def main(): data = load_dataCSV() look_back = 28 jump=4 train_data, test_data = dp.rescale_data(data) trainX, trainY = dp.create_dataset(train_data, look_back) trainX = np.reshape(trainX, (trainX.shape[0], 1, trainX.shape[1])) testX, testY = dp.create_dataset(test_data, look_back) model = mod.getModel(look_back) model.fit( trainX, trainY, batch_size=128, nb_epoch=300, validation_split=0.10) pred,perfs=mod.testModel(model,testX,testY,jump,look_back) actual_test_data=test_data[len(test_data)-len(pred):] print("\n Average Covarance between predicted and actual prices on only predicted days:") print(np.mean(perfs)) print("\n Covarance between predicted and actual prices on all days:") print(np.cov(actual_test_data,pred)[1][0]) plt.figure(3) plt.plot(actual_test_data) plt.figure(4) plt.plot(pred) mod.saveModel(model,'lstm3')
def main(): data = load_dataCSV() look_back = 28 jump = 4 train_data, test_data = dp.rescale_data(data) trainX, trainY = dp.create_dataset(train_data, look_back) trainX = np.reshape(trainX, (trainX.shape[0], 1, trainX.shape[1])) testX, testY = dp.create_dataset(test_data, look_back) savedModel = load_model('lstm3.h5') pred, perfs = mod.testModel(savedModel, testX, testY, jump, look_back) actual_test_data = test_data[len(test_data) - len(pred):] print( "\n Average Covarance between predicted and actual prices on only predicted days:" ) print(np.mean(perfs)) print("\n Covarance between predicted and actual prices on all days:") print(np.cov(actual_test_data, pred)[1][0])