# simple LSTM model
from keras.models import Sequential
from keras.layers import Activation, Dense, LSTM

model = Sequential()
model.add(LSTM(128, input_shape=(32, 1))) # 32 timespan 1 value unit
model.add(Dense(1))
model.add(Activation("linear"))
model.compile(optimizer="rmsprop", loss="mse")

# several sin curve data for train
import numpy as np

for cycle in range(4, 36):
    data = np.sin(np.arange(360) * (np.pi / cycle))
    # chunk to 32-timespan and its 1-next value
    x_train = np.reshape(
        [data[i:i+32] for i in range(len(data) - 32 - 1)], (-1, 32, 1))
    y_train = np.reshape(
        [data[i+32] for i in range(len(data) - 32 - 1)], (-1, 1))
    model.fit(x_train, y_train, verbose = False)
    pass

# make initial curve data
x = np.sin(np.arange(32) * np.pi / 18  + 3 * np.pi / 4)
# generate subsequent curve
for i in range(300):
    y = model.predict(x[-32:].reshape(1, 32, 1))
    x = np.append(x, y)
    pass

# plot curve
import matplotlib.pyplot as plt

fig = plt.figure()
sub = fig.add_subplot(1, 1, 1)
sub.plot(x[32:])
fig.show()

input("quit to enter> ")