def score(self, sequences, y=None):
        """Score the model on new data using the generalized matrix Rayleigh
        quotient

        Parameters
        ----------
        sequences : list of array-like
            List of sequences, or a single sequence. Each sequence should be a
            1D iterable of state labels. Labels can be integers, strings, or
            other orderable objects.

        Returns
        -------
        gmrq : float
            Generalized matrix Rayleigh quotient. This number indicates how
            well the top ``n_timescales+1`` eigenvectors of this model perform
            as slowly decorrelating collective variables for the new data in
            ``sequences``.

        References
        ----------
        .. [1] McGibbon, R. T. and V. S. Pande, "Variational cross-validation
           of slow dynamical modes in molecular kinetics" J. Chem. Phys. 142,
           124105 (2015)
        """
        # eigenvectors from the model we're scoring, `self`
        V = self.right_eigenvectors_

        m2 = self.__class__(**self.get_params())
        m2.fit(sequences)

        if self.mapping_ != m2.mapping_:
            V = self._map_eigenvectors(V, m2.mapping_)

        S = np.diag(m2.populations_)
        C = S.dot(m2.transmat_)

        try:
            trace = np.trace(V.T.dot(C.dot(V)).dot(np.linalg.inv(V.T.dot(S.dot(V)))))
        except np.linalg.LinAlgError:
            trace = np.nan

        return trace
Score the model on new data using the generalized matrix Rayleigh
        quotient

        Parameters
        ----------
        sequences : list of array-like
            List of sequences, or a single sequence. Each sequence should be a
            1D iterable of state labels. Labels can be integers, strings, or
            other orderable objects.

        Returns
        -------
        gmrq : float
            Generalized matrix Rayleigh quotient. This number indicates how
            well the top ``n_timescales+1`` eigenvectors of this model perform
            as slowly decorrelating collective variables for the new data in
            ``sequences``.

        References
        ----------
        .. [1] McGibbon, R. T. and V. S. Pande, "Variational cross-validation
           of slow dynamical modes in molecular kinetics" J. Chem. Phys. 142,
           124105 (2015)