def feature_TIC_correlation(self):
        r"""Instantaneous correlation matrix between mean-free input features and TICs

        Denoting the input features as :math:`X_i` and the TICs as :math:`\theta_j`, the instantaneous, linear correlation
        between them can be written as

        .. math::

            \mathbf{Corr}(X_i - \mu_i, \mathbf{\theta}_j) = \frac{1}{\sigma_{X_i - \mu_i}}\sum_l \sigma_{(X_i - \mu_i)(X_l - \mu_l} \mathbf{U}_{li}

        The matrix :math:`\mathbf{U}` is the matrix containing, as column vectors, the eigenvectors of the TICA
        generalized eigenvalue problem .

        Returns
        -------
        feature_TIC_correlation : ndarray(n,m)
            correlation matrix between input features and TICs. There is a row for each feature and a column
            for each TIC.
        """
        feature_sigma = np.sqrt(np.diag(self.cov))
        return np.dot(self.cov, self.eigenvectors[:, : self.dimension()]) / feature_sigma[:, np.newaxis]
r"""Instantaneous correlation matrix between mean-free input features and TICs

        Denoting the input features as :math:`X_i` and the TICs as :math:`\theta_j`, the instantaneous, linear correlation
        between them can be written as

        .. math::

            \mathbf{Corr}(X_i - \mu_i, \mathbf{\theta}_j) = \frac{1}{\sigma_{X_i - \mu_i}}\sum_l \sigma_{(X_i - \mu_i)(X_l - \mu_l} \mathbf{U}_{li}

        The matrix :math:`\mathbf{U}` is the matrix containing, as column vectors, the eigenvectors of the TICA
        generalized eigenvalue problem .

        Returns
        -------
        feature_TIC_correlation : ndarray(n,m)
            correlation matrix between input features and TICs. There is a row for each feature and a column
            for each TIC.