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: we could just call max_likelihood on each trans row, but this
        # way it's a bit nicer
        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]-1)
        trans_matrix.flat[::trans_matrix.shape[0]+1] = 0.

        self.trans_matrix = trans_matrix
        assert np.allclose(0.,np.diag(self.trans_matrix))
        assert np.allclose(1.,self.trans_matrix.sum(1))

        return self