - loss: 0.0064 - val_loss: 0.0080\n", - "\n", - "Epoch 00017: val_loss did not improve from 0.00225\n", - "Epoch 18/25\n", - "23632/23632 [==============================] - 1s 50us/step - loss: 0.0082 - val_loss: 0.0068\n", - "\n", - "Epoch 00018: val_loss did not improve from 0.00225\n", - "Epoch 19/25\n", - "23632/23632 [==============================] - 1s 51us/step - loss: 0.0076 - val_loss: 0.0043\n", - "\n", - "Epoch 00019: val_loss did not improve from 0.00225\n", - "Epoch 20/25\n", - "23632/23632 [==============================] - 1s 50us/step - loss: 0.0057 - val_loss: 0.0027\n", - "\n", - "Epoch 00020: val_loss did not improve from 0.00225\n", - "Epoch 21/25\n", - "23632/23632 [==============================] - 1s 50us/step - loss: 0.0043 - val_loss: 0.0022\n", - "\n", - "Epoch 00021: val_loss improved from 0.00225 to 0.00220, saving model to weights/BTC_ETH_lstm_i30_o12_Close_Volume_Low_High_model_weights.h5\n", - "Epoch 22/25\n", - "23632/23632 [==============================] - 1s 51us/step - loss: 0.0039 - val_loss: 0.0025\n", - "\n", - "Epoch 00022: val_loss did not improve from 0.00220\n", - "Epoch 23/25\n", - "23632/23632 [==============================] - 1s 51us/step - loss: 0.0040 - val_loss: 0.0029\n", - "\n", - "Epoch 00023: val_loss did not improve from 0.00220\n", - "Epoch 24/25\n", - "23632/23632 [==============================] - 1s 51us/step - loss: 0.0042 - val_loss: 0.0037\n", - "\n", - "Epoch 00024: val_loss did not improve from 0.00220\n", - "Epoch 25/25\n", - "23632/23632 [==============================] - 1s 51us/step - loss: 0.0047 - val_loss: 0.0041\n", - "\n", - "Epoch 00025: val_loss did not improve from 0.00220\n" + "Epoch 00010: val_loss did not improve from 0.00032\n" ] } ], @@ -1055,37 +1425,362 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 53, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "5909/5909 [==============================] - 2s 371us/step\n" + "__________________________________________________________________________________________________\n", + "Layer (type) Output Shape Param # Connected to \n", + "==================================================================================================\n", + "input_1 (InputLayer) (None, 48, 4) 0 \n", + "__________________________________________________________________________________________________\n", + "conv1d_1 (Conv1D) (None, 46, 30) 390 input_1[0][0] \n", + "__________________________________________________________________________________________________\n", + "lstm_1 (LSTM) (None, 30) 7320 conv1d_1[0][0] \n", + "__________________________________________________________________________________________________\n", + "lstm_2 (LSTM) (None, 30) 4200 input_1[0][0] \n", + "__________________________________________________________________________________________________\n", + "dropout_1 (Dropout) (None, 30) 0 lstm_1[0][0] \n", + "__________________________________________________________________________________________________\n", + "dropout_2 (Dropout) (None, 30) 0 lstm_2[0][0] \n", + "__________________________________________________________________________________________________\n", + "concatenate_1 (Concatenate) (None, 60) 0 dropout_1[0][0] \n", + " dropout_2[0][0] \n", + "__________________________________________________________________________________________________\n", + "dense_1 (Dense) (None, 60) 3660 concatenate_1[0][0] \n", + "__________________________________________________________________________________________________\n", + "dropout_3 (Dropout) (None, 60) 0 dense_1[0][0] \n", + "__________________________________________________________________________________________________\n", + "dense_2 (Dense) (None, 4) 244 dropout_3[0][0] \n", + "__________________________________________________________________________________________________\n", + "reshape_1 (Reshape) (None, 1, 4) 0 dense_2[0][0] \n", + "__________________________________________________________________________________________________\n", + "concatenate_2 (Concatenate) (None, 49, 4) 0 input_1[0][0] \n", + " reshape_1[0][0] \n", + "__________________________________________________________________________________________________\n", + "conv1d_2 (Conv1D) (None, 47, 30) 390 concatenate_2[0][0] \n", + "__________________________________________________________________________________________________\n", + "lstm_3 (LSTM) (None, 30) 7320 conv1d_2[0][0] \n", + "__________________________________________________________________________________________________\n", + "lstm_4 (LSTM) (None, 30) 4200 concatenate_2[0][0] \n", + "__________________________________________________________________________________________________\n", + "dropout_4 (Dropout) (None, 30) 0 lstm_3[0][0] \n", + "__________________________________________________________________________________________________\n", + "dropout_5 (Dropout) (None, 30) 0 lstm_4[0][0] \n", + "__________________________________________________________________________________________________\n", + "concatenate_3 (Concatenate) (None, 60) 0 dropout_4[0][0] \n", + " dropout_5[0][0] \n", + "__________________________________________________________________________________________________\n", + "dense_3 (Dense) (None, 60) 3660 concatenate_3[0][0] \n", + "__________________________________________________________________________________________________\n", + "dropout_6 (Dropout) (None, 60) 0 dense_3[0][0] \n", + "__________________________________________________________________________________________________\n", + "dense_4 (Dense) (None, 4) 244 dropout_6[0][0] \n", + "__________________________________________________________________________________________________\n", + "reshape_2 (Reshape) (None, 1, 4) 0 dense_4[0][0] \n", + "__________________________________________________________________________________________________\n", + "concatenate_5 (Concatenate) (None, 50, 4) 0 concatenate_2[0][0] \n", + " reshape_2[0][0] \n", + "__________________________________________________________________________________________________\n", + "conv1d_3 (Conv1D) (None, 48, 30) 390 concatenate_5[0][0] \n", + "__________________________________________________________________________________________________\n", + "lstm_5 (LSTM) (None, 30) 7320 conv1d_3[0][0] \n", + "__________________________________________________________________________________________________\n", + "lstm_6 (LSTM) (None, 30) 4200 concatenate_5[0][0] \n", + "__________________________________________________________________________________________________\n", + "dropout_7 (Dropout) (None, 30) 0 lstm_5[0][0] \n", + "__________________________________________________________________________________________________\n", + "dropout_8 (Dropout) (None, 30) 0 lstm_6[0][0] \n", + "__________________________________________________________________________________________________\n", + "concatenate_6 (Concatenate) (None, 60) 0 dropout_7[0][0] \n", + " dropout_8[0][0] \n", + "__________________________________________________________________________________________________\n", + "dense_5 (Dense) (None, 60) 3660 concatenate_6[0][0] \n", + "__________________________________________________________________________________________________\n", + "dropout_9 (Dropout) (None, 60) 0 dense_5[0][0] \n", + "__________________________________________________________________________________________________\n", + "dense_6 (Dense) (None, 4) 244 dropout_9[0][0] \n", + "__________________________________________________________________________________________________\n", + "reshape_3 (Reshape) (None, 1, 4) 0 dense_6[0][0] \n", + "__________________________________________________________________________________________________\n", + "concatenate_8 (Concatenate) (None, 51, 4) 0 concatenate_5[0][0] \n", + " reshape_3[0][0] \n", + "__________________________________________________________________________________________________\n", + "conv1d_4 (Conv1D) (None, 49, 30) 390 concatenate_8[0][0] \n", + "__________________________________________________________________________________________________\n", + "lstm_7 (LSTM) (None, 30) 7320 conv1d_4[0][0] \n", + "__________________________________________________________________________________________________\n", + "lstm_8 (LSTM) (None, 30) 4200 concatenate_8[0][0] \n", + "__________________________________________________________________________________________________\n", + "dropout_10 (Dropout) (None, 30) 0 lstm_7[0][0] \n", + "__________________________________________________________________________________________________\n", + "dropout_11 (Dropout) (None, 30) 0 lstm_8[0][0] \n", + "__________________________________________________________________________________________________\n", + "concatenate_9 (Concatenate) (None, 60) 0 dropout_10[0][0] \n", + " dropout_11[0][0] \n", + "__________________________________________________________________________________________________\n", + "dense_7 (Dense) (None, 60) 3660 concatenate_9[0][0] \n", + "__________________________________________________________________________________________________\n", + "dropout_12 (Dropout) (None, 60) 0 dense_7[0][0] \n", + "__________________________________________________________________________________________________\n", + "dense_8 (Dense) (None, 4) 244 dropout_12[0][0] \n", + "__________________________________________________________________________________________________\n", + "reshape_4 (Reshape) (None, 1, 4) 0 dense_8[0][0] \n", + "__________________________________________________________________________________________________\n", + "concatenate_11 (Concatenate) (None, 52, 4) 0 concatenate_8[0][0] \n", + " reshape_4[0][0] \n", + "__________________________________________________________________________________________________\n", + "conv1d_5 (Conv1D) (None, 50, 30) 390 concatenate_11[0][0] \n", + "__________________________________________________________________________________________________\n", + "lstm_9 (LSTM) (None, 30) 7320 conv1d_5[0][0] \n", + "__________________________________________________________________________________________________\n", + "lstm_10 (LSTM) (None, 30) 4200 concatenate_11[0][0] \n", + "__________________________________________________________________________________________________\n", + "dropout_13 (Dropout) (None, 30) 0 lstm_9[0][0] \n", + "__________________________________________________________________________________________________\n", + "dropout_14 (Dropout) (None, 30) 0 lstm_10[0][0] \n", + "__________________________________________________________________________________________________\n", + "concatenate_12 (Concatenate) (None, 60) 0 dropout_13[0][0] \n", + " dropout_14[0][0] \n", + "__________________________________________________________________________________________________\n", + "dense_9 (Dense) (None, 60) 3660 concatenate_12[0][0] \n", + "__________________________________________________________________________________________________\n", + "dropout_15 (Dropout) (None, 60) 0 dense_9[0][0] \n", + "__________________________________________________________________________________________________\n", + "dense_10 (Dense) (None, 4) 244 dropout_15[0][0] \n", + "__________________________________________________________________________________________________\n", + "reshape_5 (Reshape) (None, 1, 4) 0 dense_10[0][0] \n", + "__________________________________________________________________________________________________\n", + "concatenate_14 (Concatenate) (None, 53, 4) 0 concatenate_11[0][0] \n", + " reshape_5[0][0] \n", + "__________________________________________________________________________________________________\n", + "conv1d_6 (Conv1D) (None, 51, 30) 390 concatenate_14[0][0] \n", + "__________________________________________________________________________________________________\n", + "lstm_11 (LSTM) (None, 30) 7320 conv1d_6[0][0] \n", + "__________________________________________________________________________________________________\n", + "lstm_12 (LSTM) (None, 30) 4200 concatenate_14[0][0] \n", + "__________________________________________________________________________________________________\n", + "dropout_16 (Dropout) (None, 30) 0 lstm_11[0][0] \n", + "__________________________________________________________________________________________________\n", + "dropout_17 (Dropout) (None, 30) 0 lstm_12[0][0] \n", + "__________________________________________________________________________________________________\n", + "concatenate_15 (Concatenate) (None, 60) 0 dropout_16[0][0] \n", + " dropout_17[0][0] \n", + "__________________________________________________________________________________________________\n", + "dense_11 (Dense) (None, 60) 3660 concatenate_15[0][0] \n", + "__________________________________________________________________________________________________\n", + "dropout_18 (Dropout) (None, 60) 0 dense_11[0][0] \n", + "__________________________________________________________________________________________________\n", + "dense_12 (Dense) (None, 4) 244 dropout_18[0][0] \n", + "__________________________________________________________________________________________________\n", + "reshape_6 (Reshape) (None, 1, 4) 0 dense_12[0][0] \n", + "__________________________________________________________________________________________________\n", + "concatenate_17 (Concatenate) (None, 54, 4) 0 concatenate_14[0][0] \n", + " reshape_6[0][0] \n", + "__________________________________________________________________________________________________\n", + "conv1d_7 (Conv1D) (None, 52, 30) 390 concatenate_17[0][0] \n", + "__________________________________________________________________________________________________\n", + "lstm_13 (LSTM) (None, 30) 7320 conv1d_7[0][0] \n", + "__________________________________________________________________________________________________\n", + "lstm_14 (LSTM) (None, 30) 4200 concatenate_17[0][0] \n", + "__________________________________________________________________________________________________\n", + "dropout_19 (Dropout) (None, 30) 0 lstm_13[0][0] \n", + "__________________________________________________________________________________________________\n", + "dropout_20 (Dropout) (None, 30) 0 lstm_14[0][0] \n", + "__________________________________________________________________________________________________\n", + "concatenate_18 (Concatenate) (None, 60) 0 dropout_19[0][0] \n", + " dropout_20[0][0] \n", + "__________________________________________________________________________________________________\n", + "dense_13 (Dense) (None, 60) 3660 concatenate_18[0][0] \n", + "__________________________________________________________________________________________________\n", + "dropout_21 (Dropout) (None, 60) 0 dense_13[0][0] \n", + "__________________________________________________________________________________________________\n", + "dense_14 (Dense) (None, 4) 244 dropout_21[0][0] \n", + "__________________________________________________________________________________________________\n", + "reshape_7 (Reshape) (None, 1, 4) 0 dense_14[0][0] \n", + "__________________________________________________________________________________________________\n", + "concatenate_20 (Concatenate) (None, 55, 4) 0 concatenate_17[0][0] \n", + " reshape_7[0][0] \n", + "__________________________________________________________________________________________________\n", + "conv1d_8 (Conv1D) (None, 53, 30) 390 concatenate_20[0][0] \n", + "__________________________________________________________________________________________________\n", + "lstm_15 (LSTM) (None, 30) 7320 conv1d_8[0][0] \n", + "__________________________________________________________________________________________________\n", + "lstm_16 (LSTM) (None, 30) 4200 concatenate_20[0][0] \n", + "__________________________________________________________________________________________________\n", + "dropout_22 (Dropout) (None, 30) 0 lstm_15[0][0] \n", + "__________________________________________________________________________________________________\n", + "dropout_23 (Dropout) (None, 30) 0 lstm_16[0][0] \n", + "__________________________________________________________________________________________________\n", + "concatenate_21 (Concatenate) (None, 60) 0 dropout_22[0][0] \n", + " dropout_23[0][0] \n", + "__________________________________________________________________________________________________\n", + "dense_15 (Dense) (None, 60) 3660 concatenate_21[0][0] \n", + "__________________________________________________________________________________________________\n", + "dropout_24 (Dropout) (None, 60) 0 dense_15[0][0] \n", + "__________________________________________________________________________________________________\n", + "dense_16 (Dense) (None, 4) 244 dropout_24[0][0] \n", + "__________________________________________________________________________________________________\n", + "reshape_8 (Reshape) (None, 1, 4) 0 dense_16[0][0] \n", + "__________________________________________________________________________________________________\n", + "concatenate_23 (Concatenate) (None, 56, 4) 0 concatenate_20[0][0] \n", + " reshape_8[0][0] \n", + "__________________________________________________________________________________________________\n", + "conv1d_9 (Conv1D) (None, 54, 30) 390 concatenate_23[0][0] \n", + "__________________________________________________________________________________________________\n", + "lstm_17 (LSTM) (None, 30) 7320 conv1d_9[0][0] \n", + "__________________________________________________________________________________________________\n", + "lstm_18 (LSTM) (None, 30) 4200 concatenate_23[0][0] \n", + "__________________________________________________________________________________________________\n", + "dropout_25 (Dropout) (None, 30) 0 lstm_17[0][0] \n", + "__________________________________________________________________________________________________\n", + "dropout_26 (Dropout) (None, 30) 0 lstm_18[0][0] \n", + "__________________________________________________________________________________________________\n", + "concatenate_24 (Concatenate) (None, 60) 0 dropout_25[0][0] \n", + " dropout_26[0][0] \n", + "__________________________________________________________________________________________________\n", + "dense_17 (Dense) (None, 60) 3660 concatenate_24[0][0] \n", + "__________________________________________________________________________________________________\n", + "dropout_27 (Dropout) (None, 60) 0 dense_17[0][0] \n", + "__________________________________________________________________________________________________\n", + "dense_18 (Dense) (None, 4) 244 dropout_27[0][0] \n", + "__________________________________________________________________________________________________\n", + "reshape_9 (Reshape) (None, 1, 4) 0 dense_18[0][0] \n", + "__________________________________________________________________________________________________\n", + "concatenate_26 (Concatenate) (None, 57, 4) 0 concatenate_23[0][0] \n", + " reshape_9[0][0] \n", + "__________________________________________________________________________________________________\n", + "conv1d_10 (Conv1D) (None, 55, 30) 390 concatenate_26[0][0] \n", + "__________________________________________________________________________________________________\n", + "lstm_19 (LSTM) (None, 30) 7320 conv1d_10[0][0] \n", + "__________________________________________________________________________________________________\n", + "lstm_20 (LSTM) (None, 30) 4200 concatenate_26[0][0] \n", + "__________________________________________________________________________________________________\n", + "dropout_28 (Dropout) (None, 30) 0 lstm_19[0][0] \n", + "__________________________________________________________________________________________________\n", + "dropout_29 (Dropout) (None, 30) 0 lstm_20[0][0] \n", + "__________________________________________________________________________________________________\n", + "concatenate_27 (Concatenate) (None, 60) 0 dropout_28[0][0] \n", + " dropout_29[0][0] \n", + "__________________________________________________________________________________________________\n", + "dense_19 (Dense) (None, 60) 3660 concatenate_27[0][0] \n", + "__________________________________________________________________________________________________\n", + "dropout_30 (Dropout) (None, 60) 0 dense_19[0][0] \n", + "__________________________________________________________________________________________________\n", + "dense_20 (Dense) (None, 4) 244 dropout_30[0][0] \n", + "__________________________________________________________________________________________________\n", + "reshape_10 (Reshape) (None, 1, 4) 0 dense_20[0][0] \n", + "__________________________________________________________________________________________________\n", + "concatenate_29 (Concatenate) (None, 58, 4) 0 concatenate_26[0][0] \n", + " reshape_10[0][0] \n", + "__________________________________________________________________________________________________\n", + "conv1d_11 (Conv1D) (None, 56, 30) 390 concatenate_29[0][0] \n", + "__________________________________________________________________________________________________\n", + "lstm_21 (LSTM) (None, 30) 7320 conv1d_11[0][0] \n", + "__________________________________________________________________________________________________\n", + "lstm_22 (LSTM) (None, 30) 4200 concatenate_29[0][0] \n", + "__________________________________________________________________________________________________\n", + "dropout_31 (Dropout) (None, 30) 0 lstm_21[0][0] \n", + "__________________________________________________________________________________________________\n", + "dropout_32 (Dropout) (None, 30) 0 lstm_22[0][0] \n", + "__________________________________________________________________________________________________\n", + "concatenate_30 (Concatenate) (None, 60) 0 dropout_31[0][0] \n", + " dropout_32[0][0] \n", + "__________________________________________________________________________________________________\n", + "dense_21 (Dense) (None, 60) 3660 concatenate_30[0][0] \n", + "__________________________________________________________________________________________________\n", + "dropout_33 (Dropout) (None, 60) 0 dense_21[0][0] \n", + "__________________________________________________________________________________________________\n", + "dense_22 (Dense) (None, 4) 244 dropout_33[0][0] \n", + "__________________________________________________________________________________________________\n", + "concatenate_4 (Concatenate) (None, 2, 4) 0 reshape_1[0][0] \n", + " reshape_2[0][0] \n", + "__________________________________________________________________________________________________\n", + "reshape_11 (Reshape) (None, 1, 4) 0 dense_22[0][0] \n", + "__________________________________________________________________________________________________\n", + "concatenate_7 (Concatenate) (None, 3, 4) 0 concatenate_4[0][0] \n", + " reshape_3[0][0] \n", + "__________________________________________________________________________________________________\n", + "concatenate_32 (Concatenate) (None, 59, 4) 0 concatenate_29[0][0] \n", + " reshape_11[0][0] \n", + "__________________________________________________________________________________________________\n", + "concatenate_10 (Concatenate) (None, 4, 4) 0 concatenate_7[0][0] \n", + " reshape_4[0][0] \n", + "__________________________________________________________________________________________________\n", + "conv1d_12 (Conv1D) (None, 57, 30) 390 concatenate_32[0][0] \n", + "__________________________________________________________________________________________________\n", + "concatenate_13 (Concatenate) (None, 5, 4) 0 concatenate_10[0][0] \n", + " reshape_5[0][0] \n", + "__________________________________________________________________________________________________\n", + "lstm_23 (LSTM) (None, 30) 7320 conv1d_12[0][0] \n", + "__________________________________________________________________________________________________\n", + "lstm_24 (LSTM) (None, 30) 4200 concatenate_32[0][0] \n", + "__________________________________________________________________________________________________\n", + "concatenate_16 (Concatenate) (None, 6, 4) 0 concatenate_13[0][0] \n", + " reshape_6[0][0] \n", + "__________________________________________________________________________________________________\n", + "dropout_34 (Dropout) (None, 30) 0 lstm_23[0][0] \n", + "__________________________________________________________________________________________________\n", + "dropout_35 (Dropout) (None, 30) 0 lstm_24[0][0] \n", + "__________________________________________________________________________________________________\n", + "concatenate_19 (Concatenate) (None, 7, 4) 0 concatenate_16[0][0] \n", + " reshape_7[0][0] \n", + "__________________________________________________________________________________________________\n", + "concatenate_33 (Concatenate) (None, 60) 0 dropout_34[0][0] \n", + " dropout_35[0][0] \n", + "__________________________________________________________________________________________________\n", + "concatenate_22 (Concatenate) (None, 8, 4) 0 concatenate_19[0][0] \n", + " reshape_8[0][0] \n", + "__________________________________________________________________________________________________\n", + "dense_23 (Dense) (None, 60) 3660 concatenate_33[0][0] \n", + "__________________________________________________________________________________________________\n", + "concatenate_25 (Concatenate) (None, 9, 4) 0 concatenate_22[0][0] \n", + " reshape_9[0][0] \n", + "__________________________________________________________________________________________________\n", + "dropout_36 (Dropout) (None, 60) 0 dense_23[0][0] \n", + "__________________________________________________________________________________________________\n", + "concatenate_28 (Concatenate) (None, 10, 4) 0 concatenate_25[0][0] \n", + " reshape_10[0][0] \n", + "__________________________________________________________________________________________________\n", + "dense_24 (Dense) (None, 4) 244 dropout_36[0][0] \n", + "__________________________________________________________________________________________________\n", + "concatenate_31 (Concatenate) (None, 11, 4) 0 concatenate_28[0][0] \n", + " reshape_11[0][0] \n", + "__________________________________________________________________________________________________\n", + "reshape_12 (Reshape) (None, 1, 4) 0 dense_24[0][0] \n", + "__________________________________________________________________________________________________\n", + "concatenate_34 (Concatenate) (None, 12, 4) 0 concatenate_31[0][0] \n", + " reshape_12[0][0] \n", + "==================================================================================================\n", + "Total params: 189,768\n", + "Trainable params: 189,768\n", + "Non-trainable params: 0\n", + "__________________________________________________________________________________________________\n" ] - }, - { - "data": { - "text/plain": [ - "0.002201281368157488" - ] - }, - "execution_count": 14, - "metadata": {}, - "output_type": "execute_result" } ], + "source": [ + "model.summary()" + ] + }, + { + "cell_type": "code", + "execution_count": 54, + "metadata": {}, + "outputs": [], "source": [ "# Load the best weights\n", "model.load_weights(model_weights_name)\n", "model.compile(loss='mse', optimizer='adam')\n", - "model.evaluate(test_x, test_y)" + "#model.evaluate(test_x, test_y)" ] }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 55, "metadata": {}, "outputs": [], "source": [ @@ -1094,31 +1789,31 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 56, "metadata": {}, "outputs": [ { "data": { - "image/png": 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\n", "text/plain": [ - "
" + "\"pyplot.plot(history.history['loss'], label='train')\\npyplot.plot(history.history['val_loss'], label='test')\\npyplot.legend()\\npyplot.show()\"" ] }, + "execution_count": 56, "metadata": {}, - "output_type": "display_data" + "output_type": "execute_result" } ], "source": [ "# plot history\n", - "pyplot.plot(history.history['loss'], label='train')\n", + "\"\"\"pyplot.plot(history.history['loss'], label='train')\n", "pyplot.plot(history.history['val_loss'], label='test')\n", "pyplot.legend()\n", - "pyplot.show()" + "pyplot.show()\"\"\"" ] }, { "cell_type": "code", - "execution_count": 20, + "execution_count": 57, "metadata": {}, "outputs": [], "source": [ @@ -1128,14 +1823,14 @@ }, { "cell_type": "code", - "execution_count": 21, + "execution_count": 58, "metadata": {}, "outputs": [ { "data": { - "image/png": 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\n", 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\n", 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\n", 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VAhDq686QLiEMq2157Rrmo/AqIiIiItLONGY34Gwgut5xFLD/JOf+Crij3vEVwGrDMEpri/oaGAqcNKzKSVgsENbD3AbNMsNrzs6j4XXPItj8vnluQDRED4GQeLPVtW7z7QAuTbtakcViITrYm+hgb64ZFI1hGGTlV7AqLZfVafmsSs1jweYDAIT7eXDfRQlclRSp0CoiIiIiIsdoSMuqFXOCpfHAPswJlq4zDGPrcef1AL4FOtfN9muxWK4FbgYuwuwG/A0w2zCML0/2PLWsniXDgJwdta2uy2DfenOCp/pjYK2eZqtr/QB7ZIs1uyw3eZkGmfnlrE7LY35yNskZBVzWrxP/vKI3/p5aBkdEREREpK1raMvqacNq7c0mAbMxl6553TCMRy0Wy9+BZMMwvqg956+Ap2EY99e7zhV4EXM2YAP4xjCM35/qWQqrjchWbU7sVLAXCtKP3fLTobrk2PN9ws2W28gkiBwAkQPBv1OTdSm2Owxe/HEPsxfvpmOAJ89OSyQpJqhJniUiIiIiIi1Do4bV5qSw2kwMw5x9uCC9XpjdC4e2wcEt4Kgxz/ONgKiBtQF2IHRKBE//Ri0lJaOAe97fwIGiSn53QTd+e348rloCR0RERESkTVJYlbNnqzID674UyE429/mptR/Wjp2NHHB063AeuJ5bF97iyhoe/PRnvty0n6Fdgnn62v50DPA69+8iIiIiIiItisKqNK7yfNi/HrJTzPC6L9lcWgfMsbBdzoc+U6HHxWc99tUwDD5KyeaRL7bibnXhP1f1ZeJ5EY32FURERERExPkUVqVpGQYUZpjBNWstbP8SiveBmzckTDaDa9dxZ9XimpZTyt3vb+DnfcVMHxLDQ5f0wtPNtQm+hIiIiIiINDeFVWleDgdkroIt82HbZ1BRAF7BcN4VZnCNHnJGy+ZU2xw88d1O5ixNo3sHX56dlkhCROOOlRURERERkeansCrOY6uG1MVmcN2xEGwV5tqvva8yg2tE7wbfaumuHH7/4SaKK2v4y+SeXD80VmuyilMYhkF5tZ3iyhqKK2yUVNZQXFlDSaUNgInnRagHgIiIiEgDKKxKy1BVCjsXwuYPIfUHMOwQ3gv6XA29rzbXdz2N3NIq7p2/iR935nBBzw48fnVfgn3cm6F4aU3ySqtIzSljz+FSUnNKKasyQ6T5uw3LkdcWjq7GZMFy5L06ZdV2iivMEFpcL5CWVNqwO07+92W4nwd3jovn2kHReFgVWkVERERORmFVWp6yXNj6KWz5CLJWm+91OR8G3gQ9JoGr9aSXGobBGyvS+ffXOwj0duPfV/VhXEKHZilbWg6Hw2BfYcWRQFp/X1Bec+Q8TzcXArzcMAxzgWcwh1mDQd1feQbmP1fHfg4+7q74e7nh52nF37N2f8yxG/5ex352sKiS2Yt2sS69gMhAL+4Z340rkyKxuja867uIiIhIe6GwKi1bQQZs/gBS3oLibPDrCANmQtIM8O940su27i/i9x9sYuehEqYOiOKhS3vh73luy+ZIy1Rtc7B2bz4pGQXsqQ2kaTmlVNkcR84J9nEnPsyXruG+dA3zIT7cl65hvkQGeuHSzGv1GobB0t25PPndTjZnF9E51If/u6Abl/bt1Oy1iIiIiLRkCqvSOthtsPs7SH4N9iwCiyskTIJBs6DzmKP9Neupstl5dvFuXvoplQ7+nvznqr6M7h7mhOKlseWXVfPjjsMs3nGIpbtyKa2yYbFAVJAXXcN8iQ/zNQNpbShtid3BDcPg+22HeOr7Xew4WEKPDn78fkJ3JvTqoPHWIiIiIiisSmuUnwbJb8CGd6AiH0LiYeCN0P868Ar6xekbswr54/xN7DlcyrTBMTw4uSe+HifvSiwtj2EY7DlcyqLth1m8/RDrMwtwGOb4z/E9wxmf0IHh8SF4u7e+n6vDYfDVlgPM/n4Xabll9I0K4A8TejC6W6hCq4iIiLRrCqvSetVUwrbPYd1cyF4LVi9zJuFBN0LkgGNOrayx8/SiXby6NI2OAV48fnVfRsSHOqlwaYgau9m9d9H2QyzefpjM/HIAekf6My6hAxf0DKd3p4A203XWZnfwyYZ9PLNoN/sKKxgcF8wfJnRnSJcQZ5cmIiIi4hQKq9I2HNwC614zZxOuKYOO/c0uwn2mgpvnkdNSMgq4d/4m0nLLuH5oLPdfnICPWlkbXWF5NXsOl1JtcxwzMVHdNEXHT15U9xogv7SaH3YeZunOHEqqbHhYXRgRH3qkBTUiwJO2rNrm4IN1mTz3wx4Ol1Qxqlso907sQd+oQGeXJiIiItKsFFalbaksNidkWvca5GwH71Czi/CgWeBnzgpcWWPniW938tqKvUQFefHfq/sxVK1XZ6Wyxs6ew6XsPFjCzkMl7DhYws6DxRwqrjqn+4b5eTA+IZzxPTswopV27z1XlTV2/rcqg5eWpFJUUcP9FyUwa1RndQ0WERGRdkNhVdomw4D0ZbDqRdj1Dbi6meu1DrsdIvoAsC49nz/O30RGXjkzh8dx30U92mUoagiHwyAzv7w2jJaw81AxOw6WkJ5bRt2Sou5WF7qF+9Ijwo8eHfzo3sEPb3dzHdG6gFV/rdKjmctSbz1T8Ha30i3ct8107z1XxZU13Dd/M99sPcikPhE8fnU/jbkWERGRdkFhVdq+vFRY/RJsfBdqyiFuFAy7A7pNpNzm4PFvdvLmynTiQrz579R+DIoLdnbFjcruMFi2O4ePUrLZcbDkjK83DIMDRZWUV9sBM2TGBHvTo4MfCRF+9Ijwp0eEH3Eh3lovtIkYhsGcpWn855sddA714ZXrBxAf7ufsskRERESalMKqtB8VBeZ6rWvnQPE+CO4KQ38L/aaxKruK+z7eRHZBBVclRTGpTwTDuoTiVdsy2Bql5ZTyUUo2n6zfx8HiSoK83RjSOQTXs2ixDPf3OBJMu3fwVQu0k6xMzeWueRuorLHz+NX9mNz35GsNi4iIiLR2CqvS/thrzFmEV78I+1LAMwAGzKS8/038Z2UJ81OyKa+2H5nYZ2xCOOMSwokM9HJ25adVWmVjweb9zE/OJjmjABcLnN8jnKkDohjXMxwPa+sN32I6UFTB7e+uZ0NmITeP6syfLkpQi7aIiIi0SQqr0n4ZBmSthdUvwPYvAQv0mkL10LtYXRHNDzsO88OOo0umJET4MTYhnPEJ4STGBJ1VC2VTcDgM1uzNZ35KFl9vOUhFjZ2uYT5MHRjNlYmRhPu37dlz26Nqm4NHF2zjrVUZDOkczHPXJRLup5+ziIiItC0KqyIABRlm9+D1b0NVMfSfDuMfxvDtQGpOGT/sOMQPOw6TnF6AzWEQ6O3GmO5hjEsIZ0z3MAK93Zu95OyCcj5O2cdH67PIyq/Az8PKJf06MXVgFInRgZo1th34dEM2f/5kC/6ebrw4PYmBbWy8tYiIiLRvCqsi9VUWwbInzVmErR4w+l5zXKvVA4CiihqW7c7hhx2H+WlnDvll1bhYYEBsEDHBPmf1yLPJlPsKKli9Nw/DgBHxIUwdEM3E8yJa9RhbOTvbDxTz23dSyC6o4MHJPZk5PE6/qBAREZE2QWFV5ETyUuHbB8xlb4K7wMTHoPtFxyRLu8NgU3YhP2w/zJJdZnBtLj4erkzq05GrkqKIDvZutudKy1RUUcMfPtzEou2HuKxfJ/59VR9NgiUiIiKtnsKqyKnsXgTf/hlyd0HX8XDRvyCsh7OrEvkFh8PgpSWpPPndTrqF+/HSr5PoEubr7LJEREREzlpDw6qmmpT2qdsF8NuVMPFfkJ0MLw2Hb/4MFYXOrkzkGC4uFu4YG89bNw7mcEklU55fwZJdOc4uS0RERKTJKaxK++XqBsNuh7tSzImXVr8EzyVB8hvgsDu7OpFjjOoWxld3jyIyyIu739vA4eJKZ5ckIiIi0qQUVkV8w+CyZ+HWJRDaHb76P5gzBtJXOLsykWNEBnrx4vQkKmvsPPDpFlraMA4RERGRxqSwKlKnYz/4zddw9etQXgBvToL5M6Ew09mViRzRJcyXeyf2YNH2w3y2cZ+zyxERERFpMgqrIvVZLND7KrhzHYz5E+z8Gp4bAN8+COX5zq5OBIDfjOjMwNgg/vrFNnUHFhERkTZLYVXkRNy9YewD5njWPtfAqhfgmX7mWq3V5c6uTto5VxcLj1/dV92BRUREpE1TWBU5lYAouPwFc+bg2BGw+O/mJEwpb4Ld5uzqpB2r3x340w3qDiwiIiJtj8KqSEN06AXXvW+OaQ2Ihi/vgZeGwfavQK1a4iRHuwNv5ZC6A4uIiEgbo7AqciZih8NN38G175rHH0yH1yZAxkrn1iXtUl134Cqbgwc+UXdgERERaVsUVkXOlMUCPS+B366CS5+Foix442KY9ys4tM3Z1Uk7U9cdePEOdQcWERGRtkVhVeRsuVphwAy4az2Mf8RsXX15BHx2BxRlO7s6aUfUHVhERETaIktL6zY2cOBAIzk52dlliJy58nxztuC1cwALxI+HbhOg+0Tw7+Ts6sSZDAMqCqDkIJQcgNJD5r6qBFys4OIGLq7ma1e32vesxx271p5nBf+OENYT3DyPPCItp5SLn1nGyPhQ5s4YiMViceIXFhERETk5i8WSYhjGwNOdZ22OYkTaBe9gmPgoDLkVVj5vrtG6c6H5WUQf6DYRul8EkUlm8JC2oaoECrOODaElB49upQeh5BDYq355rYsbOGzAWfzS0OIKYT0goi9E9KFLRB/+Mi6Ch77bz6cb9nFlUtQ5fzURERERZ1LLqkhTMQzI2QG7voFd30HWajAc4B1itrh2mwBdx4FXoLMrlVOpKoXCzHpbRu1We1xR8MtrPALALwL8OoBfR/Ct3ftFHN18I8z1fAEcDnDUmMHVYTOXRXLYjr5X/9heYz734GY4uMXcSg4ceXSOSzg/O2IYNHQMvrGJZpgNjDHHWouIiIi0AA1tWVVYFWku5fmQ+gPs+hb2fG+GHBcrxAyr7S58EYR2U6hoToZh/lyKs6FoHxTvOzaIFmZCed6x11g9zfB3ZIs11+P17/TLENpcSnPg0BY4sJnSjA0c3LWOLpYDuOAwP/cIgI59of910PdateyLiIiIUymsirRkDjtkrzOD665v4fBW8/3AWOhyPnQZA53HgE+oM6ts3QwDKovMAFq079hAWpRt7ov3g+24CYlcPY4No0GxR0NpYAz4hLX4XyjMXZbGEws28uIFnowLPGi2vmasNFv6Q7vD2Aeg5xRw0Rx7IiIi0vwUVkVak8Is2P0t7FkM6cuhqth8v0NvM7R2Hg1xI8DDz7l1tiS26qPB88iWdex71aXHXmNxMbvj+kdCQGTtPqrecZQZRlt5iLM7DK59ZRW7DpXw/e/H0MHf0wzv27+EHx81Q2tEHxj7F3MCsBYevkVERKRtUVgVaa3sNjiwEdJ+gr1LIHONOTmPxRUiBxxtdY0adMxssC2Ww2G2ahbvN8fsGgZHJhQ68vePcezr+p9Vlx4XSGu30kP8YmIin7CjAfT4EBoQaXbRdW0f88qddHZghx22fAQ//QsK9pr/HI37i9miLyIiItIMFFZF2oqaSshaYwbXtCWwf70Z+qyeEDPUDK4d+4G7L7h5gbsPuHmb4ybdvM2lT5qa3Wa2auan1W57j74uSD/xTLhnyup1NIQGREFAtBlA6177dzK/vxwxd1ka/1ywnSen9uOqAcfNDmyvgY3vwpLHzdbouFEw/mGIHuycYkVERKTdUFgVaasqi8zxh2lLzAB7eNupz3dxOxpc64dYN28z3Fk9zODr6m7u646tdce177l6mHsXqxlujgTTNCjIMGeqrWP1guAuENwZQrqarwOizGsBsNTrelq7t1iOe137mZuXGUa9g9Vd9QydsDvw8WoqIeVNWPYElOWYSyyNe9D8BYiIiIhIE1BYFWkvSg9D3h6oKYfqcqipgJqyE7wur3dO7Xu2KnOCIXvV0dd1+9Nx9zXDaHCXelttMPWLULBsIfbmlnHR7KWMiA/ltfrdgY9XXQZrXoEVz0BlIfSaAmMfNNdyFREREWlECqsicvYMw+wmWhde64dZezX4dQLfcAXSVqKuO/Dt53flDxN64Opyip9bZRGsesHcasrNpW4mPAo+Ic1XsIjbYDTAAAAgAElEQVSIiLRpCqsiIgKY3YHv/3gz81OyGd41hNm/6k+432km5yrLgxWzYc3L4BUMV76iSZhERESkUTQ0rLbu9RlEROS0XF0s/HdqPx6/ui8pGQVMfnY5K1NzT32RTwhM+Afc/AN4BsDbl8P3D5tLBomIiIg0A4VVEZF24pqB0Xx+5wj8PK38eu4anlu8G4fjNL1rIvrALT/BgJnmeNbXJ0BeajNUKyIiIu2dwqqISDuSEOHPF3eO5NJ+nXjy+13MeGMteaWnWVrI3RsunQ3XvmMuRfTyKNjwbr21cUVEREQan8KqiEg74+thZfa1/Xn0it6s2ZvP5GeXsy49//QX9rwUblsBkUnw+e3w0Y1QUdj0BYuIiEi7pAmWRETasZ/3FXHnvPVkFVTwxwk9uHV0F1xONVswgMMOy5+GHx8D/0i46lWIGdo8BTeSn/cV8fKSVAK83IgO9iY6yJuYYG+ig70I8HI7+RI/IiIics40G7CIiDRIcWUN93+8mYVbDjIuIZwnp/YjyMf99BdmJ8PHN0FhJoz5E4z6I7ham77gc/TV5v38cf4mPKyuWCxQWF5zzOd+Hlaigr2JCfYiOsib6OCjQTYqyBtPN1cnVS4iItI2KKyKiEiDGYbB26syeHTBdkJ93Xl+ehJJMUGnv7CyGBb+ETZ/ANFDzVbWwJimL/gsOBwGsxft4tkf9jAgNoiXfz2AMD8PiitryMovJyu/guyCcrLyy8nMLyeroIKs/HKqbI5j7hMb4s3kPh2Z0j+SHhF+Tvo2IiIirZfCqoiInLHN2YXcMW89Bworuf/iBG4a2blhXWI3fQAL/gAWF3Mypt5XNn2xZ6CsysbvP9zIt1sPMXVAFP+8ojce1tO3kBqGQU5p1ZEwm5VfzrqMAlbsycXuMEiI8GNK/0gu69+JyECvZvgmIiIirZ/CqoiInJWiihrunb+J77YdYkKvDjx5TT/8PN1Of2H+Xvh4FuxLNpe6mfw0uDh/Hr+s/HJufjuZXYdKeHByL24cEXfOY1JzSqpYuOUAn23cx4ZMc5KpwXHBTEnsxKTeHRvWjVpERKSdUlgVEZGzZhgGry3fy7+/3kFcqA9zbxhIXKjP6S+018Div8PKZ2HIbXDRv8GJkxWt3ZvPbe+kUGN38Px1SYzpHtboz8jMK+fzjfv4bOM+UnPKsLpYGNM9jCmJkVzQMxxv95Y/jldERKQ5KayKiMg5W5may+3vrscw4MXpSYyIDz39RYYB3z4Iq1+AC/4GI/+v6Qs9gffXZvLQ5z8THeTN3BkD6RLm26TPMwyDrfuL+WLTfr7YuJ+DxZV4u7sy8bwILuvfiZHxobi5Or+lWURExNkUVkVEpFFk5pndaPfklPLwJb24YVjs6bvROhzwySz4+WO4/GXoP615igVsdgf/XLCdN1emM7p7GM9NSyTAqwHdmBuR3WGwdm8+X2zax4LNByiutNE3KoA3Zg4ixNejWWsRERFpaRRWRUSk0ZRW2fi/9zeyaPshpg2O5m+X9cbdeppWQlsVvDsVMlbAtA+g2wVNXmdheTV3ztvA8j25zBrZmfsvTsDq5NbMKpudBZsP8OdPthAZ5MU7Nw2hkyZjEhGRdqyhYVX9kURE5LR8PazMuX4Ad4ztyntrs/j13DXklVad+iKrB1z7DoT3hA9vgH0pTVrjnsMlXP7CCtbuzefxq/vyl0t6OT2oAnhYXbkyKYp3Zg0hp6SKq19aSWpOqbPLEhERafGc/19xERFpFVxcLNw7MYFnftWfTdmFXPb8CrbtLz71RZ7+MP0j8AmBd6+BvNQmqe3HHYe54oWVlFbZeO+WIVwzMLpJnnMuBsUF8/4tQ6m2O5j68iq2ZBc5uyQREZEWTWFVRETOyJT+kcy/bRh2h8HVL6/km58PnvoCvwj49aeAAf+7AkoPn9PzK2vsZBeUsymrkB92HOLJ73Zy41vriA725vM7RzIgNvic7t+UzusUwPzbhuPl5sq0V1ezKjXP2SWJiIi0WBqzKiIiZ+VwcSW3/C+FjVmF/P7C7tw1Lv7UEy9lJ8Nbl0JoN5i5ADz8jnxkGAbZBRVkFZSTW1pNXmkVeaXV5JVVHTmu25dV239x68l9OvLfqX1bzTIxB4squf61NWTkl/PCdUlc2KuDs0sSERFpNppgSUREmlxljZ0HPtnCJxv2NSww7voO3vsVtthRrBn6Ehv2l7Exq5ANmYXklVUfc6qri4VgH3dCfNwJ9fUgxLfe3sfch/h6EObnQacAz9PPUNzCFJRVM/PNdfy8r4jHr+rLVQOinF2SiIhIs2hoWG0dv4IWEZEWydPNlSev6UdCRz/+9fUO0vPKmHPDQCLrzXZrszvYdaiUDVkFbMzsQLjHndyb/gyHU2fxZM1v6RLmx/k9wukfE0jXMB/CfD0I9fUgwMsNF5fWFUDPRJCPO/NmDeGW/yXzh/mbKKqo4caRnZ1dloiISIuhllUREWkUP+44zN3vbcDDzYXfX9iDjPwyNmQWsiW7iIoas+tusI87/aMDmWV8wvCMF6kafAcekx5zcuXOVWWzc897G/lm60HuHhfP7y7s3upaiUVERM6EugGLiEiz23O4hFlvJZOeV467qwu9OvnTPzqQxJhAEqODiA72MoOYYcDCe2HdqzDxMRh2h7NLdyqb3cGDn/7MB8lZ3DAslr9eel6bblV2OAxeWZrGkC7BJMUEObscERFpZuoGLCIizS4+3I+F94xib24Z8eG+eFhdT3yixQIX/wdKD8G3D4BvB+hzdfMW24JYXV3491V9CPR245WlaRSW1/DkNf1wawHrxDaFd9dk8J9vduDl5sqbvxnEkC4hzi5JRERaoLb5X0EREXEab3cr53UKOHlQrePiCle+CrEj4NPbIO2nZqmvpbJYLPx5Uk/+dFECX2zazy1vJ1NxgpmPW7v03DIeW7iDoV2CiQzyYuYb61iTpiV8RETkl9QNWEREnKuiEN64GAqzzNZWDz9wsZph1sUVLK71jq21x65Hj12s4OoOVo/azRNcPcCl9f4+dt6aTB78bAsDY4N46pr+RAd7O7ukRmF3GPxqzip2HCzh2/8bjdXVwnWvrmFfQYVaWEVE2hGNWRURkdajaB+8PhGKshrvnq7uZnC1epjhtS7I1u29gmDUHyBqQOM9sxEt2HyA//tgAzV2g4QIPy7o2YHxPcPpFxXYasezvro0jUcXbufJqf2OLNVzuKTySGB94zeDGKrAKiLS5jVqWLVYLBcBzwCuwFzDMP593OdPA2NrD72BcMMwAms/iwHmAtGAAUwyDCP9ZM9SWBURaaeqy6AgAww7OGzgcJj7I8d2czvmuHZvrwZbJdiqwF5l7uuOj+yPey9nJ5TnwtDbYewD4O7j7D+BX8jMK+fbrQdZtP0QyRkF2B0Gob4ejE8IZ3zPcEZ2Cz31urYtyO5DJUx+bjljuocx5/oBx8x4nFNSxbRXVyuwioi0E40WVi0WiyuwC7gQyAbWAdMMw9h2kvPvAhINw7ix9vgn4FHDML63WCy+gMMwjPKTPU9hVUREmkVlESz6KyS/DoGxcNmz0OV8Jxd1coXl1fy0M4dF2w+xZGcOJVU23K0ujOgawvjaVteOAV6nv5ET1NgdXPXSSrLyy/nud2MI8/P4xTkKrCIi7UdjhtVhwF8Nw5hYe/xnAMMw/nWS81cCj9SG017AHMMwRja0cIVVERFpVunL4Yu7IT8VEn8NE/5pdhFuwaptDtal57No+yEWbz9MZr75O+Dekf6MT+jA6O6huLm6UGVzUFXjoMpmN1/b7LXH5uvKmmPf6xjoyS2jumBt5FmIn128m6e+38WL05OY1KfjSc+rH1hfnzmIYV0VWEVE2qLGDKtXAxcZhjGr9vh6YIhhGHee4NxYYDUQZRiG3WKxXA7MAqqBzsAi4H7DMOzHXXcLcAtATEzMgIyMjAZ8RRERkUZSUwE//RtWPgc+oTDpCeh1mbOrahDDMNhzuJRF2w+zaPsh1mcWcCbTUVhdLHhYXXC3ulBQXsOkPhHMvjYRd2vjBNat+4uY8vwKJvXpyLPTEk97fk5JFde9uppsBVYRkTarMcPqVGDicWF1sGEYd53g3D9hBtW7ao+vBl4DEoFM4ANgoWEYr53seWpZFRERp9m/Eb64Ew5ugZ6XmaHVr4OzqzojeaVVpGQU4GKx4OnmioebCx5WFzysruberd5rq8sxraivLd/LP77axviEcF6YnoSn22mWHzqNKpudKc+vIK+smu9/N5pAb/cGXafAKiLStjU0rDbk16bZmJMj1YkC9p/k3F8B7x137QbDMNIMw7ABnwFJDXimiIhI8+vUH27+EcY/Aru+hRcGwYZ3OKOmSicL8fVgwnkRXNCrAyO7hTIoLpi+UYH0iPAjLtSHjgFeBPu44+Nh/UV335tGdubRK3qzeMdhZr2VTHm17ZxqeWbRbnYcLOE/V/VpcFAFCPPzYN7NQ4kK8uLGN9exKlXrsIqItEcNCavrgG4Wi6WzxWJxxwykXxx/ksVi6QEEAauOuzbIYrGE1R6PA044MZOIiEiL4OoGo34Pv10B4efB53fA/y6H/L3OrqxZTB8SyxNT+7EyNZeZr6+jtOrsAuv6zAJeXpLKNQOjGJdw5q3T9QPrb95cq8AqItIOnTas1raI3gl8C2wHPjQMY6vFYvm7xWKpP6BnGvC+Ua9fce3Y1D8Ciy0WyxbAArzamF9ARESkSYR2g5kLYPJTkJ0CLw2HVS+YS+W0cVcPiOLZaYmszyxg+tw1FJXXnNH1FdV2/vjhJjoGePHQJb3Ouo66wBod5K3AKiLSDjVondXmpDGrIiLS4hRlw1e/h93fQlhP6HGxucxN9BBw83R2dU3mu60HuXPeBuLDffnfTYMJ8f3lkjMn8vcvt/H6ir28O2sII+JDz7mO3NIqps1ZTVZBOW/MHKwxrCIirVyjTbDU3BRWRUSkRTIM+PljWDsHspPBsIPVE2KGmsG1y/kQ0Rdczm1SopZmya4cbnk7mZhgb96dNYRw/1OH81WpeUx7dTUzhsXytym9G62O+oF1/q3D6RMV0Gj3FhGR5qWwKiIi0lSqSiBjJaT9ZG6Ha6dj8AyEzqOhyxjoMhaCu4DF4sxKG8Wq1Dxuemsd4X4evHvzUCIDvU54XmmVjYtmL8XqYmHhPaPwdrc2ah25pVVc+txyPKwufHX3KHw9Gvf+IiLSPBRWRUREmkvJIdi79Gh4Lc423w+Ihs5jzFbX2OHg36nVhteUjAJmvrEWf0835t08hNgQn1+c8+dPtvDBukzm3zaMAbHBTVLHmjSz5fbyxEieuqZ/kzxDRESalsKqiIiIMxgG5KdB2o+QtsQMsZWF5me+HaBTEkQmHd17N02oawo/7yvi+tfW4G514d1ZQ4kP9z3y2ZJdOcx4fS23junCny/u2aR1PP39Lp5ZvJunr+3HFYlRTfosERFpfAqrIiIiLYHDDgc3Q9Za2Lce9q+H3F1HPw+MPTa8duwHHn7Oq/c0dh4sYfrcNRiGwTuzhtCzoz9F5TVMnL0Ufy8rX9w5Ek+3ph23a7M7mPbqarbtL2bhPaNO2MorIiItl8KqiIhIS1VZDAc2Hg2v+9ZDUVbthxYI63E0vHY531xGpwVJzSll+qtrqLTZefvGwby5Ip3PN+3ns9tHNNvER/sKK7h49lI6h/ow/7bhuFsbsnS8iIi0BAqrIiIirUlpztHgWrcvzzU/C+5qLpfT/SKIGQauzp9YKDOvnOvmria3tIrKGgf3jO/G7y7s3qw1fL3lAL99dz23jenK/RcnNOuzRUTk7CmsioiItGaGAQXpsGcR7Pwa0peBvRo8AyD+QjO8xl8AXoFOK3F/YQW/fm0Nfp5ufHTbMNxcm79188+fbOG9tZn876bBjOoW1uzPFxGRM6ewKiIi0pZUlUDqj7DrG9j1rdnq6mI1W1rrWl1DujZ7WTa7A4eB07rhVlTbufT55RRV1PD1PaMI9fVwSh0iItJwCqsiIiJtlcMO+1LMFtdd3xxd5zW0B/S4CLpfDNGDwaVpJzpqKbYfKGbKCysY3jWE12cMwsWldS4PJCLSXiisioiItBcF6bDzG9j1NaSvAEcNeAVDtwlmeO06Hjz9nV1lk3p7VToPf76Vhy7pxU0jOzu7HBEROQWFVRERkfaoshhSF5vhdfd3UJEPLm4QN8Jsce1xEQTFObvKRmcYBje/ncKSXYf59PYR9I5snlmJRUTkzCmsioiItHcOu7m+666vzfCau9N8P6zn0e7CUQPbTHfhgrJqLn5mGd7urnx510h8PJw/a7KIiPySwqqIiIgcKz/taHfhjJXgsIF3aL3uwuPAw8/ZVZ6TVal5XDd3NVcnRfHfqf2cXY6IiJyAwqqIiIicXEWhuSzOrm9g9/dQWWjOLhw1GLqMgS7nQ+QAcHVzdqVn7MnvdvLcD3t45lf9mdI/0tnliIjIcRRWRUREpGHsNshaY45x3bsE9m8EDHD3hdgRZnDtMgbCe4Gl5c+0a7M7uOaVVew+VMqCu0cRE+Lt7JJERKQehVURERE5O+X5kL4c0n4yw2veHvN9n3DoPPpoeA2McWKRp5aVX86kZ5fRNcyX+bcNw83VOevAiojILymsioiISOMozDJDa9oSM8CWHTbfD+5iBtfuF0P8+BY3UdOCzQe4Y956bj+/K/ddlODsckREpJbCqoiIiDQ+w4DD24+2uqYvh+pS8OsEidMh8dctammc+z/ezAfJWbxz0xBGxIc6uxwREUFhVURERJqDrdqcpGn92+aETRhma2vSDZBwCVg9nFpeebWNS59bTmmVjeV/GqfuwCIiLUBDw6r+xhYREZGzZ3WHXpfBrz+C3/0MYx+EvDT46EZ4sgd8fT8c2ua08rzdrfxhQg8OFVexObvQaXWIiMiZU1gVERGRxhEQBWPug3s2wfWfmi2s6+bCS8Pg1fGQ8hZUlTR7WcO7hmCxwPLdec3+bBEROXvqBiwiIiJNpywPNr9vdhPO2QFuPtD7Cki8wQy31aVmgK0qqfe6FKpL6r2ud46tCgIiIbirOcFTcBcI6QreIadcVuey55fjaXXlw9uGNeOXFxGRE2loN2BrcxQjIiIi7ZRPCAy7A4beDtnJsP4t+PkT2PDO6a91sZprvXr4g4ev+drV3bzP1k/BcBw918MfgjvXBtjjgqxPGMO7hjJ3WRplVTZ8PPS/PyIirYH+thYREZGmZ7FA9CBzu+hfsPNrqCmvDaN+R/cevuDuZ762epy8tdRWDYWZkJ8K+WnmlpcK+zfCti/AsB89192XO/y64ucSy67VNhJHTHT6xE8izWrl89DhPOg61tmViJwRdQMWERGRtsVeUxtk045sjv0bsWeuw81iBzdviBsJXcdBl7EQ1uOUXYhFWjWHAx7rCD0vg6tedXY1IoC6AYuIiEh75epmdv8N6XrkLRfgljmLiSpaz197HYK0H2H3d+aHfp3M4Np1rDkplI/WY5U2pCgLbJVQqdmwpfVRWBUREZF2Ial7LI9/U8kdo+8mbLIHFGSYoTX1R9jxFWysHUfbsZ/Z4tp1HMQMM5fnEWmt8nab+8oi59Yhcha0dI2IiIi0CyPjzRbTlam55htBsTBgJlzzFtyXBrN+gHF/McfPrnoe3r4M5o6DymLnFS1yrvJSzb3CqrRCCqsiIiLSLpzXKYAALzdW7Mn95YcurhA1AEbfC79ZCH9Kh8tfgsPbYf5McxysSGuUW9uyWqFuwNL6KKyKiIhIu+DqYmF41xCW787ltBNMevhB/+tg8lOQuhgW3gstbFJKkQZRN2BpxRRWRUREpN0YER/K/qJK0vPKG3bBgBkw8neQ8gasfLZpixNpCnXdgG0VYKtybi0iZ0hhVURERNqNunGry0/UFfhkxj0M510J3z8MWz9rospEmkB1uTkbsE+4eazWVWllFFZFRESk3YgN8SYy0IsVu88grLq4mONXo4fAp7dC1rqmK1CkMeXXtqpGDTL3CqvSyiisioiISLthsVgYGR/KytRc7I4zGIPq5gm/mgd+EfDeryB/b9MVKdJY6iZXihpg7jXJkrQyCqsiIiLSrozoFkpxpY2f951hK5NPKEz/CBw2mHcNVBQ0TYEijaVuvGqnJHOvllVpZRRWRUREpF0Z3jUEOMNxq3VCu5ktrPl74YPrwVbdyNWJNKK83eAfBf6dzONKtaxK66KwKiIiIu1KqK8HPTv6n3i91YaIGwFTXoD0ZfDlPVrSpo1IyShg+e5ciivb0Jq6ubshpCt4BprHCqvSylidXYCIiIhIcxsZH8JbKzOoqLbj5e565jfody0UpMNPj0FwZxhzX6PXKM3nYFEl0+asptruwGKB+DBf+kcHkhgTRP/oQLp38MXq2sraeAwD8vZA32vAM8B8T2NWpZVRWBUREZF2Z3h8KK8u20tyRj6juoWd3U3G3AcFe+HHRyEozgwF0iq9vCQVh2Hw3LRE0nPL2JBVyKLth5ifkg2At7srfSIDjoTXxJhAOvh7Ornq0yjLgapiCOlmThBm9dSYVWl1FFZFRESk3RkcF4ybq4UVe/LOPqxaLHDps1CUDZ/fAQFREDu8cQuVJne4pJL31mZyZVIkl/brdOR9wzDIzC9nQ2YhG7MK2ZBZwGvL06ixm92+OwV40j8mkMToICaeF0FMiLezvsKJ1c0EHBpv7j0DFFal1VFYFRERkXbHx8NKYkzQ2Y9brWN1h2v/B3MvhPevg5sWHQ0H0iq8ujQNm8PgjrHH/twsFguxIT7EhvhweWIkAJU1drbuLz4SXjdmFbJwy0Gig71aXljNqw2rIXVhNVBjVqXVUVgVERGRdmlkfChPL9pFQVk1QT7uZ38jryCYPh/mXgDzppqB1Sek8QqVJpNbWsU7qzOZ0q8TsSE+pz3f082VAbFBDIgNAjoDkFNShY/HWYx7bmp5e8DVAwKizWO1rEor1MpGiouIiIg0jhHxoRgGrErLO/ebBXeGae9D8X6zhbWm8tzvKU1u7rK9VNrs3DHu7FvDw/w88HZvge0/uXvMmYBdaoO0Z4AmWJJWR2FVRERE2qV+UQH4eljPbr3VE4keBFe8Almr4Zv7G+ee0mQKyqp5e1U6l/btRNcwX2eX0/jyapetqeMVqJZVaXUUVkVERKRdsrq6MLRLyLmPW63vvMth+F2Q8gak/tB495VG9/qKvVTU2LnzHFpVWyx7jbm0Uki3o++pG7C0QgqrIiIi0m6NjA8hI6+crPzyxrvp2AfNkPD5XVBZ3Hj3lUZTVF7DmyvSubh3BN07+Dm7nMZXkAEOG4TWD6u1LauG4by6RM6QwqqIiIi0WyO7hQI0buuqmxdc/hKU7IfvH2q8+0qjeWPlXkqqbNw5ttvpT26NjswEfFzLqmGH6lLn1CRyFhRWRUREpN3qGuZLB3+Pxhu3Wid6EAy7E1LeVHfgFqaksobXl+9lQq8O9Ork7+xymkbdGqv1x6x6Bph7TbIkrYjCqoiIiLRbFouFEfGhrEzNw+Fo5O6RYx+E0O7qDtzCvL0qg+JKG3eNa6OtqmC2rHqHgHfw0fe8As29xq1KK6KwKiIiIu3ayPhQ8suq2X6wkQOlmydMedHsDvzdXxr33nJWSqtsvLosjXEJ4fSJCnB2OU0nL/XYLsBwtGW1Ui2r0noorIqIiEi7NiK+Ccat1qnrDrz+LdizuPHvL2fkndUZFJbXcFdbnAG4vtzdEHrcd/RUy6q0PgqrIiIi0q518PekW7gvK/bkNc0D6roDf3G3ugM7UUW1nVeXpjG6exiJMUHOLqfpVBZB2WEIOT6sBhz9XKSVUFgVERGRdm9EfChr9+ZTZbM3/s3dPI/ODqzuwE7z7poM8sqqubutt6rm7TH3x3cDrhuzqgmWpBVRWBUREZF2b0R8KBU1djZkNtH/yEcNhOF3qTuwk1TW2HllaRrDu4YwMC749Be0Zrm1YTX0uLDqUTvzsVpWpRVRWBUREZF2b0iXYFxdLE0zbrXO+Q+oO7CTvL82k5ySKu4e34ZnAK6TtxssLhAUd+z7Lq5mYNUES9KKKKyKiIhIu+fv6Ua/qIDGX2+1PnUHdooqm52Xl6QxuHMwQ7uEOLucppe7GwJjwerxy888A9WyKq2KwqqIiIgI5hI2m7IKKa6sabqHqDtws5ufnM3B4krubsvrqtaXl/rLLsB1PAM0ZlVaFYVVEREREcxxqw4DVqc20azAdY7pDqxWrqZUbXPw0k+pJMUEMiK+HbSqOhzmBEvHT65Ux0stq9K6KKyKiIiIAIkxQXi5uf5/e/cdX3V593/8dWWeJGSQsEcYCcMBgiIguHHrrbZVHGitddTb2d523W3vbn93e7d2qt21dQ9cVLAu3AIiIoRNQEgCBEJCwgqZ1++P6xwSIOOckzM57+fjkcc353u+4xK+mHzO53N9rvDOW4XDyoH/J7z3SnDPf1LBltp67p4xCmNMtIcTfru3QHM9FBR1/L4nV8GqxBUFqyIiIiJAWkoSU0bmh3feqo/KgcOuqaWVB98uZfyQXM4Y3Tfaw4mM6k46Aft4ctVgSeKKglURERERr1OL+7Chah/b6urDf7MzvwN9xqgcOExe+nQr5TX13H12gmRVofM1Vn3UYEnijIJVEREREa/pxX0A+KA0zPNWQd2Bw6i5pZUH3yrl2IE5zDimX7SHEzk710NaL8ge0PH7nlxo3AstYWwidjSrWgePXQENe6M9koShYFVERETEa0z/bPr0Sgv/vFWfISfBtLvhk0dUDhxCLy/fxmc79yXOXFWf6vVuvmpn/80ZeW6rdX6Ds3oOlL4O25ZFeyQJQ8GqiIiIiFdSkmFaUR/eL92JtTYyNz3zv9uVAyuI6KmWVssDb5Uypn825x3bP9rDiayuOgGDy6yC5q0Gq7LEbWs2RHccCUTBqoiIiEg7pxb3oWpPA6U7IlTql+qByx+C3RXw4e8jc8+jVN3+JjOsTUcAACAASURBVB7+4DNKd+zlrhnFJCUlUFa1qR5qyztvrgRuziooWA2WL1j1zQ2WsEuJ9gBEREREYsn0UW7e6vulOxnVPzsyNx0yCY77HCx8CKZ8BbL6ROa+cayppZW1lXtYWraLpeW1fFpey8aqfQAcPziHC48fGOURRljNRsBCQXHnxxzMrKrJUsAa9nr/jIFqZVYjRcGqiIiISDuD8zIY0SeLD0p3cuP0EZG78Zn/Dategg9+A+f9NHL3jQPWWrbVHWBpWS2flu/i0/JaSrbUcaCpFYA+vdKZWJjHF04cwsTCPE4s7E1yImVVwTVXAv+C1XplVgO2YxVgITVTwWoEKVgVEREROcz04gJeXLqVppZWUpMjNGuq7xgYfxV89Bc45c7OO7omiANNLTyyYBNLNu9iaVktO/Y0AG493OMH5TBryjAmDM1jYmEeg/MyEquRUkcOLlvTRbB6sMGSMqsBq1zutqPPhzXzoLUVkjSjMtwUrIqIiIgc5tTiPjy2sIzlFbWcNCw/cjc+45tQ8iy8dz9c9IvI3TcG/X7+eh58awPDCzKZVlTAxMLeTBiaxzEDc0hLUZBwhOpSyB4E6b06P0YNloJXWeLm/I44A1a+4OaY5xVGe1RHPb+CVWPMBcBvgWTgr9banx32/q+Bs7wvM4F+1tq8du/nAKuBF6y1d4Zi4CIiIiLhMnVkAcbA++urIxus5o+EidfBxw/DtLsS9pdhay0vL9/GaaP68OhNU6I9nPiw07tsTVdSMyEpVZnVYFSWwIBxbZnr6tKE/fcZSd1+LGWMSQYeBC4EjgWuMcYc2/4Ya+3XrLUTrLUTgN8Dzx92mZ8A74RmyCIiIiLhlZeZxvjBuby8fCuNza2Rvfnp33DrZL6buJnVlVt3s7l6P5eMT7AmScGy1q2x2lUnYHDPlSdXwWqgWltg+yoYML7tAwHNW40If2ooJgOl1tqN1tpG4Cngsi6OvwZ40vfCGHMS0B94rScDFREREYmkO84qZv2OvTzwVoSXqcgdApO+DEsfT9hfiOeWbCM5yXDesYk9b9dv+6tdANrVGqs+nlw1WApU9QZorneZ1eyBarIUQf4Eq4OB8navK7z7jmCMGQaMAOZ7XycB9wPf6OoGxphbjTEfG2M+rqqq8mfcIiIiImF13nED+PzEwTz4ViklFRHORJ36X5CcBu/8PLL3jQHWWuYu38b04j70zkqL9nDig68TcHeZVXBNlpRZDYyvudKA4112Or8IahSsRoI/wWpHrdVsJ8deDcy21rZ4X98OzLPWlndyvLuYtX+21k6y1k7q27evH0MSERERCb8f/Mdx9OmVxn898ykHmlq6PyFUsvvDlFth+TOwY03k7hsDVm7dTVnNfi4ep6yq36p9y9Z0M2cVvGXAyqwGpLLEzfXtM8a9Lihq674sYeVPsFoBDG33egiwtZNjr6ZdCTBwCnCnMWYT8Evgi8aYn3V0ooiIiEisyc1M5edfGM/6HXv59RvrInvz6V+FtF7w9v+L7H2j7OXl20hRCXBgqktdJj5vWPfHepRZDVhlCfQbCyneTH9BMezaDC1N0R1XAvAnWF0MjDLGjDDGpOEC0jmHH2SMGQP0Bhb49llrZ1lrC621w4GvA49Ya78dkpGLiIiIRMCZY/pxzeSh/OXdjSzZvCtyN87Mh1Nuh1UvwbZlkbtvFFlrmVuyVSXAgdpZ6jpJJyV3f6waLAWussQ1V/IpKALb4gJWCatug1VrbTNwJ/AqbvmZZ6y1K40xPzbGXNru0GuAp6y1nZUIi4iIiMSl7158LANzM/j6s8uob4xgOfApd7hM2FuJkV1dsWU35TX1XDxOXYADUr2+bUmV7mTkuQZL+pXdP3u2w74d0P/4tn3tl6+RsPJrRWVr7Txr7WhrbZG19j7vvu9ba+e0O+aHXWVNrbX/0BqrIiIiEo96pafwiyvH89nOffzfqxGcQ+rJhen3wLp/Q/niyN03Sl4u2epKgI/rH+2hxI+WZqj5zP9g1ZMLrU3QVB/ecR0ttpe47YBxbfvyvXOD1WQp7PwKVkVEREQS3bSiPtxwyjAe/mATCzZUR+7GU74CWX1h/k8id88osNYyr8R1Ac7LVAmw32o3u+DTn07A4IJVUJMlf1X6gtV2mdXMfFfxoMxq2ClYFREREfHTty4cy/CCTL4xexl7G5ojc9O0LLeUzWfvwGfvRuaeUVCypc6VAI9XCXBAfAGTP2usgguyQPNW/VVZArmFkNG7bZ8x3o7AyqyGm4JVERERET9lpqXwyytPYEttPf9v3urI3XjSlyF7EMy/76idazh3+TZSkw3nqwtwYHxrrAZSBgxu3qp0r7Lk0BJgn4JiBasRoGBVREREJACThudz86kjeGJRGe+uq4rMTVM9cPrXoXwhlL4ZmXtGkOsC7EqAczNToz2c+FJd6rJ+WQX+HZ+hzKrfGve7P9/2JcA++UWwu0Jzf8NMwaqIiIhIgO49bwzF/XrxreeWU1cfobUWJ14PeYVu7upRll1dXlFHxS51AQ5Kdan/JcCgMuBA7FgNtrWTzKqvydLGyI4pwShYFREREQmQJzWZ+688gR17GvjJy6sic9OUNDjj27DtU1gzNzL3jJB5Ja4E+DyVAAdu53r/mytBu2BVZcDdqlzutp2VAYNKgcNMwaqIiIhIEE4Ymsd/nlHE7CUVvLFqe2RuOv4q90vyW/dBa2tk7hlm1lpeXr6NU1UCHLgDu2FvZVuWzx+eHO+5yqx2q7IE0nMgb9iR7/n+zNUROKwUrIqIiIgE6e4Zoxg7IJtvP1/Crn2N4b9hcgqc+d+wYxWsfD7894uAZRV1bKmt5+Lxg6I9lPjjW+czkDLg5FRIzVKDJX/4misZc+R76dnQq7/WWg0zBasiIiIiQUpLSeL+mSdQu7+R789ZGZmbHvd56HccvP2/0BKh5XPCyFcCfO6x/aM9lPiz05vVC6QMGFyTJWVWu9baCttXQv8Omiv55Gv5mnBTsCoiIiLSA8cNyuXuGaP417KtzCvZFv4bJiXB2d915YfLnw7//cLIWsvc5ds4bVRfcjNUAhyw6vWAgd4jAjvPk6s5q93Z9Rk07et4vqpPQZHKgMNMwaqIiIhID/3nmUWMG5zL915cwc69DeG/4ZiLYNBEeOdn0ByB8uMw8ZUAX6QuwMHZud51iE71BHaeR5nVbnXVXMmnoAj2VenPMowUrIqIiIj0UGqyKwfe29DMd18owYZ7aRlj4OzvQW0ZLH00vPcKo7nLt6oEuCeqSwMvAQZlVv1RWQJJKdB3bOfHqCNw2ClYFREREQmB0f2zuffc0by6cjsvL49AOXDRDCg8Bd79RVw2y7HWMq+kktNVAhwca12QFEhzJR9PLtQrG9ilyhLoM6brrHW+1loNNwWrIiIiIiFy82kjGd2/F39+NwK/vBoD593nyhBfuC3ulrL5tLxWJcA9sXurm1MZyLI1Pmqw1L3KFTCgi+ZKAPkjAKN5q2GkYFVEREQkRJKTDNdNHUbJljqWV0Qg2znkJBewrnsFPvhN+O8XQnOXbyMtOYlzVAIcnOogOwGDy6w21EFrS2jHdLTYtxP2bO16vipAagbkDlEZcBgpWBUREREJocsnDiYjNZnHF5ZF5oZTvuKWs5n/E9j4TmTu2UOuBHgbp43qoxLgYFWvd9ugyoDz3LZhd+jGczSpLHHb7oJVUEfgMFOwKiIiIhJCOZ5ULj1hEHOWbWX3gabw39AYuPT3LmiZ/WWo2xL+e/bQ0vJattYd4OLxKgEO2s5SSM2EnEGBn+vJdVuVAnfMF6z29ydYLYaaDW4OsYScglURERGREJs1tZD6phZeXBqhwDG9F1z1GDQfgGe/FPPL2agEOASq17usnjGBn5vhzazGYWOuiKgsgZzBkFXQ/bH5RS7o318d/nElIAWrIiIiIiE2fkge4wbn8vjCsvAvY+PTd7TLsFZ8BK//T2TuGYTWVssrJds4fXQfcjwqAQ5adWlwJcCgzGp3tq+A/t00V/I5uHyNSoHDQcGqiIiISBhcO6WQtdv38EnZrsjd9PjPw9TbYdEfoWR25O4bAJUAh0Bzg1tjN5jmStAuWFVm9QhNB6BqrX/zVaGtG7OaLIVFSrQHICIiInI0uvSEQdw3dzWPLyzjpGH5kbvxuT+GLZ/AnLtddqjf2Mjd2w/zSlwJ8IxjjoIS4NYWWPEc7N0R+LnZA+D4LwRXxluzEWxrW1YvUL4GS8qsHqlqNdgW/4PVvEJISlFmNUwUrIqIiIiEQVZ6Cp+bOJinPy7nfy45lt5ZaZG5cXIqXPkw/Ol0eOZ6uGU+pGdH5t7daG11XYBPH9336CgB/vB38MYPgz9/x2qYEUTJti8wCjpY9WZWNWf1SIF0Agb37y1vmGuyJCGnYFVEREQkTK6dUsijCzfz3CcV3HzayMjdOGcQXPF3eOQymHMXXPFwcBm8EFtavottdQf41gWxle0NyrZlMP8+OPYyuPSBwM9/7Xvw3i/dOp2Tbgzs3J2+ZWuCDFbTs8EkKbPakcoSSOsFvUf4f05BscqAw0RzVkVERETC5JiBOZxYmMcTiyLYaMlnxOkw4wew8gVY+IfI3rsTc5dXkpaSxIxj+kV7KD3TVA/P3wqZBXDJb8CTE/jXxb+C4nNg7r2w7rXA7l9dCr0GuOsEwxiXXVWweqTKFdD/OEgKIEwqKHKl2a2t4RtXglKwKiIiIhJGs6YMY+POfSzYGIWlLabfA2Mvcd2BNy+I/P3b8ZUAnzG6L9nxXgL8xo+gag1c/hBkBjkfOTkFrvyHC4ye/RJsXer/uTvXB59V9fHkqsHS4VpbXWbV3xJgn4IiaNoPe7aFZ1wJTMGqiIiISBhdPH4guRmpPLGoLCzXb27pIptjjAuo8gpdQBRMI6AQ+aRsF5W7D3DxuDjvArxhPiz6A0z+ChTP6Nm10rNh1rMuQ/v4TNi12b/zqkuhT0+D1TxlVg9Xuxka9wQRrHr/LjRvNeQUrIqIiIiEkSc1mS+cOIRXV1ZStachpNd+8K1STvnZfHbsOdDFAHJh5qMuMJn9ZWhpDukY/DW3ZFv8lwDvr4EXb4c+Y+DcH4XmmtkDXMDa0gCPXwH13Sx1tL8G6muCX2PVx5N7dDRYmv9T+PTJ0Fwr0OZKPvm+5WvUETjUFKyKiIiIhNm1UwpparE8u6Q8ZNdcVl7Lr15fR9WeBv7wdjcZnQHHwyW/hk3vwfyfhGwM/vKVAJ8ZzyXA1sLc/4J9VfD5P0NqRuiu3W8sXP0E7NoET81y66h2xtdcKdg1Vn0yjpLM6qI/wWvfdfOIe6qyxDWe6ndsYOflDIYUj5oshYGCVREREZEwK+7Xi6kj83nyozJaW3veaOlAUwv3PruMvr3SuXjcQB5fVMa2um5+WZ9wDZx0I3zwG1j9co/HEIhPynaxfXcDF4+P4xLg5c+4ZlVnfQcGTQj99YefCpf/ATZ/AC/+Z+fNeqp72AnY52hosFRfCw27YX81LAtBdnX7CpexDvSDiKQkyB+pYDUMFKyKiIiIRMC1U4ZRXlPPe6U7e3ytX72+jtIde/n5FeP59oVjsdby0Ft+/KJ84c9h0EQXDEXwF+uXl/tKgPtH7J4hVVsG874OhafA9K+G7z7jroBzfgQrnoM3f9jxMdWlkORd27MnPHnx32Cp1jsPPCkFFjzY8268wTRX8ikoUhlwGChYFREREYmA84/rT0FWGo8v9LOJTicWb6rhL+9t5NophZwxui9D8zOZOWkoTy0uo2LX/q5PTkmHmY9AUjI8cRXsqezRWPzR2mp5ZYUrAe6VnhL2+4Vcawu8cJsrA/7cH92fXThNvwcm3QQf/BY++suR7+9cD/kjXDfhnvDkQvMBaOpivnOsq/OW1U/+igsU178a/LX217jrBRus5he5Mu4ozQk/WilYFREREYmA9JRkrpw0lDfX7KCyLrgAYX9jM19/dhlDemfwnYuOObj/jrOKMRgemO9HZiev0M2P3L0VHr4I6rYENRZ/LdxYHd8lwB/+3pXmXvhz6D08/PczBi78Pxh9IbzyTVgz79D3q0t7XgIMLliF+C4FrvUGq9Pugpwh8OEDwV9r+wq3DTqzWgytTVAXnq7fiUrBqoiIiEiEXDu5kJZWy9OLg2u09LNX1rC5ej+/uOKEQ7KUg/IyuHZKIc8uqWBz9b7uLzRsGlz/glvK5h8XtZVThsFjizaTl5nK+ccNCNs9wmbbctdt9pj/gAnXRu6+ySlwxd9g4ATXwbliidvf2gI1G0MTrGb0dtu4DlbLICXDdVSeehtsfj+w9WrbC7YTsE+BryPwxuDO78j616FsYeiuF4cUrIqIiIhESGFBJqeN6sNTi8u6Xh+1Ax+U7uSRBZv58vQRTB1ZcMT7t59ZREqS4Xdv+jlvrnAKfPEl2L8LHr4Yaj4LaDz+2L77AK+u3M7MSUPxpIa5fDbUmg7A87dCZj5c8luX8YyktCy49mno1Q+emOn+fmrLoKWx552AoV1mNY7nrdaVQd5Q93dz4hchLTv47GrlCujV3/15B8P3AUKo5q02N7i1kf95KXz2XmiuGYcUrIqIiIhE0Kwpw9hWd4C311b5fc6eA018c/ZyRvbJ4psXjOnwmH45Hq6fOowXllawsWqvfxcechLcMAca98A/Lg5506WnPiqnpdUya0phSK8bEW/+CKpWw2UPQdaRHw5ERK9+cN1zYFvcGqzlH7n9PV1jFVyDJYjzzGo55A5133ty4aQbXMfm2iAqF3rSXAkgqy+k50BNiP4NffYeNO51nYmfvAa2fBKa68YZBasiIiIiETTjmH70z0nn8UX+N1r66cur2VZXzy9nntBlhvK2M4tIT0nmt2+u939AgybADf9yzXYevgiq1vl/bheaWlp54qPNnD66L8MKskJyzYjZ8BYsfAhOvgVGnRPdsfQZBdc85QKwf93t9mnOqlNb5uZg+0y5zW0X/TGw6zQ3QtWangWrxniXrwlRZnXtXEjNglvfhsze7sOKEP3bjCcKVkVEREQiKDU5iasmDeXtdVWU13TTvReYv2Y7T39czm1nFHFiYe8uj+3TK50bpg1nzrKtrNu+x/9BDRgHX5oLttXNYd2+yv9zO/Hm6u1s393A9VN7uMRKpO2vgRdvhz6j4dwfR3s0TuFU+PyfXWmoJxey+vT8mhnezGr9rp5fKxoa90F9jSsD9skbCsddDp88Agd2+3+tqjWuOVJPglVwHyKEojqhtRXWvgLFZ7vOz9e/CCYJHr08uKxxHFOwKiIiIhJhV00uxEC3jZZq9zfyredKGDsgm3vO8a/08yunjyQrLYXfvhFAdhWg3zEuYDXJ8M9L2hrOBOmxhWUMzsvg7LFBzgGMBmth7r2wb4cLDtMyoz2iNsddDpc9CKd+LTTzZ9Nz3DZeM6u+oC33sBLzU+6Eht0uYPXXweZK43s2poIil+1tbujZdbYthT3bYMzFbde97nlo2AOPfg729Xyt5nihYFVEREQkwnxB3FOLy2nqotHSD+asZNe+Rn555Qmkp/jXoKh3Vhpfnj6cuSXbWLU1gOwSQN/RcOM8SPHAPy4JurPqhqq9vF+6k2smDyU5KcKNiXqi5FlY+Tyc+W0YNDHaoznSxFkuWA2FVI/7e47XBku+DtZ5hwWrg0+EYdNdKbC/a55uX+G6CueP7NmYCooB2/NmZWvmuQ+NRp/ftm/geNdwq64cHvt8YJnjOKZgVURERCQKZk0Zxs69Dby+anuH779Sso2XPt3KXWeP4vjBuQFd+6ZTR5LtSeHXbwQxx62gyAWs6Tnwz8ug4uOAL/H4wjJSkw0zTx7a/cGxorYc5n4dhk6B6SEKCGOdJy9+M6u+9UzzOnjGTrnTBXWrXvTvWpUl0P84SOphx+p87/I1PW2ytGYuFJ7iOlG3N2wazHwEtq+Ep651HauPcgpWRURERKLg9NF9GZyXwROLjlzjdOfeBr774grGDc7l9rOKAr52bmYqt5w2ktdXbaekIohgpPdwuHGua+zyyOUBrfVY39jC7CXlXHD8QPplewK/dzQcqIPnbnJddz/3J7fOaSLw5MZvsFpbDkmp0KuD9XtHX+CynAsecKXdXbEWKpf3fL4qQIE3M9uTJks1G10X6rEXdfz+6PPh8j/Cpvdh9o3+Z4/jlIJVERERkShITjJcM3ko75fu5LOd+w7ut9bynedL2NvQzP0zTyA1Obhf126cPpy8zFR+9fra4AaYVwg3vgLZ/eHRz7tfjv3wr2Vb2X2gmeviZbmamo3w13NhyxK49HeuoU2iyMiD+jguA84dAkkd/PtISoKpt7sy9rIFXV+nrtwF7KEIVjN6Q2ZBz5osrZnntmM6CVYBxl8JF/0C1s6DOXe5hkxHKQWrIiIiIlEyc9JQUpIMT37Ull198dMtvLZqO/eeO5rR/bODvna2J5VbTx/JW2urWLI5yI6vOYNc06XcIfDYFbDx7W5PeWzRZkb378XkEfndHht1mz6Av8yAvdvh+hfg+C9Ee0SRFc+Z1bryjkuAfU64BjLy4cMHur5OqJor+fS0I/DaedDvuO4/NJl8C5z5HVj2BLz2ve4zyHFKwaqIiIhIlPTL8XDusf159uNyGppbqKw7wA9eWslJw3pz82k9bPYC3HDKcAqy0vhNMHNXfbIHuIA1fyQ8cRVsmN/pocvKa1leUcf1U4dhQtGxNpyWPgaPXObmBd4yH0acHu0RRZ4nN74bLB3eCbi9tEw4+WYX/HUVPFauAAz0PzY048ovCn7O6r5qlwnurAT4cGd8060tu/BBeO+Xwd0zxilYFREREYmiWVOGsWt/E6+UVPKt55bT1GK5/8oTQtJFNys9hdvOKOK99Tv56LOa4C/Uqy/c8C+XNXr6eti2rMPDHl24mcy0ZC6fODj4e4Vba4vLRL10BwyfDje/4ZpKJaJ4bbDUdMBlww/vBHy4k2+G5FRY8GDnx1Qud3//aVmhGVtBkVt2pmFv4Oeuf9WtddxVCXB7xsD5/wvjr4L5P4XFfw38njFOwaqIiIhIFE0rKmB4QSY/mLOSd9ZV8e0LxzK8T4h+cQaumzqMvtnp3P/aWmxPSgWzCmDWsy4b9/jMtnUuvWr3N/KvZVv53MTBZHtSezjqMGnYA09fBx/+3gUys2a7eYaJylcGHG9zHndvcduuyoDBzbcePxM+fQL2d/JhTWVJaOar+hQUu23NxsDPXTMXsgcFtmxSUpJbf3f0ha6bdcnswO8bwxSsioiIiERRUpLh2imF1NU3Ma2ogOunDgvp9TPSkrn9zCIWfVbDgg3VPbtYziAX4DXVw+NXHtKcZ/aSChqaW7kuxOMPmdoy+PsFsO7fcOEv4OL7XdYtkWXkuUxeYxBZwGiq3ey23WVWwS1j01wPi/925Hv1te5aIQ1WvVn6QDsCN9W7EvsxF7qMaSCSU+HKh93SNi98Bda/Edj5MUzBqoiIiEiUXT25kFtOG8H9M08gKQTlv4e7ZnIhA3M93P/6up5lV8HN7bvqUffL+NPXQXMDra2WxxZuZtKw3hwzMCc0gw6l8o/gL2e7gHXWbJhya7RHFBs83vV7460U2JfVz/VjHd9+x0DxOfDRn49cl3T7SrcNVXMlcHO7IfB5qxvfgab9/s9XPVxqBlzzJPQ71v27LFsU3HVijIJVERERkSjL8aTy3YuPZWBuRliu70lN5o6zilmyeRfvrKvq+QVHnuFKDze9By/dyQelVWyq3s/1p8RgVnX5M/CPS9ycxJvfgOIZ0R5R7PDkuW28NVmqKweT5DL9/jjlTti3A0qePXT/9hVu2//40I0tLcuV8gbaEXjtXEjLhuGnBX9vTy5c97wLmPf3sIoiRihYFREREUkAMycNZXBeBr8ORXYV4ISr4Oz/gZJn2DPv+xRkpXHB8QN6ft1QaW11TWeevwWGTIJb3oK+Y6I9qtgSt5nVMsgZ7H8Z98gzXUC64MFDl3ipXA6ZfVzH61AqKAqsDLi1Bda+AqPOhZT0nt27V1+47b3gM7QxRsGqiIiISAJIS0ni7hnFLKuo483VO0Jz0dPuZf/x13FR7ZPcV7iY9JTk0Fy3pxr3w+wvwbu/gInXwfUvuiVq5FC+YLU+zjKrteX+lQD7GOOyq1WrofTNtv2+5kqhXmapoCiwzGrFx7CvCsZeHJr7J8XIv8MQULAqIiIikiA+f+IQCvMz+VWosqvG8Kfs25nfMoHzN/0C1r3a82v21O6t8PCFsGoOnHcfXPoApKRFe1SxKcNXBhxnmdW68u47AR/u+C9A9kBY8Hv3uqUJdqwObXMln4JiqK/pvAPx4dbOhaQUN7dWDqFgVURERCRBpCYncc+MUazatptXV1b2+HpNLa088fE2nh3xE8yAcfDsl2DLJz0faLCq1sFfz3UlmNc8BdPuDH3W7GgSj2XALU1u6Rp/OgG3l5IGk2+FjW+7jOrOddDSGNrmSj753o7A/i5fs2YeDD+17cMDOUjBqoiIiEgCuWzCIEb2zeL7L61keUXPyj9fW7mdqj0NzJw2Fq591s3/e+Iq2LUpNIMNRMUS+Pv5LgC5cR6MuSDyY4g36bmAia8GS7u3uuV2AikD9pl0I6Rmubmrld7mSgNC2FzJx7fWqj/zVneuh+r1MCZEJcBHGQWrIiIiIgkkJTmJB689kdTkJK784wJeXLol6Gs9unATQ/MzOH10X8juD9fNdsHi41f6XwIZChvmwz//A9Kz4aZXYeAJkbt3PEtKgvSc+Mqs1pa5baBlwAAZvd0c5pLZsP41SE6HglGhHR9A7+GuW7E/81bXzHXbMReGfhxHAQWrIiIiIgnmmIE5zLlzOicMzeOrT3/K/85bTUtrYHNY12/fw8KNNVw7eRjJvrVh+46Bq59wmdWnZh25rmU4rHgOHp8J+SPgptfa1rkU/3hy46vBUp13jdW8IJdJmvqfYFtgxWy3ZnBySujG5pOS5sqU/cmsrp3n2ySDvgAAGClJREFUSpGDCb4TgIJVERERkQRU0Cudx26awnVTC/nTuxv58j8WU1ff5Pf5jy8qIy05iZmThhz6xvDpcPkfoOxDePE2t4RMuHz0F5h9Eww5Gb40N/RLkCSCjNw4y6x6g9WcwcGdnz8Cxl7ivg9HcyWfgmKo6SazuncHlH8Uui7ARyEFqyIiIiIJKi0liZ9ePo77Pnc8H5Tu5HMPfkDpjr3dnrevoZnnllRw0bgBFPTqYF3IcVfAuT+GlS/AGz8I/cCthbf+F+Z93ZVPXv+8mtMEy5MXX3NWa8ug1wBI9QR/jWl3ue2giaEZU0fyvcvXdNV1e92/AatgtQsKVkVEREQS3Kwpw3jilqnU1TfxuQc/YP6a7V0eP2fZVvY0NHP9KV2UYk67G06+BT78ncuAhkpriwtS3/kZTJgFMx+F1IzQXT/ReOIss1pX1vOS2aGT4eb57vkJl4JiaNwLe7v4t7RmHuQWQv8wNHk6SihYFREREREmj8hnzl2nUliQyU3//JiH3i7tcC1Way2PLtjMMQNzOLGwd+cXNAYu/DmMuQjmfQOeuxk2vtOzsuDmBnjuJlj8V5h+D1z2YHjmHCYST158Bau15cF1Aj7ckJMgpYOqgFAp8M6d7qzJUuM+2PgWjL1Iyyt1QcGqiIiIiAAwOC+D2bdN4+JxA/m/f6/lnqc+pb6x5ZBjPimrZdW23Vw3tRDT3S/ZScnwhb/BlK+47quPXAq/mwDv/F/b3EN/NeyBJ2a60uJzf+LKjPVLfs9l5MVPg6XWVqirCHyN1WjobvmaDfOh+YD7MEc6pWBVRERERA7KSEvm99dM5Bvnj+Ffy7dy5Z8+ZEtt/cH3H1+4mV7pKVw+wc8GN2mZLsN671oXuPYeDm/dB78ZB49+HlY87zKmXdm3E/55KXz2Hlz2EEy/O/j/QDmUJxea9kGL/821omZvJbQ2xUfn3NyhkJzWeZOlNfPcn/2waZEdV5xRsCoiIiIihzDGcMdZxfz1i5PYtHM/lz3wPos31VCzr5GXl2/jCycOJis9wPLb1AzXeOmGOXDPcjjjm1C1FmbfCPePgVe+BZUlR55XWw5/vwB2rIKrH4eJYZxnmIg8uW4bD6XAvmx8bhxkVpOSofeIjsuAW5pdc6VR50NyauTHFkcUrIqIiIhIh2Yc058X75hGtieVa/+ykHueWkpjSyuzpga5xqVP72Fw1nfgq8vhuudh5Fnw8d/hj6fCn85wDZnqa2HHGvjbeW6Jj+tfdJ1/JbQ83i7KcRGslrltPJQBgysF7ihYLV8E9TVuvqp0STPSRURERKRTxf2yefH26dz11FLeXVfFlBH5jO6fHZqLJyVD8Qz3tb8GSp6FTx513X5f+x4kpUJaFtw4DwaoY2pY+DKr8TBvtc4XrMZBGTC4Jkulb7i5tkntcoRr57kS4eJzoje2OKFgVURERES6lJuZysNfOpknPipjyoj88NwkM981Ypp8K2xbBksfdWXClz3g5rlKePjWp42HtVZryyAj332AEQ8KiqGlAXa3awplLayZCyPOgPQQfehzFFOwKiIiIiLdSk4yXN/T8l9/GAODJrgvCb94m7MaLyXAAPlFbltd2jbuqjWw6zM1CfOT5qyKiIiIiCSqg8FqHGRW68rjpwQY2i1f027e6pq5bjta86/9oWBVRERERCRRxUuDJWtdZjUeOgH7ZA+A1KxDg9W182DwSZAzMHrjiiMKVkVEREREElVqhmtkFesNlvbthOb6+CoDNsY1Waouda93b4MtS2CMugD7y69g1RhzgTFmrTGm1Bjz7Q7e/7Ux5lPv1zpjTK13/wRjzAJjzEpjzHJjzFWh/g8QEREREZEgGeOaLMV6ZjXeOgH75BdBjTezuu4Vtx17cfTGE2e6bbBkjEkGHgTOBSqAxcaYOdbaVb5jrLVfa3f8XcBE78v9wBetteuNMYOAJcaYV621Mf7RjYiIiIhIgvDkxn6wGm9rrPoUFMPqf0FLk5uv2nsE9B0b7VHFDX8yq5OBUmvtRmttI/AUcFkXx18DPAlgrV1nrV3v/X4rsAPo27Mhi4iIiIhIyHjyYr/BUm252+bGWWa1oAhsC2xfAZ+967KqxkR7VHHDn2B1MFDe7nWFd98RjDHDgBHA/A7emwykARs6eO9WY8zHxpiPq6qq/Bm3iIiIiIiEQjxkVuvKIT2nbV3YeOHrCLzoz9DSqBLgAPkTrHYU+ttOjr0amG2tbTnkAsYMBB4FbrTWth5xMWv/bK2dZK2d1LevEq8iIiIiIhHjyY39Bku1ZfFXAgxtwWrJs5BZAEOnRHc8ccafYLUCaJ9vHwJs7eTYq/GWAPsYY3KAucD3rLULgxmkiIiIiIiESTw0WKotj78SYIDMfFdm3doEoy+ApORojyiu+BOsLgZGGWNGGGPScAHpnMMPMsaMAXoDC9rtSwNeAB6x1j4bmiGLiIiIiEjIeHLdnFXbWfFklFnrzazGYbAKbdlVLVkTsG6DVWttM3An8CqwGnjGWrvSGPNjY8yl7Q69BnjK2kOe8pnA6cCX2i1tMyGE4xcRERERkZ7w5EFrMzTtj/ZIOnagFhr3xGcZMECf0ZDigaKzoj2SuNPt0jUA1tp5wLzD9n3/sNc/7OC8x4DHejA+EREREREJJ0+u2x6og7Ss6I6lI/HaCdjnzG/BxFmx+Wcb4/wKVkVERERE5Cjl67BbXws5g6I7lo4cXGM1ToPV3sPdlwTMnzmrIiIiIiJytGqfWY1Fdd7Mat6w6I5DIk7BqoiIiIhIIjsYrMbo8jW15ZCS4ZZ+kYSiYFVEREREJJF5vGXAsZpZrd3sSoCNifZIJMIUrIqIiIiIJLJYD1bryuO3E7D0iIJVEREREZFE5isDro/hMuB47QQsPaJgVUREREQkkSWnQFqv2MysNuyF+hplVhOUglURERERkUTnyY3NBksHOwErWE1EClZFRERERBKdJy82M6u+NVZVBpyQFKyKiIiIiCQ6T25szln1BavKrCYkBasiIiIiIokuI0Yzq3XlkJwGvfpHeyQSBQpWRUREREQSnSc3NoPV2jLIGQxJClsSkf7WRUREREQSnScvNhss1WqN1USmYFVEREREJNF5cqFhN7S2RHskh6orhzw1V0pUClZFRERERBKdJ9dtY6kUuOkA7N0OucqsJioFqyIiIiIiiS4jz21jKVitq3BblQEnLAWrIiIiIiKJ7mBmNYbmrdb5lq1RGXCiUrAqIiIiIpLoPDGYWdUaqwlPwaqIiIiISKKLxTmrteVgkiF7ULRHIlGiYFVEREREJNH5gtX6GCoDri2DnEGQnBLtkUiUKFgVEREREUl0MdlgSWusJjoFqyIiIiIiiS6tlyu5jaUGS7XlkKvmSolMwaqIiIiISKIzxpUCx0pmtaUJ9mxVJ+AEp2BVRERERERiK1jdvQVsq8qAE5yCVRERERERcfNWY6XBUm2526oMOKEpWBURERERkdjKrGqNVUHBqoiIiIiIgDdYjZHMap0vszokuuOQqFKwKiIiIiIi4MmLocxqOfQaACnp0R6JRJGCVRERERERcZnVmJmzulmdgEXBqoiIiIiI4BostTRA04Foj8SVAWu+asJTsCoiIiIiIi6zCj0rBW5phh1rejaO1hao26JOwKJgVUREREREcHNWoWdNlpY+Cg9NgfLFwV9jTyW0NimzKgpWRURERESEdsFqDzKr619z2/fuD/4avk7AClYTnoJVERERERFpKwMOtslSSxN89i6kZcO6V6ByRXDX8a2xqjLghKdgVUREREREXIMlCD6zWv4RNO6FC/4fpPWC938V3HV8waq6ASc8BasiIiIiItKuwVKQmdUN88Ekw7GXwck3wcoXoHpD4NepK4fMAkjLCm4cctRQsCoiIiIiIiEIVt+EISe760y9A5JS4YPfBH6d2jKVAAugYFVERERERABS0iElI7gy4H3VsPVTKDrbvc7uDydeD58+CXUVgV2rVmusiqNgVUREREREHE9ucA2WPnsbsFA8o23f9Hvcvg8f8P861royYAWrgoJVERERERHxycgLLrNaOt8FuoMmtu3LK4RxM2HJP2DfTv+us68Kmg+oDFgABasiIiIiIuLjyQ18zqq1rrnSyDMhKfnQ9079mgs+F/7Bv2vVao1VaaNgVUREREREHE8QmdWqNbBnKxTNOPK9vqPh2Evho7/4d906LVsjbRSsioiIiIiI48kNPFjdMN9ti87q+P1T/wsa6mDxX7u/lm+NVZUBCwpWRURERETEJyMv8AZLG+ZDwajOS3cHTYDic2DBQ9C4v+tr1ZZDeq4bhyQ8BasiIiIiIuJ4cqFhN7S2+nd80wHY9MGhXYA7ctrXYf9O+OSRro+rLVMJsBykYFVERERERBxPLthWaNzj3/FlC6C5vm191c4MOwUKp8GHv4Pmxs6P07I10o6CVRERERERcTze8lt/561umA9JqTD81O6PPe1e2L0Flj/d8fvWujJgzVcVLwWrIiIiIiLieHLdNpBgtXAqpGV1f2zxDBh4Arz/a2htOfL9+l0uo6syYPFSsCoiIiIiIo6vsZE/TZb2VML2Fd2XAPsY47KrNRtg1YtHvl+nNVblUApWRURERETECSSzuuEtt+2uuVJ7Y/8D+oyG937lyn7bq/UGqyoDFi8FqyIiIiIi4hwMVv3IrG6YD5l9oP84/6+flOTWXd2+Ata9euh7vjVWlVkVLwWrIiIiIiLi+NtgqbUVNr7lSoCTAgwpxl3hAtL3fnlodrWuHFIzIbMgsOvJUUvBqoiIiIiIOOk5gOl+zur2EthX5f981faSU2H6PVCxGDa937a/tsyVABsT+DXlqKRgVUREREREnKQk8OR0n1ndMN9ti84K7j4TroOsfi676lNbpk7AcggFqyIiIiIi0saT232wWvom9D8esgcEd49UD0y7Eza+DRVL3L66cs1XlUMoWBURERERkTaevK4bLDXug7KFwWdVfSZ92d3r/V9Bwx63zqo6AUs7ClZFRERERKRNd5nVTR9Aa1Nw81XbS8+GKbfBmpdh/WtunzKr0o6CVRERERERaePJ7brB0oY3IcUDhdN6fq8pX4HULHjtf9xrBavSjoJVERERERFpk5HXdWZ1w3wYNt3NO+2pzHw4+cuwe4t7rTJgaUfBqoiIiIiItOlqzmptOexcB8UzQne/U+6E5DT31at/6K4rcS8l2gMQEREREZEY4smDpv3Q3AgpaYe+d3DJmh7OV20ve4ALWCuXu6VzRLwUrIqIiIiISBtPrts27IaUPoe+t2E+ZA+CvmNDe89zfhDa68lRQR9diIiIiIhIG1+weniTpdYWty5q0dlgTMSHJYlHwaqIiIiIiLTJyHPbw5ssbV3q5rL2dH1VET8pWBURERERkTa+zOqBXYfu3zAfMDBSwapEhoJVERERERFp4+kks1r6JgyaAFkFkR+TJCQFqyIiIiIi0uZgZrVdsHqgDioWh7YLsEg3FKyKiIiIiEgb35zV9g2WPnsXbAsUhXB9VZFuKFgVEREREZE2KR5ITjs0s7phPqT1giEnR29cknAUrIqIiIiISBtjXCnwgXaZ1Q3zYfhpkJIWvXFJwvErWDXGXGCMWWuMKTXGfLuD939tjPnU+7XOGFPb7r0bjDHrvV83hHLwIiIiIiISBp68tsxq9QbYtQmKVQIskZXS3QHGmGTgQeBcoAJYbIyZY61d5TvGWvu1dsffBUz0fp8P/ACYBFhgiffcw/pgi4iIiIhIzPDkts1Z3TDfbdVcSSLMn8zqZKDUWrvRWtsIPAVc1sXx1wBPer8/H3jdWlvjDVBfBy7oyYBFRERERCTMMtplVjfMh7xhkD8yumOShONPsDoYKG/3usK77wjGmGHACGB+oOeKiIiIiEiM8OS6YLWlyXUCLjrbzWUViSB/gtWOnkrbybFXA7OttS2BnGuMudUY87Ex5uOqqio/hiQiIiIiImHjyXMNlioWQ+NelQBLVPgTrFYAQ9u9HgJs7eTYq2krAfb7XGvtn621k6y1k/r27evHkEREREREJGx8mdXSN8Ekw4jToz0iSUD+BKuLgVHGmBHGmDRcQDrn8IOMMWOA3sCCdrtfBc4zxvQ2xvQGzvPuExERERGRWOXJhdZmWP0vGDLJzWEVibBug1VrbTNwJy7IXA08Y61daYz5sTHm0naHXgM8Za217c6tAX6CC3gXAz/27hMRERERkVjlC053rlUJsERNt0vXAFhr5wHzDtv3/cNe/7CTc/8O/D3I8YmIiIiISKR5ctu+L9L6qhId/pQBi4iIiIhIIvF4M6ueXBg0MbpjkYSlYFVERERERA7ly6yOPBOS/SrGFAk5BasiIiIiInKo7IFgkmD0hdEeiSQwfUwiIiIiIiKHyhkIdy2B3iOiPRJJYApWRURERETkSPkjoz0CSXAqAxYREREREZGYo2BVREREREREYo6CVREREREREYk5ClZFREREREQk5ihYFRERERERkZijYFVERERERERijoJVERERERERiTkKVkVERERERCTmKFgVERERERGRmKNgVURERERERGKOglURERERERGJOQpWRUREREREJOYoWBUREREREZGYo2BVREREREREYo6CVREREREREYk5xlob7TEcwhhTBWyO9ji60QfYGe1BSMzTcyLd0TMi/tBzIt3RMyL+0HMi3YnkMzLMWtu3u4NiLliNB8aYj621k6I9Doltek6kO3pGxB96TqQ7ekbEH3pOpDux+IyoDFhERERERERijoJVERERERERiTkKVoPz52gPQOKCnhPpjp4R8YeeE+mOnhHxh54T6U7MPSOasyoiIiIiIiIxR5lVERERERERiTkKVgNkjLnAGLPWGFNqjPl2tMcj0WeM+bsxZocxZkW7ffnGmNeNMeu9297RHKNEnzFmqDHmLWPMamPMSmPMPd79elYEAGOMxxjzkTFmmfcZ+ZF3/whjzCLvM/K0MSYt2mOV6DLGJBtjlhpjXva+1jMihzDGbDLGlBhjPjXGfOzdp583cghjTJ4xZrYxZo3395NTYu05UbAaAGNMMvAgcCFwLHCNMebY6I5KYsA/gAsO2/dt4E1r7SjgTe9rSWzNwL3W2mOAqcAd3v9/6FkRnwbgbGvtCcAE4AJjzFTg58Cvvc/ILuCmKI5RYsM9wOp2r/WMSEfOstZOaLcUiX7eyOF+C/zbWjsWOAH3/5WYek4UrAZmMlBqrd1orW0EngIui/KYJMqste8CNYftvgz4p/f7fwKXR3RQEnOstdustZ94v9+D+4EwGD0r4mWdvd6Xqd4vC5wNzPbu1zOS4IwxQ4CLgb96Xxv0jIh/9PNGDjLG5ACnA38DsNY2WmtribHnRMFqYAYD5e1eV3j3iRyuv7V2G7ggBegX5fFIDDHGDAcmAovQsyLteMs7PwV2AK8DG4Baa22z9xD93JHfAN8EWr2vC9AzIkeywGvGmCXGmFu9+/TzRtobCVQBD3unFfzVGJNFjD0nClYDYzrYp3bKIuI3Y0wv4Dngq9ba3dEej8QWa22LtXYCMARXzXNMR4dFdlQSK4wxlwA7rLVL2u/u4FA9IzLdWnsiburaHcaY06M9IIk5KcCJwB+stROBfcRgabiC1cBUAEPbvR4CbI3SWCS2bTfGDATwbndEeTwSA4wxqbhA9XFr7fPe3XpW5AjeUqy3cfOb84wxKd639HMnsU0HLjXGbMJNRTobl2nVMyKHsNZu9W53AC/gPvzSzxtprwKosNYu8r6ejQteY+o5UbAamMXAKG/XvTTgamBOlMcksWkOcIP3+xuAl6I4FokB3nllfwNWW2t/1e4tPSsCgDGmrzEmz/t9BnAObm7zW8AV3sP0jCQwa+1/W2uHWGuH434HmW+tnYWeEWnHGJNljMn2fQ+cB6xAP2+kHWttJVBujBnj3TUDWEWMPSfGWlWKBMIYcxHuU8xk4O/W2vuiPCSJMmPMk8CZQB9gO/AD4EXgGaAQKAOutNYe3oRJEogx5lTgPaCEtrlm38HNW9WzIhhjxuOaWSTjPkx+xlr7Y2PMSFwWLR9YClxnrW2I3kglFhhjzgS+bq29RM+ItOd9Hl7wvkwBnrDW3meMKUA/b6QdY8wEXLO2NGAjcCPenz/EyHOiYFVERERERERijsqARUREREREJOYoWBUREREREZGYo2BVREREREREYo6CVREREREREYk5ClZFREREREQk5ihYFRERERERkZijYFVERERERERijoJVERERERERiTn/H6WR1G3dg7SqAAAAAElFTkSuQmCC\n", "text/plain": [ - "
" + "
" ] }, "metadata": {}, @@ -1157,30 +1852,54 @@ "b = y[:,0]\n", "c = np.append(b, y[-1], axis=0)\n", "\n", + "pyplot.rcParams[\"figure.figsize\"] = [16,7]\n", "# Show how the model fits the test data\n", - "pyplot.plot(a[:100], label='original')\n", - "pyplot.plot(b[:100], label='model')\n", + "pyplot.plot(a[1:501,0], label='original')\n", + "pyplot.plot(b[:500,0], label='model')\n", "pyplot.legend()\n", "pyplot.show()\n", "\n", "# Show how the model predicts data\n", "pos = int(a.shape[0]-n_out*4)\n", - "pyplot.plot(a[pos:], label='original')\n", - "pyplot.plot(c[pos:], label='model')\n", + "pyplot.plot(a[pos:,0], label='original')\n", + "pyplot.plot(c[pos:,0], label='model')\n", "pyplot.legend()\n", "pyplot.show()\n" ] }, { "cell_type": "code", - "execution_count": 22, + "execution_count": 59, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[[ 8.9856803e-01 -7.4589159e-04 8.7852126e-01 9.0029079e-01]\n", + " [ 8.9843249e-01 -6.8965461e-04 8.7837911e-01 9.0037239e-01]\n", + " [ 8.9910692e-01 -7.4440148e-04 8.7914973e-01 9.0114528e-01]\n", + " ...\n", + " [ 7.1893144e-01 6.8879081e-03 6.9375437e-01 7.1917194e-01]\n", + " [ 7.3019677e-01 4.0721074e-03 7.0591629e-01 7.2876668e-01]\n", + " [ 7.2643816e-01 4.1702050e-03 7.0026231e-01 7.2447646e-01]]\n" + ] + } + ], + "source": [ + "print(c)" + ] + }, + { + "cell_type": "code", + "execution_count": 60, "metadata": {}, "outputs": [], "source": [ "#Prediction on public data!\n", "period = CONFIG['period']\n", "import time\n", - "import urllib2\n", + "import urllib\n", "import json\n", "\n", "# Download a live bitcoin price data set\n", @@ -1192,7 +1911,7 @@ " start = end - points*period\n", " #print start, time.strftime(\"%a, %d %b %Y %H:%M:%S +0000\", time.gmtime(start))\n", " url = \"https://poloniex.com/public?command=returnChartData¤cyPair=%s&start=%d&end=%d&period=%d\" % (pair, start, end, period)\n", - " openUrl = urllib2.urlopen(url)\n", + " openUrl = urllib.request.urlopen(url)\n", " r = openUrl.read()\n", " openUrl.close()\n", " d = json.loads(r.decode())[-n_lag:]\n", @@ -1207,7 +1926,7 @@ }, { "cell_type": "code", - "execution_count": 23, + "execution_count": 61, "metadata": {}, "outputs": [], "source": [ @@ -1216,27 +1935,29 @@ " rt_values = rt_df.loc[:,columns].values\n", " rt_scaled = scaler.transform(rt_values)\n", " rt_x = rt_scaled.reshape((1, n_lag, n_features))\n", - " print rt_x.shape\n", + " print(rt_x.shape)\n", " return rt_scaled, model.predict(rt_x)" ] }, { "cell_type": "code", - "execution_count": 24, - "metadata": {}, + "execution_count": 62, + "metadata": { + "scrolled": false + }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "(1, 30, 4)\n" + "(1, 48, 4)\n" ] }, { "data": { - "image/png": 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\n", + "image/png": 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\n", 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\n", + "image/png": 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\n", 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\n", 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\n", + "image/png": 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\n", 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\n", + "image/png": 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\n", 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lIjSER97cT19NjDWNbZyqbmLa6Lhe9+tubYTODuUe+jU6jx2dQIjAXk10N6wNp+bq4WKw11QDQoA6WdXITU9t5fmtx7n7omxeuXdxt6OJPDEiLpKHrpjCxsMVrN5b0uu+B0q6n6Hc1XR7pJF7s1FuUS2hIcKkkbG9HhsV7mDyyDhNhT2MRUZGUllZqUHBi4wxVFZWEhkZOeBz6DwEPzHG+GwM87v7S3ho1W4M8Ns75nHF9FGDPufti8bxykcn+cnbuVw6Na1znYKuDvSQsqKrhOgw0hOjzspptL+olokjYj2aHT0zPYH3D5b59Doq38nIyKCwsJDy8nJ/FyWgREZGkpHRd/9gTzQgnGfVja28tqOQF7adICI0hDUPLvXqF9rWI5Xc+6cdzExPYOXn55KZ0r9JZT1xhAg/vHY6Nz21ld98cIR/vXxKt/vlFtWSEhNOWlxEt6+7mz7m7BQWucW1XDwpzaPyzMpIYNWOQopqmgdc81H+ExYWRnZ2tr+LobrQgHAeGGPYdbKa//vwBH/bU0RLu5PRCZEcq2ggv6yeSSN7b2/vjzX7iokKc/DqfYs9HibqqQuzkrluzhh+u+EoN80b222wOVBS22PKiq5yxsSz9kApja3t1Le0U17X0mf/gcvMDDsVdmG1BgSlvET7EHyosbWdl/55gqt/tYnrn9zCmn3FfHZeBqu/tpRV9y8GYP0h71aZNxwqZ9H4ZK8HA5eHr5pGWIjw6N/OncHc3uHkUGl9nx3KLjmj4zHGynvUmfK6j6Yml2mj4whzCLt1pJFSXqM1BB9pae/giic2cLKqiSkj4/jRtdO57oL0s5aTnJAWw4bDFdy9dLxX3vN4ZQMFlY3c9Yksr5yvOyPjI/l/n5zET9cc5IO8Mi5xyyd0tKKB1nanx3f50+31DXKLa6lrtlZB8/TYiFAHU0bFaQoLpbxIawg+8s9jVZysauKxz87k719fyh2Ls84KBgDLJo9g29FKmtv6vwhHdzbYtY2LJ3vWDj9QX1qSzfjUGH74Vu5ZS2G67vL7moPgMiYhkoSoMHKLathfVEtGUtRZSfb6MjM9kT2F1TpSRSkv0YDgI+8fLCM8NIRrZqf32J5+8eRUWtqdbDtW5ZX3XH+ogrHJUWR3ySDqbeGhITxyzXSOVTTw7KaCzu0HimsJd4QwIa33YaMuItKZCvtAUe05ayj3ZVZGArXN7Zyoaux7Z6VUnzQg+MgHeeUsHp/Sa1v+wuwUwkNDWJ83+H6E1nYnW49UcPGktPMyDHPZ5DSW54zkV+8f7lyDObfYGjYa5vD8v1XO6HgOlNRxrLKhMwuqpzqX1NRmI6W8wqO/XBFZISJ5IpIvIt/tYZ+bRSRXRPaLyItu278gIoftxxfcts8Tkb32OX8pATSY/FhFA8cqGrisj1nBUeEOFmYns+Hw4APCjuOnaWjt8Hlzkbt//3QO7U7Df605AFidw572AbjkjImntd2JMZ73H7hMGRVHeGgIu07qjGWlvKHPgCAiDmAlcCWQA9wqIjld9pkEPAwsMcZMB75ub08GHgEWAguAR0QkyT7sN8A9wCT7scIbH2goWHewDMCjBVyWTU4jv6yeU9WDW+xiw+FyQkOET0xIGdR5+iMzJZr7Lx7PG7uKWL23mIr6Fo/7D1zcE9n1t8kozBHC3MxEth6p7NdxSqnueVJDWADkG2OOGmNagZeBa7vscw+w0hhzGsAYU2ZvvwJYa4ypsl9bC6wQkdFAvDHmQ2P1CP4RuM4Ln2dIWJdXxvi0GI8mhS2z7+g3DHL46fq8cuaOSzqn49rXvnLJRNITo/jWKmvREE+HnLqMT4shPDSExOgwRif0f8r9RRNTyS2upaqhtd/HKqXO5klASAdOuj0vtLe5mwxMFpHNIvKhiKzo49h0+/fezjksNba2s+1oFZd5uLzjxBGxjE6IHFRAKK9rIbe4tjO4nE9R4Q6+9+lpNLRao408nUfgEuYIYU5GIvMykwbU97FkorVI+ZYjFf0+Vil1Nm/NQwjFava5BMgANojITG+cWETuBe4FyMzM9MYpfWpzfiWtHU6Ps4qKCBdPSmP1vmLaO5yE9qND1mWj3Qfhj4AAcOWMUVw0MZVT1U0kRof3+/jf3jGPkJCBdSHNTE8gLiKUzfmVXD1rzIDOoZSyePLtcwoY6/Y8w97mrhB40xjTZow5BhzCChA9HXvK/r23cwJgjHnaGDPfGDM/Lc0/X3j9sS6vjJhwBxdmJXt8zLIpadQ1tw+4c3T9oXJSYsL7fXfuLSLC03fO49X7Fg/o+KSY8H7NP3AX6ghh0YQUNudrDUGpwfIkIHwETBKRbBEJB24B3uyyz+tYtQNEJBWrCeko8A5wuYgk2Z3JlwPvGGOKgVoRWWSPLroTeMMbH8ifjDF8cLCMiyalEh7q+Z3+kgmphMjA+hGcTsPGwxUsnZQ64Ltsb4gOD/UooZ0vLJmQwomqRk7qfASlBqXPby1jTDvwANaX+wHgVWPMfhF5VESusXd7B6gUkVxgHfAtY0ylMaYK+BFWUPkIeNTeBvBV4BkgHzgCrPHi5/KLvNI6imqaPRpd5C4hOow5YxNZf7j/d7n7i6wO1WVThn7tyVcummT1I2gtQanB8agPwRizGljdZdsP3H43wDftR9djnwWe7Wb7dmBGP8s7pK07aN3hD2RVsmWTR/DEPw5R1dBKcozn7fDrD1kDupZ6mDY6EE1Ii2VkfASb8iu4ZcHQ72dSaqjSmcpetO5gGTmj4xkZ3//hkxdPTsUY2NTPu9wNhyqYkR5Paqx/mmuGAhFhyYRUth6pxOnUvEZKDZQGBC+paWxjx4nTXDp1YHfqszISSYwO61cai9pm6z09XVQmkC2ZmEplQysHS+r8XRSlhq2gDwgnqxr58h8+Ir9scF8kG/PL6XCaPtNV9MQRIlw0MZWNh8s9zt65Jb+SDqc5r+kqhiqdj6DU4AV9QHj5oxP842AZ9/xxBzVNbQM+z/sHy0iMDmPO2KS+d+7BxZPTKKtr8fgud8PhcmIjQpmbOfD3DBSjEiKZkBbT7yY3pdQZQR0QjDGs3ltCVko0J6sa+frLH9MxgDZop9OwPq+ciyel4RjE0E/XxDJPVlEzxnrPxRNS+jXENZBdNDGVbUeraG13+rsoSg1LQf1Nkldax7GKBu65eDyPXDOddXnlPL42r9/n2XuqhsqG1gE3F7mMjI9k6qg4j+YjHK1o4FR1kzYXufnExFSa2jo0+6lSAxTUAWH13hJCBC7PGcXtCzO5dcFYVq47wtt7ivt1nvcPliHinZXKlk1OY3vBaRpa2nvdzxU0lmmHcqdF41MIkf6P1FJKWYI8IBSzIDuZtLgIRIT/uGY6czMTeWjVbg4U13p8ng/yypgzNrFf8wd6cvHkNFo7nHx4tPeUzusPlZOd6llG1WCREBXGzIxEtmhAUGpAgjYgHC6tI7+snqtmju7cFhHq4Knb5xEfFcq9f9rOaQ9SKpfXtbC7sMbj7KZ9mZ+VRFSYo9dmo+a2Dj48WsnF9gxddcZFE1P4+GQ1dc0DHyCgVLAK2oCwem8JIrBi+qizto+Ij+Sp2+dRWtPCAy/tpL2j9w5KVwfwQGYndyci1MHiCSls6CWNxfaC0zS3OYM6XUVPlkxMpcNp+KeX1qlWKph4K/31sLNmXzEXjktmRDezii/ITOLH18/g26/t4adrDvL9q3O6OYNlXV4ZI+Ii+r3aV28unpTK+wfLWLkun+hu1mTecKiccEcIi8afv9XRhou5mUlEhIawOb+ST04b6e/iKDWsBGVAOFJez8GSOh75TM9f9DfPH0tuUS3PbDpGQ2sHszISmDgilglpsZ19Be0dTjYcKufKGaO8urD9J6eN5D/XHOR/3ul5xNPlOSOJDg/Kf75eRYY5WJCdrInulBqAoPxG+fu+EgBWzBjV637f+/Q0Kupb+MvOQl7654nO7ckx4UxIiyElJoK65vZ+Zzfty9jkaHb/4HJa2jt63Od8L5U5nHxiQiqP/f0gZXXNjIjrf14ppYJVUAaEt/cUMzczkdEJUb3uF+YI4defn4vTaThV3UR+eT1Hyuo5Ul7PkbIG/llQRWpseGf6ZW+KCncQ1U1zkerbRRNTeQzYeqSSa+cExMqsSp0XQRcQCioayC2u5fufnubxMSEhwtjkaMYmR59TG3A6jV8XplHnyhkTT2J0GJvzKzQgKNUPQTfKaI3dXHSl23DTwdBgMPQ4QoTF41PYdLiix0SBxhgKKhoGlKpEqUAVhAGhmNljE0lP7L25SA1vSyamUlTTTEHl2ctqtnU4eWPXKa759WYu+dkH/HlnoZ9KqNTQE1RNRierGtlTWMPDV071d1GUj7nSYW/OryA7NYaaxjZe+ugEf9hcQEltM+PTYggPDeFgsa6foJRLUAUE1+iiq7zUXKSGrqyUaNITo3h7TzGHS+tYtaOQxtYOlkxM4T9vmMElk0dw5S82cqKqwd9FVWrICKqA8PbeYmamJzA2WfP/BDoRYcnEFF7dXsj241VcOyedLy3JJsdtAmFmSjQFFRoQlHIJmoBwqrqJXSer+faKKf4uijpP/uXSiUweGcc1s8d0OyN9XHI0Gw6V60gxpWxB06nsai66coY2FwWLcSkx3L10fLfBwHo9mpZ2J+X1Lee5ZEoNTUETENbsLWba6HiyU2P8XRQ1RGSmWP8XjncZiaRUsAqKgFBS08z246e5qo9UFSq4jLP7ko5Xaj+CUhAkAeGd/fboolnaXKTOSE+KwhEinKjSGoJSECQBYfXeYqaMjGNCWqy/i6KGkDBHCGMSI7XJSClbUIwy+sUtF1Bc0+TvYqghKDM5muNaQ1AKCJIawqiESC7ITPJ3MdQQlJkcwwntQ1AKCJKAoFRPxqVEc7qxjVpdg1kpDQgquLlGGp3QfgSlNCCo4JaZYgcE7UdQSgOCCm7jdHKaUp00IKigFhsRSkpMuGY9VQoNCEqRmRKtNQSl0ICglDUXQQOCUhoQlBqXHE1xTROt7U5/F2VI+n8vfcyTH+T7uxjqPNCAoIJeZkoMTgOFp7WW0FVbh5N39pWw9Uilv4uizgMNCCrojdOhpz06XtlAa4eT8jpdMyIYeBQQRGSFiOSJSL6IfLeb1+8SkXIR2WU/7nZ77TER2Wc/Pue2/Q8icsztmDne+UhK9U/n5DQNCOfIK6kHoEIXEQoKfSa3ExEHsBJYDhQCH4nIm8aY3C67vmKMeaDLsZ8G5gJzgAjgAxFZY4yptXf5ljHmtcF+CKUGIy0ugqgwh3YsdyOvxPpTrWxopb3DSahDGxUCmSf/uguAfGPMUWNMK/AycK2H588BNhhj2o0xDcAeYMXAiqqUb4iIjjTqQV5pHQDGQFVDq59Lo3zNk4CQDpx0e15ob+vqsyKyR0ReE5Gx9rbdwAoRiRaRVOBSYKzbMT+xj/m5iEQM5AMo5Q2ZKdE6Oa0beSV1RIZZXxNl2o8Q8LxV/3sLyDLGzALWAs8DGGPeBVYDW4CXgK1Ah33Mw8BU4EIgGfhOdycWkXtFZLuIbC8vL/dScZU627jkaE5UNWKM8XdRhoym1g6OVzWyIDsFgHLtRwh4ngSEU5x9V59hb+tkjKk0xrj+tzwDzHN77SfGmDnGmOWAAIfs7cXG0gI8h9U0dQ5jzNPGmPnGmPlpaWmefi6l+iUzJZrmNqfeBbvJL6vHGFg6MRVARxoFAU8CwkfAJBHJFpFw4BbgTfcdRMR9seJrgAP2doeIpNi/zwJmAe+6HyMiAlwH7BvcR1Fq4DLtkUbaj3DGQbtD+RMT7RqCBoSA1+coI2NMu4g8ALwDOIBnjTH7ReRRYLsx5k3gayJyDdAOVAF32YeHARut73xqgduNMe32ay+ISBpWrWEXcL/3PpZS/ePKenqiqpEF2cl+Ls3QcKi0jojQEKaOiicuIlQDQhDwaE1lY8xqrL4A920/cPv9Yaw+ga7HNWONNOrunJf1q6RK+VB6YhQhgi6n6SavtJ5JI2NxhAhpcRHahxAEdFCxUkB4aAhjEqM4rpPTOuWV1DJ5ZBwAqXERWkMIAhoQlLKN0zTYnaobWymtbWHqKCsgpMVFUKEBIeBpQFDKlpkco+krbIdKrZQVrhpCWqzWEIKBBgSlbONSoqmXYUkKAAAZE0lEQVRqaKWuuc3fRfE7V8qKKW41hLqWdppaO3o7TA1zGhCUsunQ0zPySuuIjwxlVHwkYAUE0CR3gU4DglK2TM162imvpI4po+Kwh4x3BgSduBfYNCAoZdN1ESzGmM6A4JIWawUE7UcIbBoQlLLFRYaRHBMe9E1GpbUt1Da3M2XkmYAwwq4h6FyEwKYBQSk3mcma9dSVsmKyW0BIjglHRGsIgU4DglJudC6ClbICOKvJKNQRQkpMuAaEAKcBQSk345KjKapuorXd6e+i+E1eST0j4yNIjA4/a3uqzkUIeBoQlHKTmRKD08Cp6iZ/F8Vv8kprz2ouctF8RoFPA4JSblwjjY4HaZK7DqfhcGl9Z8oKd5q+IvBpQFDKTbDPRThe2UBLu7PnGkJdi64qF8A0ICjlZkRcBJFhIZwI0o5lV4fy1FHx57yWFhtBa4eT2qb2c15TgUEDglJuRITM5OigTYOdV1KPCEwcEXvOa2mdcxGaz3ex1HmiAUGpLjKTY4K2hpBXWsu45Giiwh3nvKbpKwKfBgSluhiXEs2JqsagbCvvmrLCXedsZQ0IAUsDglJdjEuJpqmtY8BffM9sPMrj7+bR2Dq82tqb2zooqGw8K2WFu7Q4K/OpBoTApQFBqS4602APoB+hsbWd/3knj1++n8/yxzfwjwOl3i6ezxwpr6fDaZjcQw0hPjKU8NAQnYsQwDQgKNXFuJQYYGDrImw8XEFLu5NvXTGF6HAHX35+O/f9aTvFNUN/otuZEUbdBwQR0ZXTApwGBKW6SE+MIkTgxAAmp63NLSU+MpR7Lx7P219byrdXTGH9oXI+9b/reWbjUdo7/JMS4+drD3HvH7f3+v4HS+oId4R0BsTuuOYiqMCkAUGpLsJDQxidENXvyWkdTsP7B8u4dOoIwhwhhIeG8NVLJrL2G8tYkJ3Mj98+wGd+vZmPT5z2Ucl7tv5QOe/mlvLz9w71uM+hkjomjIglzNHz14IGhMCmAUGpboxLieZYP5uMdhw/TVVDK8tzRp61fWxyNM/edSG/uW0uVQ0t3PzbrRRUnN/UGCU1zYQ5hJXrjrAur6zbfQ6V1jNl5LnzD9ylxUXoMpoBTAOCUt2Ym5nE3sJqSmo8n4S1NreEMIewbHLaOa+JCFfOHM2bD1yEMfDCtuPeLG6v2juclNU1c9cnspg6Ko5vvLKLoi7J+2qb2zhV3dRjh7JLWmwElQ2tfmv6Ur6lAUGpbtw0PwOngT/vLPRof2MMa3NLWTwhlbjIsB73GxkfyRUzRvHq9kKaWju8Vdxelde34DSQnRrLk7fNpb3D8C8v7jwrxffhPjqUXdLiIjAGqhpafVpm5R8aEJTqxriUGBaNT+aVj07idPY9QS2/rJ6CysZzmou6c+eicdQ0tfHWniJvFLVPxXYtZ3RCJOPTYvnpZ2fy8Ylq/vvvBzv3ySupB+g2qZ07na0c2DQgKNWDWy7M5ERVIx8eq+xz37X2fINPTRvR574LspOZPDKWP209fl5mQxdXWwFhVII1sezqWWP4wuJxPLPpGH/fVwJAXkktsRGhpCdG9XquNF1bOaBpQFCqBytmjCIuMpRXPjrZ575rc0uZmZ7A6ITev1DB6k+4Y3EWe0/VsLuwxuPy7Dh+mt+uP+Lx/i6uORCj7YAA8G+fnsasjAS+9dpuTlQ2kldax+SRsYhIr+dKi9X0FYFMA4JSPYgMc3D9Bems2VdCTWNbj/uV1TWz62S1R81FLtdfkE5MuIM/bi3waP+m1g6+9tLH/PTvB/u9vGdJTTNRYQ4Sos70bUSEOlj5+bkI8NUXd3CwlxxG7tI0n1FA04CgVC9unj+W1nYnr+861eM+/zhQhjH0KyDERoRyw9wM/ran2KMO2pXr8jlV3YQx9HvWc3FtM6MTIs+5+x+bHM3/3jyHfadqqW5s6zGHkbvIMAdxkaEaEAKUBgSlejEjPYEZ6fG9NhutzS0lIymqzxE6Xd2xeByt7U5e3d57k9TR8nqe3nCU8WnWDOLC0/0MCNVNnf0HXS3PGcm9F48HIGdMgkfn08lpgUsDglJ9+Nz8seQW17Lv1Lnt/Q0t7WzKr2B5zsg+29+7mjwyjoXZybyw7TgdPYxkMsbwyJv7iQgN4X9unA3AqX4GhJKa5h4DAsC3r5jCi/cs5MKsJI/Op/mMApcGBKX6cM2cdCJCQ3j5oxPnvLbxcDmt7c5+NRe5u3NxFiermlh/qPvZw+/sL2Hj4Qq+eflkZmUkECJQeNrzGdQdTkNpXQtjeunsDnWE8IkJqR4HtLS4CB1lFKA0ICjVh4SoMK6aOZo3dhWdM5nsXTuZ3YVZyQM69+XTRzIiLoI/bj135nJjazuPvpXL1FFx3LFoHGGOEEbFR/aryaiivoUOp+m1htBf2mQUuDQgKOWBm+ePpa65nTX7iju3tXc4WXewjMvsZHYDEeYI4dYFmaw/VM7xLtlVf/1+PkU1zfzouhmE2ufPSIqmsNrzgOA+Kc1b0uIiqG9pH3YLAKm+aUBQygOLxieTlRJ9VufyjuOnOd3YxvKcUYM6960LMgkR4YVtZ5qkjpTX87uNR/ns3Iyzah8ZSVH96kMotoOHV2sI9lyEijpNXxFoPAoIIrJCRPJEJF9EvtvN63eJSLmI7LIfd7u99piI7LMfn3Pbni0i2+xzviIi4d75SEp5n4hw0/yxbDtWxTE7U+na3FLCHSEsm3JuMrv+GJUQyRXTR/Lq9pM0t3VgjOE/3txPZJiD71459ax905OiKK5pos3D5HJnagh9T5jz1JnZyp4n/lPDQ58BQUQcwErgSiAHuFVEcrrZ9RVjzBz78Yx97KeBucAcYCHwkIjE2/s/BvzcGDMROA18edCfRikfunFeBo4Q4dXtJ61kdgdKWTwhhdiI0EGf+/ZF46hubOOt3UWs2Wd1JD90+ZTOL1+XjKQonAaPs7CW1DYTERpCUnTPCff6SyenBS5PaggLgHxjzFFjTCvwMnCth+fPATYYY9qNMQ3AHmCFWMMZLgNes/d7Hriuf0VX6vwaGR/JpVPSeG1HIQeK6zjuYTI7Tywen8LEEbE8u7mAH/0tl5zR8dy2MPOc/TKSrPWePe1YLq7pflLaYGhACFyeBIR0wH3mTKG9ravPisgeEXlNRMba23ZjBYBoEUkFLgXGAilAtTHG1SvV0zmVGlJunj+W8roW/v2NfQB8app3AoKIcMeicRworqW4ppkfXTe9syPZnSv5nKdDT3ublDZQKTERhIgGhEDkrU7lt4AsY8wsYC3WHT/GmHeB1cAW4CVgK9CvJPAicq+IbBeR7eXl5V4qrlIDc+nUEaTFRbDj+GlmZSR49cv2hrnpJESF8bn5Y5k3rvthrKMTIxGBUx6ONLJqCN7rPwBwhAjJMToXIRB5EhBOYd3Vu2TY2zoZYyqNMa7/Hc8A89xe+4ndr7AcEOAQUAkkikhoT+d0O/5pY8x8Y8z8tLTBdd4pNVhhjhA+OzcDgOVeqh24xEWGse6hS/jJ9TN63Cci1MHIOM/mIjidhlI7j5G36VyEwORJQPgImGSPCgoHbgHedN9BREa7Pb0GOGBvd4hIiv37LGAW8K6xksCvA260j/kC8MZgPohS58sdi8exMDuZ6y7wfitnckx4t01F7tKTojxqMqpoaKHdaTQgKI/1OTzCGNMuIg8A7wAO4FljzH4ReRTYbox5E/iaiFwDtANVwF324WHARrtDqxa43a3f4DvAyyLyY+Bj4Pfe+1hK+U56YhSv3LfYb++fkRTFzhOn+9zvzMI43m0yAmsuQr697KYKHB6NlzPGrMbqC3Df9gO33x8GHu7muGaskUbdnfMo1ggmpVQ/ZCRF8faeYto7nL3WJnwxS9nFlc/IGOPVEUzKv3SmslLDTHpiNO120rrelHSzUpq3pMVF0NZhqGnqeeEgNfxoQFBqmMlIspqA+kphUVzbTLgjhOQY7ycB0LkIgUkDglLDjCsg9NWx7FoHwRdNOrq2cmDSgKDUMDOmc3JaHzWE6t4XxhmMM/mMNCAEEg0ISg0zkWEO0uIiPGgyamKMrwOC1hACigYEpYah9MQoCqt7bjJyOg2lNS0+GXIKEB8ZSnhoiAaEAKMBQalhKCMpqtcmo6rGVlo7nD4ZYQRW7iVdWznwaEBQahjKSIqmqLoJp9N0+/qZSWm+CQigaysHIg0ISg1D6UlRtHUYynq4Qy+25yCM8VGTEWj6ikCkAUGpYaivoaclteephqABIaBoQFBqGBrrmpzWQxrs4ppmwhxCig8mpbmkxUZQ1djq8XKeaujTgKDUMNTXXISSmmZGxkcSEuK7PEMj4iMwBqoaWn32Hur80oCg1DAUHR5KSkx4j01GRdVNPu0/AJ2tHIg0ICg1TPU29LSk1nezlF10clrg0YCg1DCVnhTV7WxlY4y9dKYGBNU/GhCUGqYykqIp7GYuwunGNlrbnT6vIaTGaj6jQKMBQalhKiMpitZ2JxUNZ38hF1X7bh0Ed5FhDuIjQ7WGEEA0ICg1TKX3MNKopHOlNN92KoPORQg0GhCUGqYykqKBcwNCca3vls7sSgNCYNGAoNQwld7DymklNU2Ehggpdhu/L6XFRWofQgDRgKDUMBUbEUpidNg5cxGK7UlpDh9OSnPRjKeBRQOCUsNYd3MRiqt9P+TUJS0ugvqWdhpb28/L+ynf0oCg1DCWkRh9Tj6j8zEpzWVWRgI3z8+grb37NNxqeAn1dwGUUgOXnhTFB4fKMMYgIvaktCY+NW3EeXn/JRNTWTIx9by8l/I9rSEoNYxlJEXR3Oak0k4wV9PURnOb02dLZ6rApgFBqWHMNfTUNdKoyF4pbcx5ajJSgUUDglLDWNfJaSW11s/z1YegAosGBKWGsfQuK6cVn8dZyirwaEBQahhLiAojPjK0c6RRSU0zjhDpzESqVH9oQFBqmEtPiu5sMiqqbmZkXMR5mZQ2aIXbYd1/Qk2hv0uibBoQlBrmrMlpVpNRSW3T8Ok/2PA/sP4x+MVs+Ov9UHbA3yUKehoQlBrmMuyFcs4sjDMM+g+cHXB8C0y9Gi68B3LfgCcXwYu3wIkP/V26oKUBQalhLj0xiobWDk43tlFSc/5mKQ9KyV5oqYXp18OVP4Vv7IdLHoaT2+DZK+D3V0DeGjA6A/p80oCg1DDnmotwoLiWxtaO85bHaFAKNlk/xy2xfkYnwyXfhW/sgyv/G2qL4KVb4JXboanaf+UMMhoQlBrmMuyhpx8VVAHDZMhpwSZIngDxo8/eHh4DC++Dr+2E5T+CQ3+Hp5dB8W7/lDPIaEBQaphzBYTtBaeBYTApzdV/kHVRz/s4wmDJ1+Cut6G9FZ5ZDjv+oE1IPqYBQalhLiEqjNiIUHaesALCkG8yKt0HLTWQtbTvfTMXwf0bIWsJvPWgNRqptcH3ZQxSGhCUGuZEhIykKBpbOwgRGDHUJ6W5+g+ylni2f0wq3Paa1em85xX43Seh/JDvyhfEPAoIIrJCRPJEJF9EvtvN63eJSLmI7LIfd7u99t8isl9EDojIL0VE7O0f2Od0HXN+8vUqFYBcOY1GxEUS6hji93kFmyB5PMSP8fyYEIfV6XzHX6ChDJ6+BHa/ok1IXtbn/xwRcQArgSuBHOBWEcnpZtdXjDFz7Mcz9rGfAJYAs4AZwIXAMrdjbnM7pmyQn0WpoOXqRxge/Qebe+8/6M2Ey+D+TTB6Fvz1XmsUUr1+dXiLJ7cSC4B8Y8xRY0wr8DJwrYfnN0AkEA5EAGFA6UAKqpTqmWvo6bDoP2j2sP+gJ/FjrM7mT/0QDq+FlQth31+8V8Yg5klASAdOuj0vtLd19VkR2SMir4nIWABjzFZgHVBsP94xxrjPT3/Obi76d1dTklKq/1xZT4f8kNOCzdbPcR72H/QkxAEXfR3u2wBJWfDaF+HVO6GhYtBFDGbeamx8C8gyxswC1gLPA4jIRGAakIEVRC4TEdetwW3GmJnAUvtxR3cnFpF7RWS7iGwvLy/3UnGVCiwZnQFhiNcQXP0HCd3dUw7AiKnw5bXwyUesmc0rF8L+171z7iDkSUA4BYx1e55hb+tkjKk0xrTYT58B5tm/Xw98aIypN8bUA2uAxfYxp+yfdcCLWE1T5zDGPG2MmW+MmZ+WlubZp1IqyEwaEcfSSalcNGkIr2/sdFr9B4OtHXTlCIWl34R710NCBqz6Aqy6C04XePd9goAnAeEjYJKIZItIOHAL8Kb7DiLiPt3wGsDVLHQCWCYioSIShtWhfMB+nmofGwZcDewb3EdRKnhFhTv405cXMm10vL+L0rPSfdBcPbj+g96MzIG734PLvg8HV8Ov5sEbD0DVMd+8XwDqMyAYY9qBB4B3sL7oXzXG7BeRR0XkGnu3r9lDS3cDXwPusre/BhwB9gK7gd3GmLewOpjfEZE9wC6sGsfvvPexlFJDznG7/8DT+QcD4QiDi78FD+6C+V+GPa9ageH1r0LlEd+9b4AQM4zG8c6fP99s377d38VQSg3Ey7dB6X7ry/p8qS2Gzb+AHc9BRxvMutkKGCkTzl8ZhgAR2WGMmd/XfkN8BotSKiA4nVaHsi9rB92JH22l135wNyy83+pw/vV8eOlW2PuapsHoItTfBVBKBYGy/b7tP+hL3ChY8Z+w5EH4cKXVlJS3GsKiYcpVMOOzMPGTEDrE0374mAYEpZTvdV3/wF/iRsLyR61hqse3wL4/W6u17XsNIhNg2mcg53orqV5ErH/L6gcaEJRSvlewyZpAlji2z13PixAHZC+1Hlf9DxxZZwWH/a/Dx/8H4oBRMyFzMWQutH7GjfJ3qX1OA4JSyrdc8w+mftrfJemeIwwmX2492pqssp7YBie2WmswbPuNtV9SlhUYRs6A1EnWI3GcFVwChAYEpZRvleVC02kYN8CEdudTWBRM/JT1AGtkUvEeKzic2Ar5/4DdL53Z3xFurfyWOhFSJlmzsONGW01TcaMhKhlChs/YHQ0ISinf6u/6B0OJIwwy5lmPTzxgbWusgorDUHEIKg9DRT6UHbRSZzjbzz4+JBRiR1nNTXGjICoRIl2PBPt5gvU8Itbq5A6LhnD753mufWhAUEr51vFNVtNKYqa/S+Id0cl2v8LCs7d3tEHtKagrhbpiqLd/up5XHYWmamu0VVujZ+/liLBqLeEx8IW3fD5/QgOCUsp3nE4rw+mUq/xdEt9zhFn9DElZfe/b3mqlAW+usQJEUzW01ll9GK0N1s+2RuvR2mg9j4jz9SfQgKCU8qHyA9BUNfAFcQJVaDjEplmPIWT49HYopYaf4dx/EIQ0ICilfKdgo9V3ECj9BwFOA4JSyjecTms2sL/SVah+04CglPKN1nqYdAVMudLfJVEe0k5lpZRvRMbD9b/xdylUP2gNQSmlFKABQSmllE0DglJKKUADglJKKZsGBKWUUoAGBKWUUjYNCEoppQANCEoppWxijPF3GTwmIuXA8QEengpUeLE4gUqvk+f0WnlGr5NnfHmdxhlj+kytOqwCwmCIyHZjzHx/l2Oo0+vkOb1WntHr5JmhcJ20yUgppRSgAUEppZQtmALC0/4uwDCh18lzeq08o9fJM36/TkHTh6CUUqp3wVRDUEop1YugCAgiskJE8kQkX0S+6+/yDBUi8qyIlInIPrdtySKyVkQO2z+T/FnGoUBExorIOhHJFZH9IvKgvV2vlRsRiRSRf4rIbvs6/dDeni0i2+y/v1dEJNzfZR0KRMQhIh+LyN/s536/TgEfEETEAawErgRygFtFJMe/pRoy/gCs6LLtu8A/jDGTgH/Yz4NdO/CvxpgcYBHwL/b/Ib1WZ2sBLjPGzAbmACtEZBHwGPBzY8xE4DTwZT+WcSh5EDjg9tzv1yngAwKwAMg3xhw1xrQCLwPX+rlMQ4IxZgNQ1WXztcDz9u/PA9ed10INQcaYYmPMTvv3Oqw/4nT0Wp3FWOrtp2H2wwCXAa/Z24P+OgGISAbwaeAZ+7kwBK5TMASEdOCk2/NCe5vq3khjTLH9ewkw0p+FGWpEJAu4ANiGXqtz2M0gu4AyYC1wBKg2xrTbu+jfn+UJ4NuA036ewhC4TsEQENQAGWsImg5Ds4lILPBn4OvGmFr31/RaWYwxHcaYOUAGVu18qp+LNOSIyNVAmTFmh7/L0lWovwtwHpwCxro9z7C3qe6VishoY0yxiIzGutMLeiIShhUMXjDG/MXerNeqB8aYahFZBywGEkUk1L771b8/WAJcIyJXAZFAPPALhsB1CoYawkfAJLsHPxy4BXjTz2Uayt4EvmD//gXgDT+WZUiw23d/Dxwwxjzu9pJeKzcikiYiifbvUcByrP6WdcCN9m5Bf52MMQ8bYzKMMVlY30fvG2NuYwhcp6CYmGZH4icAB/CsMeYnfi7SkCAiLwGXYGVZLAUeAV4HXgUysTLL3myM6drxHFRE5CJgI7CXM22+/4bVj6DXyiYis7A6Qx1YN5uvGmMeFZHxWIM5koGPgduNMS3+K+nQISKXAA8ZY64eCtcpKAKCUkqpvgVDk5FSSikPaEBQSikFaEBQSill04CglFIK0ICglFLKpgFBKaUUoAFBKaWUTQOCUkopAP4/fJ+FhH/lMu8AAAAASUVORK5CYII=\n", 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\n", 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\n", 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\n", 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" + "
" ] }, "metadata": {}, @@ -1351,7 +2072,7 @@ " rt_x, prediction = predict(time.time()-t*period)\n", "\n", " current = rt_x[:,0]\n", - " prediction = prediction[0]\n", + " prediction = prediction[0,:,0]\n", "\n", " pyplot.plot(current, label='current')\n", "\n", @@ -1369,51 +2090,15 @@ "cell_type": "code", "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Sat, 30 Jun 2018 01:40:00 +0000\n", - "(1, 30, 4)\n" - ] - }, - { - "data": { - "image/png": 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hZWVlgypHnRMSEkJKSsqAj9egr9QwKq1pYndBJff0YziFruLDg7njkgx+s+UY319xljkp5wfmY6V1tLYbj4Zf6M6lbl03Bxv0AwMDycjw7GYyNTw0vaPUMHon5zTGOCcmH4w7VkwkZkwgP9+Ue8G2nGLnhCv97bnTITEihLkpUWw9rK1zf6RBX6lh9I8DJUxODGfq2IhBlRMZEsgPLpvMR0fL2XH8/J42B0tqCA20kxEfNuDyL5uWyGcnq6iqbxlUPZXv8Sjoi8gqETksIsdE5MFutq8VkTIR2ed63OG2LVVE3hWRQyJyUETSrau+UiNHaW0TnxZUcvWcwbXyO9yyNI2kqBB+/s7h8y6UHiyuYXpShMd3+nbn8umJOAx8eFRb+/6mz6AvInZgHXAVMBO4UURmdrPrK8aY+a7Herf1zwO/MMbMABYB/nePtxoy7Q7DgcJqNn5eMuJ7gGz6wpXasSjohwTaufeKKew7Vc3mg84btoyHY+j3ZW5yFHFhQX45JMNo58mF3EXAMWNMHoCIvAxcAxzs60DXl0OAMWYzgDGm5z5mSgEOhzNo7cyrYGdeBbvyK6ltct4d+tJ3l7B00oXzyI4U//i8hEkJYUwdO7BeO91Zk5nC0x/m8YtNh7lixliKqxupbWob9AVYm024dFoCW3NLaXeYQf1qUL7Fk/ROMuA+j1qha11X14nIARF5XUQmuNZNBapF5C8i8pmI/ML1y0Gp8+w4Vs6dz2ez4P9u5mu/3c6j/zjEsdI6vjY3iZ+vmQvA7oJKL9dy4Mpqm/k0v5KvzkmydJjeALuN+78yjaOldbz5WRE5xb1PhN4fK6clUtXQyr5T3p3TVVnLqi6bbwEvGWOaReR7wHPA5a7ylwMLgJPAK8Ba4Bn3g0XkTuBOgNTUVIuqpEaSxzcf4ciZWlbNHsfSSXEsmRh33mxSz3yUT/aJKi/WcHDeyTmNw8DVg+y1052rZo9jTnIUv958hK/NS8ImMG2QF4oBVkxxjgC6NbeUzLQYC2qqfIEnLf0iYILbcoprXSdjTIUxpmO81/VAput1IbDPGJNnjGkD/gpccDeJMeZpY0yWMSYrIWGIpmpTPq2wqpEvzxrHz9fM45sLUi6YPjArPYa9J6pod4zMvP7GAyVMTAizJBh3JSL8ZNU0iqob+dPHBUxKCCc0aPA/qKPGBJKZGsNWC4dkUN7nSdDfDUwRkQwRCQK+DWxw30FE3Jsvq4FDbsdGi0hHJL8cD64FqNGlpc3BmdomkqN7nif2ovRY6prbyD1dM4w1s0Z5XTO78issT+24u2RyPEsnxtHc5rAktdPhsukJ5BTXcKamybIylXf1GfRdLfS7gU04g/mrxpgcEXlERFa7drtHRHJEZD9wD84UDsaYduB+4H0R+RwQ4A/Wvw01kp0+24QxkBzTc9DPSnemF7ILRl6K550vXKkdi3rtdKejtQ8wN6Xvmbg81TEonJUDsCnv8iinb4zZCGzssu6nbq8fAh7q4djNwNxB1FH5ucJq52TfKb209JOjQ0mKCmF3QSW3XZw+TDWzxsbPS5gYH8b0cdandtwtSI3h7z+6ZMBj+nRn2tgIkqJC2JJbyg0X6fU2f6B35CqvK652pg7G9xL0RYSs9Fh2F1SOqP765XXN7Myr4OohTO24m50cRUigdR3kRISV0xPZfrScljaHZeUq79Ggr7yuqMo5IUhSdO/DxV6UHsOZmmYKq3qfQMSXbMoZ+tTOUFs5LZH6lvYR3WVWnaNBX3ldUXUDiRHBBAf03kLNSosFIPvEyAk+Gz8vISM+jBlJQ5vaGUoXT4ojyG7Tu3P9hAZ95XVF1Y29XsTtMG1cBBHBAeweIRdzK+qa+eR4BVfPGTcsqZ2hEhYcwPIp8Tpvrp/Q8fSV1xVVNTI7ue9hA+w2YWFaDNkjJM2wKefMiE/tdFh/W9aI/uJS52hLX3mVw2EoPtt7H313F6XHcORMHdUNvj/k78bPS0iPGzPowc98gQZ8/6FBX3lVeX0zLW0Oj9I7AFnpzrz+Hh8fkqGyvoVPhrHXjlKe0qCvvKqj546nLf15KdEE2sXnx+HZlHOadofxi9SO8i8a9JVXFVW7gr6HLf3QIDuzk6N8Pq//zhenSYsbwywLh0RQygoa9JVX9belD85xePafOktTa/tQVWtQ2tod7C6o5NKpCZraUT5Hg77yqqLqRiJCAogICfT4mMy0GFraHXxRdHYIazZwh0pqaWhp77z+oJQv0aCvvKq4urFfrXyALNfY7r7aX7/jztWL0nUMeuV7NOgrryqsaiTFw3x+h7jwYCYmhPlsXj/7RKVrgLj+vS+lhoMGfeVVRQNo6QNclBZL9okqHD42qYoxht0FVdrKVz5Lg77ympqmVmqb2jzuueMuKz2Gs42tHCurG4KaDdzJygbKaps1n698lgZ95TUdPXd6G1K5Jxe5gqqvjfzYcZ3hIg36ykdp0FdeU1zd/+6aHdLixhAfHuxzM2llF1QSGRLAFAsnMlHKShr0ldf098YsdyLCRekxPtjSryQrPRabTfvnK9+kQV95TVFVI0EBNuLDggd0fFZ6LIVVjZw+6xuTdlfUNXO8rL5zPl+lfJEGfeU1ha6eOwNtFXf0kPGVSVU6BoFbpPl85cM06CuvKapqZHwfUyT2ZmZSJGOC7D6T188+UUVQgI05KX3PDaCUt2jQV14zkLtx3QXYbSxIjfaZvP7ugkrmpUT1Oe2jUt6kQV95RXNbO6W1zSRHjxlUOVlpsRwqqaG2qdWimg1MY0s7XxSd1f75yudp0FdeUVLtvPg6kJ477i5Kj8Vh4LOT1VZUa8D2F1bT2m70Tlzl8zToK68oGkQffXfzU6Ox24SPj5dbUa0B6xgHKDNVW/rKt2nQV14xkHH0uxMeHMDl0xP54/YC9p3yXmt/d0EV08ZGEDXG8yGilfIGDfrKK4qqGxGBcVED773T4efXzSUxMpi7/ryH8rpmC2rXP+0Ow94TVdo/X40IGvSVVxRVNzI2IoSggMH/F4wJC+L3N2dSWd/CD1/YS1u7w4Iaeu7w6Vpqm9t0vB01ImjQV15RVNU46Iu47mYnR/Gz6+awK7+Sn72da1m5nui4OUxb+mok0KCvvGKg4+j35psLUlh7cTrrt+ezYX+xpWX3ZndBFUlRIZa/H6WGgkdBX0RWichhETkmIg92s32tiJSJyD7X4w63be1u6zdYWXk1MjkchpKzjQMaUrkv/3L1DC5Kj+GB1w9wqKTG8vK7MsawO985yJpOgq5Ggj6DvojYgXXAVcBM4EYRmdnNrq8YY+a7Huvd1je6rV9tTbXVSFZa20xru7E0vdMhKMDGupsWEhESwPf/vIezDUN701ZhVSOna5q0f74aMTxp6S8Cjhlj8owxLcDLwDVDWy3lzzr66KcMUTokMSKE/3dzJsXVjfz4lc+GdErFznx+ml7EVSODJ0E/GTjltlzoWtfVdSJyQEReF5EJbutDRCRbRHaKyDcGU1nlHwYzjr6nMtNi+OnXZ7H1cBmPvZNLXXPbkPyd3QVVRAQHMG1cxJCUr5TVAiwq5y3gJWNMs4h8D3gOuNy1Lc0YUyQiE4EtIvK5Mea4+8EicidwJ0BqaqpFVVK+ajDTJPbHzYtTOXCqmqc+zGP99nxmJ0exZGIsSybGkZUWQ0TI4G+kyi6oZGFaDHadNEWNEJ4E/SLAveWe4lrXyRhT4ba4Hvi527Yi13OeiGwDFgDHuxz/NPA0QFZW1tD9Flc+oai6gajQQMKDrWpzdE9EeOy6uXxjQTI78yrYmVfBs9vzeeqDPGwCc5KjWDIxjuuzJjB5ANMbVje0cORMHavnjR+C2is1NDz51O0GpohIBs5g/23gO+47iEiSMabEtbgaOORaHwM0uH4BxAPLcPtCUKNTUZX13TV7YrMJyybHs2xyPOAcDXPvySp25VWwM6+SP35cwPrt+dy4aAI//tJU4sM9n8WrY9IUHVlTjSR9Bn1jTJuI3A1sAuzAs8aYHBF5BMg2xmwA7hGR1UAbUAmsdR0+A3hKRBw4rx/8zBhzcAjehxpBiqubSI0b3JDKAxUaZD/vS6C8rpnfvH+UF3ad5K+fFXPXZZO4fVkGoUF9j4m/u6CKQLswLyV6qKutlGXEGN/KpmRlZZns7GxvV0MNEWMMc/79XdZkpvDvq2d5uzqdjpfV8bO3c9l88AzjIkO4/yvTuHZBcq9TOa75fztoN4Y3f7BsGGuqVPdEZI8xJquv/YY2qapUFzWNbdQ1t/nc3auTEsL5w61Z7Mqr4D83HuL+1/bz7PZ8brs4rduZsAyGA4VnWbssffgrq9QgaNBXw6qwugEY2u6ag7F4Yhxv/mAZbx0o5ufvHOaBNz7vdf9LXGkipUYKDfpqWFk1jv5QstmEa+Ync9XspM57CroTHGAb8m6nSllNg74aVsXDcGOWVYICbGTEh3m7GkpZSkfZVMOqqLqR4AAbcWFB3q6KUqOSBn01rDqGVNYRKZXyDg36alhZPXmKUqp/NOirYTUUk6copTynF3LVsGlqbae8rkWD/mjSUg9v3Qt1pRCTBjHpEJ0GMRnO5TFxoKm+YaVBfwi1Owz/viGHVbPHdd72P5qNpJ47ygLtbfD67XD0XRi/AA6/DfVl5+8TFA5xkyBlEUxYDBMucn4p6BfBkNGgP4Se+vA4/73zBMXVjRr0OTeOvvZtHwWMgY33w5F34Ku/gotcM6g210H1Sag+AVUFUHUCSg/C/pdg9x+c+4SPhQmuL4GURZA0FwL1/4xVNOgPkYPFNfx68xGC7DY+yaugpc1BUMDovoQyEm7MUhbZ/jjs+SMs+/G5gA8QHA5jZzof7hztzuB/ahec2u18PvSWc5vYIX4qJM079xg3B0Iih+/9+BEN+kOgua2d+17dR1RoED9ZNY2fvH6A7BOVXDxpdLf2i6obsQmMiwrxdlXUUNr/Crz/CMy5Hq542LNjbHZnIB8359yXRF0pFO6Gkv3OR/4HcODlc8fEToTEmRA32Zkiip3kfA4fq+mhXmjQHwJPvneU3NO1PHNbFosnxvEvf/mcD4+Ua9CvamRcZAiB9tH9i8ev5X0Af/shpC+Ha9aBbRD/1uGJMP2rzkeHulIoOQAl+5xfBGW5zmsG7S3n9gkKh9gM55dCZDJEJLke45zPkUkQNHrvtNagb7E9Jyr5/QfHuSFrAlfMGAs452v98EgZD1413cu1866iau2j79fO5MArNztb3jf8GQI8n5DGY+GJMOVLzkcHRzucPQUVx6Eyz/lcccxZn6PvQWv9heUER0JYPITGQEi089n9ERLlTEUFhUFgmPM5KMz5hRIU5rzGYOt7zgVfpEHfQg0tbfzzq/tJigrl3742o3P9iqkJ/GLTYcpqm0mIGIIPwghRVN1IZlqMt6uhhsLZIvjzGmdAvPl1CB3GiWVsdmdX0Jh04IoLtzfVQO1pqC1xey6BhgporHI+qvJdr6sBD+cYsQWAPdj55RYQAgFBzmd7INgC3Z4D3JYDXA+781nszl9DHa9j0uDiH1l3brqhQd9CP3s7l4KKBl767pLzJt1eMcUZ9D86Wsa1C1O8WEPvaXcYTp9t0ou4/qjpLLywBppr4fZ3IMrH/o+HRDofCVP73tfhgOYaaKp23mPQUg8tddDS4Pa6HtqaoK35wuf2ZtdzKzhand1WW+pdy23OZ9Pu/HXS8dz5us15kVqD/sjw0dEynv/kBLcvy2DppLjzts0aH0lcWBAfHhm9Qf90TRNtDqPpHX904FUoPwI3vQ7jZnu7NoNjszl/pQznL5VhpkHfAmcbWvlfrx1gUkIYP1k17YLtNptwyZR4th8rx+EwvU7B56/2n6oGYEaSdrPzOxfdAWnLLuyGqXySdqOwwL+/lUNZXTO/vmE+IYHdX9xZMSWB8roWDpbUDHPtfMOuvArGBNmZkxzl7aooq4lowB9BNOgP0o5j5bz5WRE/XDmZuSk9/yRcPtXZXfPDo2U97uPPduZVkpUeq901lfIy/QQO0meutMX3Vkzsdb/EiBBmJEXy4ZHRF/Qr6po5fKaWJRNjvV0VpUY9DfqDVFjVQFxYEGHBfV8eWTElnj0nqqhvbhuGmvmOT/MrAVicEdfHnkqpoaZBf5AK+zGvJU+OAAAV+klEQVQpyIqpCbS2Gz45XjHEtfItO/MqCA20MzdF8/lKeZsG/UHqz6QgWekxhAbaR11ef1d+JVnpMZrPV8oH6KdwEIwxFFU1kuJhSz84wM6SibGjKq9fWd9C7ulalkzU1I5SvkCD/iCU17XQ3Obo112mK6YmUFDRwMmKhiGsme/4NN+ZytKLuEr5Bg36g1DUORPUGI+PWTE1ARg9XTd35lUSGmhnTrL/3uGo1EiiQX8QCqucrXVP0zsAE+PDSI4OHTUpnp15FWSlx4z6CWSU8hX6SRyEzpmg+hH0RYQVU+PZcbyC1nbHUFXNJ1RpPl8pn+NR0BeRVSJyWESOiciD3WxfKyJlIrLP9bijy/ZIESkUkf+yquK+oKi6kYiQACLdRtT0xIopCdQ1t/HZyeohqplv2NXZP1/z+Ur5ij6DvojYgXXAVcBM4EYR6W6gjVeMMfNdj/Vdtv1f4MNB19bHFFY1ktKPfH6HiyfHY7eJ36d4duZVEBJo63V4CqXU8PKkpb8IOGaMyTPGtAAvA9d4+gdEJBMYC7w7sCr6rqIqz/vou4sKDWT+hOgRdzG33WF4Y08hJWcbPdp/Z14FWWmxms9Xyod48mlMBk65LRe61nV1nYgcEJHXRWQCgIjYgF8B9w+6pj7GGENRted99LtaMSWBz4vOUlnf0vfOPqC0tolbntnFP7+2n5+8fqDP/asbWnS8HaV8kFVNsLeAdGPMXGAz8Jxr/Q+AjcaYwt4OFpE7RSRbRLLLykZG6/dsYyt1zW0DD/pT4zEGth8rt7hm1vvkeAVf/c129pyo4kszEvnoaHnneDo92ZVfiTHoRVylfIwnQb8ImOC2nOJa18kYU2GMaXYtrgcyXa+XAneLSAHwS+BWEflZ1z9gjHnaGJNljMlKSEjo51vwjsKOnjsDnP5vbko0UaGBPp3XdzgM/7XlKDet30lESAB/u3sZv71xIQkRwfzy3cMY0/NcoprPV8o3eRL0dwNTRCRDRIKAbwMb3HcQkSS3xdXAIQBjzE3GmFRjTDrOFM/zxpgLev+MROduzBpY0LfbhEsmx/PR0bJeg6e3VNa3sPZPu/nlu0f4+rzxbLj7EqaPiyQ0yM4PL5vEp/mVfHys54HjduZVkpmm/fOV8jV9fiKNMW3A3cAmnMH8VWNMjog8IiKrXbvdIyI5IrIfuAdYO1QV9hUdLf2B9N7psGRSHGdqmjvL8hXZBZVc/eRH7Myr4D++OZsnbphPuNvQ0TcuTmV8VAi/2tx9a7+6oYXc0zUs0aGUlfI5Hs2Ra4zZCGzssu6nbq8fAh7qo4w/AX/qdw19VFFVI6GBdmLG9K+PvruFqc7Ux54TVUyIHfiXh5XeP3SGO/97DykxofzlrouZ3c30hsEBdn50xRQe+svnbD1cyuXTx563/dOOfP4kDfpK+Rr97T1ARdUNJMeEIjLwSc6njY0gLMjO3pNVFtZs4FraHDzy94NMSgjjrR9d0m3A77AmM4XU2DH86t0jF7T2d+ZVEhxg0/HzlfJBGvQHaDDdNTsE2G3MmxDNnhO+EfRf+vQkJyoaeOjqGX3eZRxot3HvFVPIKa5hU87p87btzKsgMy2G4IDuJ4lXSnmPBv0BKhzgjVldZabFkHu61utTKNY2tfLk+0dZOjGOy6Z61oPqGwuSmZgQxuObj9DucLb2qxtaOHS6RrtqKuWjNOgPQH1zG9UNrQPuueNuYWoM7Q7D/kLvjsPz1Ad5VNa38NDV0z1OWdltwj99aSpHztTx9wPFgFs+X4O+Uj5Jg/4AdHTXHEzPnQ4LXBdzvTn42pmaJtZvz2P1vPH97lf/1TlJTB8XwRPvHaWt3cGufGc+f94Ezecr5Ys06A9Axzj6VqR3oscEMSkhzKt5/V+70jP/6yvT+n2szSb805VTyS+v583PitiZV8HCVM3nK+WrNOgPQFFnH/3BB31w5vX3nqzyyk1aR8/U8mr2KW5Zkj7gbqNfnjmWOclRPL75CAdLNJ+vlC/ToD8AhdWNBNltJIQHW1LewtQYqhtaySuvt6S8/njsnVzCggK4+/LJAy5DRLjvy1MpOdvkyufrIGtK+SoN+gNQWNXI+OgQbLaB99F3l5kWA8DeYU7x7Mqr4L1Dpdy1chKxYUGDKuuyqQmubprObqhKKd/k0R256nxFVY2W9NzpMCkhnMiQAPaerOL6rAl9H2ABYwz/+XYu4yJDuH1ZxqDLExGe/PZ8TlU2EhKo+XylfJW29AegqLqRlGjrhk2w2YQFqTHsPTF8PXg2fn6a/aeque/LUy0L0ikxY1iqQy8o5dM06PdTU2s7ZbXNlrb0wZnXP1JaS01Tq6XldqelzcHPN+UybWwE1y1MGfK/p5TyHRr0+6m4enDj6PckMy0GY2DfMPTX7xhu4cGrpmO36LqEUmpk0KDfT+duzLI26M+bEIVNGPL++mdqmnh88xGWTIzlsmkjY8IapZR1NOj3U+eMWRYH/YiQQKaOjRjSETeNMTzwxgGa29r5j2/OGdQIoUqpkUmDfj8VVTVitwnjIkMsLzszLYZ9J6txOIbmJq2XPj3FtsNlPLhqOpMSwofkbyilfJsG/X4qqm5kXGQIAXbrT93C1Bhqm9s4WlpnedknKxp49B8HuXhSHLcuTbe8fKXUyKBBv58KqxosT+106LxJy+IUT7vDcP9r+7GL8Ivr51l2U5lSauTRoN9PRVWNpFjcc6dDWtwYYsOCLL+Y+8z2PD4tqOTh1bMs73WklBpZNOj3Q2u7g9M1TZb33OkgIixMjbG0pX/kTC2/3HSEK2eO5bqFyZaVq5QamTTo98Pps004jPU9d9wtTIsmr6yeqvqWQZfV0ubgn17ZR0RIAP/ftdpbRymlQb9fOrtrWjgEQ1eZqc68/menBt/a/68tR8kpruE/vjmHeItGBFVKjWwa9PthqG7Mcjc3JZoAmww6r7//VDXrth3n2gXJrJo9zqLaKaVGOg36/dAxeUpStPV99DuEBtmZOT5yUIOvNbW2c9+r+0iMCObh1bMsrJ1SaqTToN8PhVUNJEYED/lUgAtTY9h3qpq2dseAjn/+kwKOl9Xz2HVziQoNtLZySqkRTYN+PxRVNw5paqfDwrQYGlvbyT1d2+9jG1raeOqDPJZPiWfFVB1bRyl1Pg36/VBU3UhyzNBdxO2wMNU589RAum7+9ycnqKhv4cdfmmJ1tZRSfkCDvoccDkNxdeOw3NyUHB3K2Mjgfk+f2NDSxlMfOlv5mWk6T61S6kIa9D1UWttMa7sZlvROx01ae/rZ0n/+kxNU1rfw4y9NHaKaKaVGOg36HiqqbgCG9sYsd5lpMZyqbKS0tsmj/eub23j6wzxWuCYoV0qp7ngU9EVklYgcFpFjIvJgN9vXikiZiOxzPe5wrU8Tkb2udTki8n2r38Bw6bgxa6jG3elqgesmrfcPlXq0/7lWvubylVI9C+hrBxGxA+uAK4FCYLeIbDDGHOyy6yvGmLu7rCsBlhpjmkUkHPjCdWyxFZUfTkM1eUpP5qVEsTA1moc35DApIZxFGT3n6J2t/ONcOjWBhanayldK9cyTlv4i4JgxJs8Y0wK8DFzjSeHGmBZjTLNrMdjDv+eTiqobiQ0LYkxQn9+Tlgiw23jmtotIiQnljud2c7iX7pvPf3KCqoZWbeUrpfrkSRBOBk65LRe61nV1nYgcEJHXRWRCx0oRmSAiB1xlPDYSW/ngbOkP97DEMWFBPH/7IkKD7Nz27Kedw0C4q3O18i+bltCZElJKqZ5Y1fJ+C0g3xswFNgPPdWwwxpxyrZ8M3CYiY7seLCJ3iki2iGSXlZVZVCVrFVU1DEvPna5SYsbw3O2LqG9p47ZnP6W64fzRN5//pICqhlbuvUJb+UqpvnkS9IuACW7LKa51nYwxFW5pnPVAZtdCXC38L4Dl3Wx72hiTZYzJSkjwvbtIjTHOG7O8NAHJ9HGR/OHWLE5WNnD7n3bT2NIOOFv5f/gwT1v5SimPeRL0dwNTRCRDRIKAbwMb3HcQkSS3xdXAIdf6FBEJdb2OAS4BDltR8eFUUd9CU6tj2C7idmfJxDievGE+n52q5kcv7aWt3cFzOwpcuXztl6+U8kyfVyWNMW0icjewCbADzxpjckTkESDbGLMBuEdEVgNtQCWw1nX4DOBXImIAAX5pjPl8CN7HkOoYXTNlGIZg6M1Vc5J4ZPUs/vffcvjJGwfYklvKymkJzJ8Q7dV6KaVGDo+6ohhjNgIbu6z7qdvrh4CHujluMzB3kHX0uo4LqL4wv+wtS9MprW3mt1uOAXCvtvKVUv0wPP0PR7jCquG9G7cv9105ldZ2Q0ubQ1v5Sql+0aDvgaKqRiJCAnxmbHoR4cGrpnu7GkqpEWjE3iw1nLzZc0cppaykQd8Dx8vqSY317kVcpZSyggb9PpyqbCC/vJ4lE+O8XRWllBo0Dfp92HbYOcrlZdN876YxpZTqLw36ffjgSBmpsWPIiA/zdlWUUmrQNOj3ormtnR3HK7hsWgIi4u3qKKXUoGnQ78Xu/CoaWtq5dKqmdpRS/mHUBP3H3snl1mc/7dcx2w6XEmS3sXSSXsRVSvmHURP039pfzIdHyjhUUuPxMduOlLF4YuywTZyilFJDbVQE/aLqxs7pDt/YU+jRMYVVDRwrrdPUjlLKr4yKoL8rrwKASQlh/HVfEa3tjj6P2XbYOZnLZdMSh7RuSik1nEZJ0K8kKjSQn6yaTnldCx8e6Xt2rm2Hy0iJCWVSgnbVVEr5j9ER9PMruCg9lsunJxIXFsTrfaR4Wtoc7DhezqVTtaumUsq/+H3QP1PTREFFA0smxhJot3HN/GTeO3SGqvqWHo/JLqikoaVdUztKKb/j90F/pyufvzjD2e1yTWYKre2GDfuLezxm25Eyguw2LtaumkopP+P3QX9XfiURwQHMHB8JwMzxkcxMiuSNvT2neLYdLuWijBjCgrWrplLKv/h/0M+rICs9BrvtXG5+TWYKBwrPcvh07QX7F1c3cuRMHZdN1dSOUsr/+HXQL6tt5nhZPYu7DIt8zfzxBNik29b+ua6a2j9fKeV//Drof5pfCcDijNjz1seFB7NyeiJ/2VtEW5c++9sOl5IcHcrkxPBhq6dSSg0Xvw76u/IrGBNkZ3Zy1AXb1mSmUF7XzEdHyzvXtbQ5+PhYOSu0q6ZSyk/5d9DPqyQzLYZA+4Vvc+W0RGK79NnPPlFJfUu7pnaUUn7Lb4N+ZX0Lh8/U9jjNYVCAjWvmj2fzwTNUNzj77H9wpIxAu7BscvxwVlUppYaN3wb9nvL57tZkptDS7uAtV5/9Dw6XkZUWS7h21VRK+Sm/Dfq78isIDrAxNyW6x31mjY9i+rgIXt9bRMnZRnJP12pqRynl1/w36OdVsjA1hqCA3t/imswU9p+q5pmP8gEdVVMp5d/8MuifbWjl0OkaFk/sObXT4RsLkgmwCc98nM+4yBCmjtWumkop/+WXQX93QSXGnBtvpzfx4cFcNi0RY9AJ0JVSfs8vg/6u/AqC7DYWpPacz3d3fVYKAJdP19SOUsq/eRT0RWSViBwWkWMi8mA329eKSJmI7HM97nCtny8in4hIjogcEJEbrH4D3dmVX8n8CdGEBNo92v/LM8fyxl1LuXLm2CGumVJKeVeffRNFxA6sA64ECoHdIrLBGHOwy66vGGPu7rKuAbjVGHNURMYDe0RkkzGm2orKd6e2qZUvis7yw5WTPT5GRMhM6zv/r5RSI50nLf1FwDFjTJ4xpgV4GbjGk8KNMUeMMUddr4uBUmBI+0TuOVGFw8N8vlJKjTaeBP1k4JTbcqFrXVfXuVI4r4vIhK4bRWQREAQcH1BNPbQrv5IAm7AwzbN8vlJKjSZWXch9C0g3xswFNgPPuW8UkSTgv4H/YYxxdD1YRO4UkWwRyS4r63vS8t7syqtgbkoUY4L0rlqllOrKk6BfBLi33FNc6zoZYyqMMc2uxfVAZsc2EYkE/gH8qzFmZ3d/wBjztDEmyxiTlZAw8OxPQ0sbBwrPXjB+vlJKKSdPgv5uYIqIZIhIEPBtYIP7Dq6WfIfVwCHX+iDgTeB5Y8zr1lS5Z3tPVNPmML2Ot6OUUqNZnzkQY0ybiNwNbALswLPGmBwReQTINsZsAO4RkdVAG1AJrHUd/i1gBRAnIh3r1hpj9ln7Npx25VdgtwlZ6Rr0lVKqOx4lvo0xG4GNXdb91O31Q8BD3Rz3Z+DPg6yjx3blVTJ7fKSOkqmUUj3wmztym1rb2XeqWvP5SinVC78J+jVNrayaPY7LpurQyEop1RO/yYMkRoTwmxsXeLsaSinl0/ympa+UUqpvGvSVUmoU0aCvlFKjiAZ9pZQaRTToK6XUKKJBXymlRhEN+kopNYpo0FdKqVFEjDHersN5RKQMODGIIuKBcouq48/0PHlGz5Nn9Dx5bqjOVZoxps8hCXwu6A+WiGQbY7K8XQ9fp+fJM3qePKPnyXPePlea3lFKqVFEg75SSo0i/hj0n/Z2BUYIPU+e0fPkGT1PnvPqufK7nL5SSqme+WNLXymlVA/8JuiLyCoROSwix0TkQW/Xx5eIyLMiUioiX7itixWRzSJy1PUc4806+gIRmSAiW0XkoIjkiMi9rvV6rtyISIiIfCoi+13n6f+41meIyC7XZ/AVEQnydl19gYjYReQzEfm7a9mr58kvgr6I2IF1wFXATOBGEZnp3Vr5lD8Bq7qsexB43xgzBXjftTzatQH/bIyZCSwBfuj6f6Tn6nzNwOXGmHnAfGCViCwBHgN+bYyZDFQB/9OLdfQl9wKH3Ja9ep78IugDi4Bjxpg8Y0wL8DJwjZfr5DOMMR8ClV1WXwM853r9HPCNYa2UDzLGlBhj9rpe1+L8oCaj5+o8xqnOtRjoehjgcuB11/pRf54ARCQF+Cqw3rUsePk8+UvQTwZOuS0Xutapno01xpS4Xp8GxnqzMr5GRNKBBcAu9FxdwJWy2AeUApuB40C1MabNtYt+Bp2eAH4COFzLcXj5PPlL0FeDYJxduLQbl4uIhANvAD82xtS4b9Nz5WSMaTfGzAdScP7Snu7lKvkcEfkaUGqM2ePturjzl4nRi4AJbssprnWqZ2dEJMkYUyIiSThbbKOeiATiDPgvGGP+4lqt56oHxphqEdkKLAWiRSTA1YrVzyAsA1aLyNVACBAJPImXz5O/tPR3A1NcV8WDgG8DG7xcJ1+3AbjN9fo24G9erItPcOVbnwEOGWMed9uk58qNiCSISLTrdShwJc7rH1uBNa7dRv15MsY8ZIxJMcak44xJW4wxN+Hl8+Q3N2e5vk2fAOzAs8aY//BylXyGiLwEXIZzdL8zwMPAX4FXgVSco5p+yxjT9WLvqCIilwAfAZ9zLgf7Lzjz+nquXERkLs4LkHacDcdXjTGPiMhEnJ0oYoHPgJuNMc3eq6nvEJHLgPuNMV/z9nnym6CvlFKqb/6S3lFKKeUBDfpKKTWKaNBXSqlRRIO+UkqNIhr0lVJqFNGgr5RSo4gGfaWUGkU06Cul1Cjy/wMVAzZVQDLhVQAAAABJRU5ErkJggg==\n", 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "#Prediction on live data!\n", + "\"\"\"\n", "starttime=time.time()\n", "while True:\n", " now = time.time() \n", " end = now - now % period\n", - " print time.strftime(\"%a, %d %b %Y %H:%M:%S +0000\", time.gmtime(end))\n", + " print(time.strftime(\"%a, %d %b %Y %H:%M:%S +0000\", time.gmtime(end)))\n", " rt_x, prediction = predict()\n", "\n", " current = rt_x[:,0]\n", @@ -1430,34 +2115,34 @@ " pyplot.legend()\n", " pyplot.show()\n", "\n", - " time.sleep(period - ((time.time() - starttime) % period))" + " time.sleep(period - ((time.time() - starttime) % period))\"\"\"" ] }, { - "cell_type": "code", - "execution_count": null, + "cell_type": "markdown", "metadata": {}, - "outputs": [], - "source": [] + "source": [ + "###### " + ] } ], "metadata": { "kernelspec": { - "display_name": "Python 2", + "display_name": "Python 3", "language": "python", - "name": "python2" + "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", - "version": 2 + "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", - "pygments_lexer": "ipython2", - "version": "2.7.12" + "pygments_lexer": "ipython3", + "version": "3.6.6" } }, "nbformat": 4, diff --git a/weights 1-48/.gitignore b/weights 1-48/.gitignore new file mode 100644 index 0000000..76bedae --- /dev/null +++ b/weights 1-48/.gitignore @@ -0,0 +1,5 @@ +# Ignore everything in this directory +* +# Except this file +!.gitignore + diff --git a/weights 2-24/.gitignore b/weights 2-24/.gitignore new file mode 100644 index 0000000..76bedae --- /dev/null +++ b/weights 2-24/.gitignore @@ -0,0 +1,5 @@ +# Ignore everything in this directory +* +# Except this file +!.gitignore + diff --git a/weights 3-31/.gitignore b/weights 3-31/.gitignore new file mode 100644 index 0000000..76bedae --- /dev/null +++ b/weights 3-31/.gitignore @@ -0,0 +1,5 @@ +# Ignore everything in this directory +* +# Except this file +!.gitignore + diff --git a/weights 4-32/.gitignore b/weights 4-32/.gitignore new file mode 100644 index 0000000..76bedae --- /dev/null +++ b/weights 4-32/.gitignore @@ -0,0 +1,5 @@ +# Ignore everything in this directory +* +# Except this file +!.gitignore + diff --git a/weights addforward/.gitignore b/weights addforward/.gitignore new file mode 100644 index 0000000..76bedae --- /dev/null +++ b/weights addforward/.gitignore @@ -0,0 +1,5 @@ +# Ignore everything in this directory +* +# Except this file +!.gitignore + diff --git a/weights addforward2/.gitignore b/weights addforward2/.gitignore new file mode 100644 index 0000000..76bedae --- /dev/null +++ b/weights addforward2/.gitignore @@ -0,0 +1,5 @@ +# Ignore everything in this directory +* +# Except this file +!.gitignore + diff --git a/weights origtri/.gitignore b/weights origtri/.gitignore new file mode 100644 index 0000000..76bedae --- /dev/null +++ b/weights origtri/.gitignore @@ -0,0 +1,5 @@ +# Ignore everything in this directory +* +# Except this file +!.gitignore + From 5ff940a5010aa6aecadc9046003812c506f21d3b Mon Sep 17 00:00:00 2001 From: sidneyriffic <31529542+sidneyriffic@users.noreply.github.com> Date: Sun, 1 Jul 2018 08:02:27 -0700 Subject: [PATCH 2/3] Change weights to proper feature --- weights/.gitignore | 5 ----- ..._i300_o12_Close_Volume_High_Low_model.json | 1 + ...o12_Close_Volume_High_Low_model_weights.h5 | Bin 0 -> 304080 bytes ...m_i30_o12_Close_Volume_Low_High_model.json | 1 + ...o12_Close_Volume_Low_High_model_weights.h5 | Bin 0 -> 92520 bytes ...Close_Volume_Low_High_model_weights.h5asdf | Bin 0 -> 159560 bytes ...0_o12_Close_Volume_Low_High_modelasdf.json | 1 + ...m_i48_o12_Close_Volume_High_Low_model.json | 1 + ...o12_Close_Volume_High_Low_model_weights.h5 | Bin 0 -> 1023936 bytes ...m_i60_o12_Close_Volume_High_Low_model.json | 1 + ...o12_Close_Volume_High_Low_model_weights.h5 | Bin 0 -> 515208 bytes ...C_ETH_lstm_i60_o12_Close_Volume_model.json | 1 + ...lstm_i60_o12_Close_Volume_model_weights.h5 | Bin 0 -> 790112 bytes weights/BTC_ETH_lstm_i60_o12_Close_model.json | 1 + ...TC_ETH_lstm_i60_o12_Close_model_weights.h5 | Bin 0 -> 297432 bytes ...m_i96_o12_Close_Volume_High_Low_model.json | 1 + ...o12_Close_Volume_High_Low_model_weights.h5 | Bin 0 -> 304080 bytes 17 files changed, 8 insertions(+), 5 deletions(-) delete mode 100644 weights/.gitignore create mode 100644 weights/BTC_ETH_lstm_i300_o12_Close_Volume_High_Low_model.json create mode 100644 weights/BTC_ETH_lstm_i300_o12_Close_Volume_High_Low_model_weights.h5 create mode 100644 weights/BTC_ETH_lstm_i30_o12_Close_Volume_Low_High_model.json create mode 100644 weights/BTC_ETH_lstm_i30_o12_Close_Volume_Low_High_model_weights.h5 create mode 100644 weights/BTC_ETH_lstm_i30_o12_Close_Volume_Low_High_model_weights.h5asdf create mode 100644 weights/BTC_ETH_lstm_i30_o12_Close_Volume_Low_High_modelasdf.json create mode 100644 weights/BTC_ETH_lstm_i48_o12_Close_Volume_High_Low_model.json create mode 100644 weights/BTC_ETH_lstm_i48_o12_Close_Volume_High_Low_model_weights.h5 create mode 100644 weights/BTC_ETH_lstm_i60_o12_Close_Volume_High_Low_model.json create mode 100644 weights/BTC_ETH_lstm_i60_o12_Close_Volume_High_Low_model_weights.h5 create mode 100644 weights/BTC_ETH_lstm_i60_o12_Close_Volume_model.json create mode 100644 weights/BTC_ETH_lstm_i60_o12_Close_Volume_model_weights.h5 create mode 100644 weights/BTC_ETH_lstm_i60_o12_Close_model.json create mode 100644 weights/BTC_ETH_lstm_i60_o12_Close_model_weights.h5 create mode 100644 weights/BTC_ETH_lstm_i96_o12_Close_Volume_High_Low_model.json create mode 100644 weights/BTC_ETH_lstm_i96_o12_Close_Volume_High_Low_model_weights.h5 diff --git a/weights/.gitignore b/weights/.gitignore deleted file mode 100644 index 76bedae..0000000 --- a/weights/.gitignore +++ /dev/null @@ -1,5 +0,0 @@ -# Ignore everything in this directory -* -# Except this file -!.gitignore - diff --git a/weights/BTC_ETH_lstm_i300_o12_Close_Volume_High_Low_model.json b/weights/BTC_ETH_lstm_i300_o12_Close_Volume_High_Low_model.json new file mode 100644 index 0000000..fcdfe25 --- /dev/null +++ b/weights/BTC_ETH_lstm_i300_o12_Close_Volume_High_Low_model.json @@ -0,0 +1 @@ +{"class_name": "Model", "config": {"name": "model_1", "layers": [{"name": "input_1", "class_name": "InputLayer", "config": {"batch_input_shape": [null, 300, 4], "dtype": "float32", "sparse": false, "name": "input_1"}, "inbound_nodes": []}, {"name": "conv1d_1", "class_name": "Conv1D", "config": {"name": "conv1d_1", "trainable": true, "filters": 15, "kernel_size": [3], "strides": [1], "padding": "valid", "data_format": "channels_last", "dilation_rate": [1], "activation": "linear", "use_bias": true, "kernel_initializer": {"class_name": "VarianceScaling", "config": {"scale": 1.0, "mode": "fan_avg", "distribution": "uniform", "seed": null}}, "bias_initializer": {"class_name": "Zeros", "config": {}}, "kernel_regularizer": 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...o12_Close_Volume_Low_High_model_weights.h5 | Bin 0 -> 92520 bytes ...Close_Volume_Low_High_model_weights.h5asdf | Bin 0 -> 159560 bytes ...0_o12_Close_Volume_Low_High_modelasdf.json | 1 + ...m_i48_o12_Close_Volume_High_Low_model.json | 1 + ...o12_Close_Volume_High_Low_model_weights.h5 | Bin 0 -> 1023936 bytes ...m_i60_o12_Close_Volume_High_Low_model.json | 1 + ...o12_Close_Volume_High_Low_model_weights.h5 | Bin 0 -> 515208 bytes ...C_ETH_lstm_i60_o12_Close_Volume_model.json | 1 + ...lstm_i60_o12_Close_Volume_model_weights.h5 | Bin 0 -> 790112 bytes .../BTC_ETH_lstm_i60_o12_Close_model.json | 1 + ...TC_ETH_lstm_i60_o12_Close_model_weights.h5 | Bin 0 -> 297432 bytes ...m_i96_o12_Close_Volume_High_Low_model.json | 1 + ...o12_Close_Volume_High_Low_model_weights.h5 | Bin 0 -> 304080 bytes ...o12_Close_Volume_High_Low_model_weights.h5 | Bin 1023936 -> 1023936 bytes 18 files changed, 26 insertions(+), 24 deletions(-) create mode 100644 weights 5-13/BTC_ETH_lstm_i300_o12_Close_Volume_High_Low_model.json create mode 100644 weights 5-13/BTC_ETH_lstm_i300_o12_Close_Volume_High_Low_model_weights.h5 create mode 100644 weights 5-13/BTC_ETH_lstm_i30_o12_Close_Volume_Low_High_model.json create mode 100644 weights 5-13/BTC_ETH_lstm_i30_o12_Close_Volume_Low_High_model_weights.h5 create mode 100644 weights 5-13/BTC_ETH_lstm_i30_o12_Close_Volume_Low_High_model_weights.h5asdf create mode 100644 weights 5-13/BTC_ETH_lstm_i30_o12_Close_Volume_Low_High_modelasdf.json create mode 100644 weights 5-13/BTC_ETH_lstm_i48_o12_Close_Volume_High_Low_model.json create mode 100644 weights 5-13/BTC_ETH_lstm_i48_o12_Close_Volume_High_Low_model_weights.h5 create mode 100644 weights 5-13/BTC_ETH_lstm_i60_o12_Close_Volume_High_Low_model.json create mode 100644 weights 5-13/BTC_ETH_lstm_i60_o12_Close_Volume_High_Low_model_weights.h5 create mode 100644 weights 5-13/BTC_ETH_lstm_i60_o12_Close_Volume_model.json create mode 100644 weights 5-13/BTC_ETH_lstm_i60_o12_Close_Volume_model_weights.h5 create mode 100644 weights 5-13/BTC_ETH_lstm_i60_o12_Close_model.json create mode 100644 weights 5-13/BTC_ETH_lstm_i60_o12_Close_model_weights.h5 create mode 100644 weights 5-13/BTC_ETH_lstm_i96_o12_Close_Volume_High_Low_model.json create mode 100644 weights 5-13/BTC_ETH_lstm_i96_o12_Close_Volume_High_Low_model_weights.h5 diff --git a/crypto_predict.ipynb b/crypto_predict.ipynb index 8159e03..a32de88 100644 --- a/crypto_predict.ipynb +++ b/crypto_predict.ipynb @@ -1362,7 +1362,7 @@ }, { "cell_type": "code", - "execution_count": 52, + "execution_count": null, "metadata": { "scrolled": false }, @@ -1373,45 +1373,39 @@ "text": [ "Train on 23618 samples, validate on 5905 samples\n", "Epoch 1/10\n", - "23618/23618 [==============================] - 27s 1ms/step - loss: 0.0026 - val_loss: 0.0010\n", + "23618/23618 [==============================] - 26s 1ms/step - loss: 0.0017 - val_loss: 1.7750e-04\n", "\n", - "Epoch 00001: val_loss improved from inf to 0.00101, saving model to weights/BTC_ETH_lstm_i48_o12_Close_Volume_High_Low_model_weights.h5\n", + "Epoch 00001: val_loss improved from inf to 0.00018, saving model to weights/BTC_ETH_lstm_i48_o12_Close_Volume_High_Low_model_weights.h5\n", "Epoch 2/10\n", - "23618/23618 [==============================] - 26s 1ms/step - loss: 0.0025 - val_loss: 7.9232e-04\n", + "23618/23618 [==============================] - 26s 1ms/step - loss: 0.0016 - val_loss: 1.4801e-04\n", "\n", - "Epoch 00002: val_loss improved from 0.00101 to 0.00079, saving model to weights/BTC_ETH_lstm_i48_o12_Close_Volume_High_Low_model_weights.h5\n", + "Epoch 00002: val_loss improved from 0.00018 to 0.00015, saving model to weights/BTC_ETH_lstm_i48_o12_Close_Volume_High_Low_model_weights.h5\n", "Epoch 3/10\n", - "23618/23618 [==============================] - 25s 1ms/step - loss: 0.0022 - val_loss: 7.3845e-04\n", + "23618/23618 [==============================] - 26s 1ms/step - loss: 0.0016 - val_loss: 1.3597e-04\n", "\n", - "Epoch 00003: val_loss improved from 0.00079 to 0.00074, saving model to weights/BTC_ETH_lstm_i48_o12_Close_Volume_High_Low_model_weights.h5\n", + "Epoch 00003: val_loss improved from 0.00015 to 0.00014, saving model to weights/BTC_ETH_lstm_i48_o12_Close_Volume_High_Low_model_weights.h5\n", "Epoch 4/10\n", - "23618/23618 [==============================] - 26s 1ms/step - loss: 0.0021 - val_loss: 5.1478e-04\n", + "23618/23618 [==============================] - 27s 1ms/step - loss: 0.0016 - val_loss: 1.3395e-04\n", "\n", - "Epoch 00004: val_loss improved from 0.00074 to 0.00051, saving model to weights/BTC_ETH_lstm_i48_o12_Close_Volume_High_Low_model_weights.h5\n", + "Epoch 00004: val_loss improved from 0.00014 to 0.00013, saving model to weights/BTC_ETH_lstm_i48_o12_Close_Volume_High_Low_model_weights.h5\n", "Epoch 5/10\n", - "23618/23618 [==============================] - 26s 1ms/step - loss: 0.0022 - val_loss: 4.8799e-04\n", + "23618/23618 [==============================] - 26s 1ms/step - loss: 0.0016 - val_loss: 1.2697e-04\n", "\n", - "Epoch 00005: val_loss improved from 0.00051 to 0.00049, saving model to weights/BTC_ETH_lstm_i48_o12_Close_Volume_High_Low_model_weights.h5\n", + "Epoch 00005: val_loss improved from 0.00013 to 0.00013, saving model to weights/BTC_ETH_lstm_i48_o12_Close_Volume_High_Low_model_weights.h5\n", "Epoch 6/10\n", - "23618/23618 [==============================] - 27s 1ms/step - loss: 0.0021 - val_loss: 6.6165e-04\n", + "23618/23618 [==============================] - 26s 1ms/step - loss: 0.0016 - val_loss: 1.2844e-04\n", "\n", - "Epoch 00006: val_loss did not improve from 0.00049\n", + "Epoch 00006: val_loss did not improve from 0.00013\n", "Epoch 7/10\n", - "23618/23618 [==============================] - 29s 1ms/step - loss: 0.0021 - val_loss: 3.1704e-04\n", + "23618/23618 [==============================] - 27s 1ms/step - loss: 0.0017 - val_loss: 1.3677e-04\n", "\n", - "Epoch 00007: val_loss improved from 0.00049 to 0.00032, saving model to weights/BTC_ETH_lstm_i48_o12_Close_Volume_High_Low_model_weights.h5\n", + "Epoch 00007: val_loss did not improve from 0.00013\n", "Epoch 8/10\n", - "23618/23618 [==============================] - 28s 1ms/step - loss: 0.0021 - val_loss: 7.2353e-04\n", + "23618/23618 [==============================] - 26s 1ms/step - loss: 0.0018 - val_loss: 1.3220e-04\n", "\n", - "Epoch 00008: val_loss did not improve from 0.00032\n", + "Epoch 00008: val_loss did not improve from 0.00013\n", "Epoch 9/10\n", - "23618/23618 [==============================] - 27s 1ms/step - loss: 0.0024 - val_loss: 4.8849e-04\n", - "\n", - "Epoch 00009: val_loss did not improve from 0.00032\n", - "Epoch 10/10\n", - "23618/23618 [==============================] - 30s 1ms/step - loss: 0.0022 - val_loss: 8.4974e-04\n", - "\n", - "Epoch 00010: val_loss did not improve from 0.00032\n" + "22750/23618 [===========================>..] - ETA: 0s - loss: 0.0019 " ] } ], diff --git a/weights 5-13/BTC_ETH_lstm_i300_o12_Close_Volume_High_Low_model.json b/weights 5-13/BTC_ETH_lstm_i300_o12_Close_Volume_High_Low_model.json new file mode 100644 index 0000000..fcdfe25 --- /dev/null +++ b/weights 5-13/BTC_ETH_lstm_i300_o12_Close_Volume_High_Low_model.json @@ -0,0 +1 @@ +{"class_name": "Model", "config": {"name": "model_1", "layers": [{"name": "input_1", "class_name": "InputLayer", "config": {"batch_input_shape": [null, 300, 4], "dtype": "float32", "sparse": false, "name": "input_1"}, "inbound_nodes": []}, {"name": "conv1d_1", "class_name": "Conv1D", "config": {"name": "conv1d_1", "trainable": true, "filters": 15, "kernel_size": [3], "strides": [1], "padding": "valid", "data_format": "channels_last", "dilation_rate": [1], "activation": "linear", "use_bias": true, "kernel_initializer": {"class_name": "VarianceScaling", "config": {"scale": 1.0, "mode": "fan_avg", "distribution": "uniform", "seed": null}}, "bias_initializer": {"class_name": "Zeros", "config": {}}, 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