Fisher.py 文件源码

def condensed_svd(self, M, tol=1e-3, store_singular_vals=False):
        U, S, Vt = linalg.svd(M, full_matrices=False)
        if store_singular_vals:
            self.singular_vals = S

        #want tolerance on fraction of variance in singular value
        #when not norm_covariance, need to normalize singular values
        S_norm = np.sum(S)

        rank = np.sum( (S/S_norm) > tol )

        return U[:,:rank], S[:rank], Vt[:rank,:], S_norm