Expand Up @@ -412,10 +412,14 @@ def estimate_rank(self, tol): The optimal hard threshold for singular values is 4/sqrt(3) """ mat = self.to_hankelet() # nrows, ncols = mat.shape # beta = nrows / ncols # omega = 0.56 * beta ** 3 - 0.95 * beta ** 2 + 1.82 * beta + 1.43 _, s, _ = sli.svd(mat, min(10, min(mat.shape))) if np.any(mat): # check that the matrix contains non-zero entries # nrows, ncols = mat.shape # beta = nrows / ncols # omega = 0.56 * beta ** 3 - 0.95 * beta ** 2 + 1.82 * beta + 1.43 _, s, _ = sli.svd(mat, min(10, min(mat.shape))) else: s = np.zeros(min(10, min(mat.shape)))
# return np.argmin(s > omega * np.median(s)) eigen = s**2 diff = np.abs(np.diff(eigen / eigen[0])) Expand Down