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@khuangaf
Created January 1, 2018 01:29
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CryptocurrencyPrediction
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,:]
original_A = np.array(original_df)[:,None,:]
time_stamps = np.array(time_stamps)[:,None,None]
#Make samples of temporal sequences of pricing data (channel)
NPS, NFS = 256, 16 #Number of past and future samples
ps = PastSampler(NPS, NFS, sliding_window=False)
B, Y = ps.transform(A)
input_times, output_times = ps.transform(time_stamps)
original_B, original_Y = ps.transform(original_A)
import h5py
with h5py.File(file_name, 'w') as f:
f.create_dataset("inputs", data = B)
f.create_dataset('outputs', data = Y)
f.create_dataset("input_times", data = input_times)
f.create_dataset('output_times', data = output_times)
f.create_dataset("original_datas", data=np.array(original_df))
f.create_dataset('original_inputs',data=original_B)
f.create_dataset('original_outputs',data=original_Y)
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