khuangaf

#include <cstdlib>
#include <cstddef>
#include <windows.h>
#include <string>
#include <iostream>
#define MOD_NOREPEAT    0x4000
#define MOD_ALT         0x0001
using namespace std;

void shortCut(int argc, TCHAR *argv[]);

khuangaf

import json
import numpy as np
import os
import pandas as pd
import urllib2

# connect to poloniex's API
url = 'https://poloniex.com/public?command=returnChartData&currencyPair=USDT_BTC&start=1356998100&end=9999999999&period=300'

# parse json returned from the API to Pandas DF

khuangaf

import numpy as np
import pandas as pd
 
class PastSampler:
    '''
    Forms training samples for predicting future values from past value
    '''
     
    def __init__(self, N, K, sliding_window = True):
        '''

khuangaf

file_name='bitcoin2015to2017_close.h5'

from sklearn.preprocessing import MinMaxScaler
scaler = MinMaxScaler()
# normalization
for c in columns:
    df[c] = scaler.fit_transform(df[c].values.reshape(-1,1))
    
#Features are input sample dimensions(channels)
A = np.array(df)[:,None,:]

khuangaf

import pandas as pd
import numpy as numpy
from keras.models import Sequential
from keras.layers import Dense, Dropout, Activation, Flatten
from keras.layers import Conv1D, MaxPooling1D, LeakyReLU, PReLU
from keras.utils import np_utils
from keras.callbacks import CSVLogger, ModelCheckpoint
import h5py
import os
import tensorflow as tf

khuangaf

import pandas as pd
import numpy as numpy
from keras.models import Sequential
from keras.layers import Dense, Dropout, Activation, Flatten,Reshape
from keras.layers import Conv1D, MaxPooling1D
from keras.utils import np_utils
from keras.layers import LSTM, LeakyReLU
from keras.callbacks import CSVLogger, ModelCheckpoint
import h5py
import os

khuangaf

import pandas as pd
import numpy as numpy
from keras.models import Sequential
from keras.layers import Dense, Dropout, Activation, Flatten,Reshape
from keras.layers import Conv1D, MaxPooling1D, LeakyReLU
from keras.utils import np_utils
from keras.layers import GRU,CuDNNGRU
from keras.callbacks import CSVLogger, ModelCheckpoint
import h5py
import os

khuangaf

from keras import applications
from keras.models import Sequential
from keras.models import Model
from keras.layers import Dropout, Flatten, Dense, Activation
from keras.callbacks import CSVLogger
import tensorflow as tf
from scipy.ndimage import imread
import numpy as np
import random
from keras.layers import LSTM

khuangaf

model.fit(training_datas, training_labels,verbose=1, batch_size=batch_size,validation_data=(validation_datas,validation_labels), epochs = epochs, callbacks=[CSVLogger(output_file_name+'.csv', append=True),ModelCheckpoint('weights/'+output_file_name+'-{epoch:02d}-{val_loss:.5f}.hdf5', monitor='val_loss', verbose=1,mode='min')]

khuangaf

predicted = model.predict(validation_datas)
predicted_inverted = []

for i in range(original_datas.shape[1]):
    scaler.fit(original_datas[:,i].reshape(-1,1))
    predicted_inverted.append(scaler.inverse_transform(predicted[:,:,i]))
print np.array(predicted_inverted).shape
#get only the close data
ground_true = ground_true[:,:,0].reshape(-1)
ground_true_times = ground_true_times.reshape(-1)