class Evaluate:
def __init__(self,
Data,
lstm_neurons,
epochs,
ax,
gradient=0.9056,
dense_neruons=3):
self.lstm = LSTM(lstm_neurons, dense_neruons)
self.lstm.create_model()
self.lstm.fit_model(epochs, Data)
self.lstm.print_stats()
self.states = States(4000, 23000)
self.states.create_unperturbed(self.lstm, Data)
self.states.create_perturbed(self.lstm, Data)
self.Lyapunov = Lyapunov(self.states)
self.Lyapunov.plot_exponent(ax, gradient)
class Predict:
def __init__(self, Data, lstm_neurons, epochs, dense_neurons=3):
self.lstm = LSTM(Data, lstm_neurons, epochs)
self.lstm.fit_model(False)
self.PredStates()
print(self.unperturbed.shape)
def PredStates(self):
old_state = tf.expand_dims(
self.lstm.Data.datapoints[200000:200000 +
self.lstm.Data.time_steps], 0)
predicted_states = []
for i in range(300):
new_state = self.lstm.model.predict(
old_state, batch_size=self.lstm.Data.batch_size) + old_state
old_state = new_state
predicted_states.append(np.squeeze(np.squeeze(old_state, 0), 0))
self.unperturbed = np.array(predicted_states)
