99483e3ca1578397e421a5ea8a5f96e937e59af9,pygsp/graphs/graph.py,Graph,estimate_lmax,#Graph#Any#,653

Before Change

        try:
            // For robustness purposes, increase the error by 1 percent
            lmax = 1.01 * \
                sparse.linalg.eigs(self.L, k=1, tol=5e-3, ncv=10)[0][0]

        except sparse.linalg.ArpackNoConvergence:
            self.logger.warning("Cannot use default method.")
            lmax = 2. * np.max(self.d)

After Change

        except sparse.linalg.ArpackNoConvergence:
            self.logger.warning("Lanczos method did not converge. "
                                "Using an alternative method.")
            if self.lap_type == "normalized":
                lmax = 2  // Spectrum is bounded by [0, 2].
            elif self.lap_type == "combinatorial":
                lmax = 2 * np.max(self.d)
            else:
                raise ValueError("Unknown Laplacian type "
                                 "{}".format(self.lap_type))

        self._lmax = lmax

    def get_edge_list(self):
        r

Instances