Markov Chains, Probabilistic Models, Text Generation, Hidden Markov Models, Python.
True) pi = [(x/np.sum(left[:, 0])).real for x in left[:, 0]] return pi pi_lev = left_eigen_vectors() In addition, you can use the following code to find the probability of a given sequence of states. # Finding the probability of a sequence def seq_prob(seq, A, pi): start_state = seq[0] prob =