def compute_neigh_hessian()

in utils/shape_utils.py [0:0]

• 21 lines of code
• 1 McCabe index (conditional complexity)

    def compute_neigh_hessian(self):

        neigh = self.get_neigh()

        n_vert = self.get_vert().shape[0]

        H = sparse.lil_matrix(1e-3 * sparse.identity(n_vert))

        I = np.array(neigh[:, 0].detach().cpu())
        J = np.array(neigh[:, 1].detach().cpu())
        V = np.ones([neigh.shape[0]])
        U = -V
        H = H + sparse.lil_matrix(
            sparse.coo_matrix((U, (I, J)), shape=(n_vert, n_vert))
        )
        H = H + sparse.lil_matrix(
            sparse.coo_matrix((U, (J, I)), shape=(n_vert, n_vert))
        )
        H = H + sparse.lil_matrix(
            sparse.coo_matrix((V, (I, I)), shape=(n_vert, n_vert))
        )
        H = H + sparse.lil_matrix(
            sparse.coo_matrix((V, (J, J)), shape=(n_vert, n_vert))
        )

        self.neigh_hessian = H