def pred_func(model: NeuralNetwork, timestep):
global data_q
if len(data_q) > timestep:
data = np.expand_dims(np.asarray(
[data_q.pop() for _ in range(timestep)]),
axis=0)
return model.predict(x=data)
else:
print('Not Enough Data to predict')
return None
def pred_func(model: NeuralNetwork, data):
return model.predict(x=data)