split_percent=0.85)
model = lstm.build_model([X_train.shape[2], window, 100, 1],
dropout=0.3,
problem_class='classification')
encoder = LabelEncoder()
encoded_Y = encoder.fit_transform(y_train)
dummy_y = np_utils.to_categorical(encoded_Y)
model.fit(X_train,
dummy_y,
batch_size=768,
nb_epoch=10,
validation_split=0.1,
verbose=1)
diff = []
ratio = []
pred = model.predict(X_test)
for u in range(len(y_test)):
pr = pred[u][0]
ratio.append((y_test[u] / pr) - 1)
diff.append(abs(y_test[u] - pr))
import matplotlib.pyplot as plt2
print(lstm.accuracy_rate(y_test, pred))
plt2.plot(pred, color='red', label='Prediction')
plt2.plot(y_test, color='blue', label='Ground Truth')
plt2.legend(loc='upper left')
plt2.show()
