Created
January 1, 2018 02:54
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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) | |
ground_true_times = pd.to_datetime(ground_true_times, unit='s') | |
# since we are appending in the first dimension | |
predicted_inverted = np.array(predicted_inverted)[0,:,:].reshape(-1) | |
print np.array(predicted_inverted).shape | |
validation_output_times = pd.to_datetime(validation_output_times.reshape(-1), unit='s') |
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