run.py

import lstm
import time
import matplotlib.pyplot as plt

def plot_results(predicted_data, true_data):
    fig = plt.figure(facecolor='white')
    ax = fig.add_subplot(111)
    ax.plot(true_data, label='True Data')
    plt.plot(predicted_data, label='Prediction')
    plt.legend()
    plt.show()

def plot_results_multiple(predicted_data, true_data, prediction_len):
    fig = plt.figure(facecolor='white')
    ax = fig.add_subplot(111)
    ax.plot(true_data, label='True Data')
    #Pad the list of predictions to shift it in the graph to it's correct start
    for i, data in enumerate(predicted_data):
        padding = [None for p in range(i * prediction_len)]
        plt.plot(padding + data, label='Prediction')
        plt.legend()
    plt.show()

#Main Run Thread
# if __name__=='__main__':
# 	global_start_time = time.time()
# 	epochs  = 1
# 	seq_len = 50

# 	print('> Loading data... ')

# 	X_train, y_train, X_test, y_test = lstm.load_data('sp500.csv', seq_len, True)

# 	print('> Data Loaded. Compiling...')

# 	model = lstm.build_model([1, 50, 100, 1])

# 	model.fit(
# 	    X_train,
# 	    y_train,
# 	    batch_size=512,
# 	    nb_epoch=epochs,
# 	    validation_split=0.05)

# 	predictions = lstm.predict_sequences_multiple(model, X_test, seq_len, 50)
# 	#predicted = lstm.predict_sequence_full(model, X_test, seq_len)
# 	#predicted = lstm.predict_point_by_point(model, X_test)        

# 	print('Training duration (s) : ', time.time() - global_start_time)
# 	plot_results_multiple(predictions, y_test, 50)