Algorithms, Dynamic Programming, Machine Learning, Artificial Intelligence, Markov Models.
range(len(path_probabilities) - 1, -1, -1): path.append(path_step_state) path_step_state = \ path_probabilities[t][path_step_state].previous_state path.reverse() Try testing this implementation on the following HMM. hmm = HMM( # initial state probabilities { 's0': 0.8, 's1': 0.1, 's2': 0.1 }, #