epochs = 1
seq_len = 50
print('> Loading data... ')
X_train, y_train, X_test, y_test = read_csv('../file//others//sp500.csv',
seq_len, True)
print('X_train shape:', X_train.shape) #(3709L, 50L, 1L)
print('y_train shape:', y_train.shape) #(3709L,)
print('X_test shape:', X_test.shape) #(412L, 50L, 1L)
print('y_test shape:', y_test.shape) #(412L,)
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)
multiple_predictions = lstm.predict_sequences_multiple(model,
X_test,
seq_len,
prediction_len=50)
print('multiple_predictions shape:',
np.array(multiple_predictions).shape) #(8L,50L)
full_predictions = lstm.predict_sequence_full(model, X_test, seq_len)
temp[-1] = pred
real_value_pred = batch_scale.inverse_transform([temp])[0][-1]
return real_value_pred
if __name__ == '__main__':
lstm = LSTM()
df = pd.read_csv('000002-from-1995-01-01.csv')
window = 20
X_train, y_train, X_test, y_test = lstm.preprocess_data(df[::-1],
window,
predict_length=1,
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)):
