hmm.py 文件源码

def _baum_welch_step(self, sequence, model, symbol_to_number):

        N = len(model._states)
        M = len(model._symbols)
        T = len(sequence)

        # compute forward and backward probabilities
        alpha = model._forward_probability(sequence)
        beta = model._backward_probability(sequence)

        # find the log probability of the sequence
        lpk = logsumexp2(alpha[T-1])

        A_numer = _ninf_array((N, N))
        B_numer = _ninf_array((N, M))
        A_denom = _ninf_array(N)
        B_denom = _ninf_array(N)

        transitions_logprob = model._transitions_matrix().T

        for t in range(T):
            symbol = sequence[t][_TEXT]  # not found? FIXME
            next_symbol = None
            if t < T - 1:
                next_symbol = sequence[t+1][_TEXT]  # not found? FIXME
            xi = symbol_to_number[symbol]

            next_outputs_logprob = model._outputs_vector(next_symbol)
            alpha_plus_beta = alpha[t] + beta[t]

            if t < T - 1:
                numer_add = transitions_logprob + next_outputs_logprob + \
                            beta[t+1] + alpha[t].reshape(N, 1)
                A_numer = np.logaddexp2(A_numer, numer_add)
                A_denom = np.logaddexp2(A_denom, alpha_plus_beta)
            else:
                B_denom = np.logaddexp2(A_denom, alpha_plus_beta)

            B_numer[:,xi] = np.logaddexp2(B_numer[:,xi], alpha_plus_beta)

        return lpk, A_numer, A_denom, B_numer, B_denom