hmm_states.py 文件源码

def _messages_backwards_log_fast(trans_potential, init_potential, likelihood_log_potential_llt):
        errs = np.seterr(over='ignore')
        Al = np.log(trans_potential)
        pil = np.log(init_potential)
        aBl = likelihood_log_potential_llt
        nhs = trans_potential.shape[0]
        sequence_length = aBl.shape[0]
        betal = np.zeros((sequence_length, nhs * 2))
        giant_Al_pil = np.tile(np.vstack((np.tile(pil, (nhs,1)), Al )), (1,2))


        for t in xrange(betal.shape[0]-2,-1,-1):
            np.logaddexp.reduce( giant_Al_pil + betal[t+1] + aBl[t+1], axis=1, out=(betal[t]))

        np.seterr(**errs)
        return betal


    ### Gibbs sampling