9844e657a565158d7e46b0900270b21fbcc38d82,brainiak/reprsimil/brsa.py,BRSA,_initial_fit_singpara,#BRSA#Any#Any#Any#Any#Any#Any#Any#Any#Any#Any#Any#Any#Any#Any#Any#Any#Any#Any#Any#Any#Any#Any#Any#Any#Any#Any#Any#Any#Any#Any#,900

Before Change

        // (3) random initialization

        current_vec_U_chlsk_l = self.random_state_.randn(n_l)
        // vectorized version of L, Cholesky factor of U, the shared
        // covariance matrix of betas across voxels.

        rho1 = np.sum(

After Change

        // There are several possible ways of initializing the covariance.
        // (1) start from the point estimation of covariance

        cov_point_est = np.cov(beta_hat[n_X0:, :]) / np.var(residual)
        current_vec_U_chlsk_l = \
            np.linalg.cholesky((cov_point_est + np.eye(n_C)) / 2)[l_idx]

        // We use the average of covariance of point estimation and an identity
        // matrix as the initial value of the covariance matrix, just in case
        // the user provides data in which n_V is smaller than n_C.

Instances