MLP ODE Timeseries

ML_ODE_Split_Data.py

def split_sequence(sequence, n_steps):
	_X, _y = list(), list()
	for i in range(len(sequence)):
		# find the end of this pattern
		end_ix = i + n_steps
		# check if we are beyond the sequence
		if end_ix > len(sequence)-1:
			break
		# gather input and output parts of the pattern
		seq_x, seq_y = sequence[i:end_ix], sequence[end_ix]
		_X.append(seq_x)
		_y.append(seq_y)
	return np.array(_X), np.array(_y)

#split data ODE Critical Dumped
x, y = split_sequence(Critical_Dumped, n_sampling)