def test_laplacian_submatrix_matrix_node_dim(self):
with pytest.raises(nx.NetworkXError):
from networkx.algorithms.distance_measures import _laplacian_submatrix
M = sp.sparse.csr_matrix([[1, 2, 3], [4, 5, 6], [7, 8, 9]],
dtype=np.float32)
_laplacian_submatrix(1, M, [1, 2, 3, 4])
def test_laplacian_submatrix_square(self):
with pytest.raises(nx.NetworkXError):
from networkx.algorithms.distance_measures import _laplacian_submatrix
M = sp_sparse.csr_matrix([[1, 2], [4, 5], [7, 8]],
dtype=np.float32)
_laplacian_submatrix(1, M, [1, 2, 3])
def test_laplacian_submatrix(self):
from networkx.algorithms.distance_measures import _laplacian_submatrix
M = sp_sparse.csr_matrix([[1, 2, 3], [4, 5, 6], [7, 8, 9]],
dtype=np.float32)
N = sp_sparse.csr_matrix([[5, 6], [8, 9]], dtype=np.float32)
Mn, Mn_nodelist = _laplacian_submatrix(1, M, [1, 2, 3])
assert_equal(Mn_nodelist, [2, 3])
assert_true(np.allclose(Mn.toarray(), N.toarray()))
