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

        Parameters
        ----------
        sequences : list of array, each of shape (n_samples_i, n_features)
            Test data. A list of sequences in afeature space, each of which is a 2D
            array of possibily different lengths, but the same number of features.

        Returns
        -------
        gmrq : float
            Generalized matrix Rayleigh quotient. This number indicates how
            well the top ``n_timescales+1`` eigenvectors of this tICA 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)
        """

        assert self._initialized
        V = self.eigenvectors_

        # Note: How do we deal with regularization parameters like gamma
        # here? I'm not sure. Should C and S be estimated using self's
        # regularization parameters?
        m2 = self.__class__(shrinkage=self.shrinkage, n_components=self.n_components, lag_time=self.lag_time,
                            landmarks=self.landmarks, kernel_params=self.kernel_params)
        m2.fit(sequences)
        numerator = V.T.dot(m2.offset_correlation_).dot(V)
        denominator = V.T.dot(m2.covariance_).dot(V)

        try:
            trace = np.trace(numerator.dot(np.linalg.inv(denominator)))
        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, each of shape (n_samples_i, n_features)
            Test data. A list of sequences in afeature space, each of which is a 2D
            array of possibily different lengths, but the same number of features.

        Returns
        -------
        gmrq : float
            Generalized matrix Rayleigh quotient. This number indicates how
            well the top ``n_timescales+1`` eigenvectors of this tICA 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)