transitions.py 文件源码

def max_likelihood(self,stateseqs=None,expected_transcounts=None):
        trans_counts = sum(expected_transcounts) if stateseqs is None \
                else self._count_transitions(stateseqs)

        # NOTE: could just call max_likelihood on each trans row, but this way
        # it handles a few lazy-initialization cases (e.g. if _row_distns aren't
        # initialized)
        errs = np.seterr(invalid='ignore',divide='ignore')
        trans_matrix = np.nan_to_num(trans_counts / trans_counts.sum(1)[:,na])
        np.seterr(**errs)

        # all-zero rows get set to uniform
        trans_matrix[trans_matrix.sum(1) == 0] = 1./trans_matrix.shape[0]
        assert np.allclose(trans_matrix.sum(1),1.)

        self.trans_matrix = trans_matrix

        return self