Viterbi algorithm on Python.

viterbi.py

def viterbi(P, A):
    """
    P: log probability matrix (n_samples by n_states)
    A: log transition probability matrix (n_states by n_states)
    """
    n_samples = P.shape[0]
    states = np.arange(P.shape[1])
    V = np.zeros((n_samples, 2))
    S = np.zeros((n_samples, 2), dtype=int)
    V[0] = P[0]

    for t in range(1, n_samples):
        values = V[t-1] + A
        S[t-1] = np.argmax(values, axis=1)
        V[t] = P[t] + np.max(values, axis=1)

    y = np.zeros(n_samples, dtype=int)
    y[-1] = V[-1].argmax()
    for t in range(2, n_samples+1):
        y[-t] = S[-t, y[-t+1]]
    return y