def test_compute_laplacian_matrix_args(almost_equal_decimals=5): input_types = ['data', 'adjacency', 'affinity'] params = [{}, {'radius': 4}, {'radius': 5}] lapl_params = [{}, {'scaling_epps': 4}, {'scaling_epps': 10}] adjacency_method = 'auto' affinity_method = 'auto' for laplacian_method in laplacian_methods(): X = np.random.uniform(size=(10, 2)) D = compute_adjacency_matrix(X, adjacency_method, **params[1]) A = compute_affinity_matrix(D, affinity_method, **params[1]) for init_params in lapl_params: for kwarg_params in lapl_params: true_params = init_params.copy() true_params.update(kwarg_params) laplacian_true = compute_laplacian_matrix( A, laplacian_method, **true_params) for input in input_types: G = Geometry(adjacency_method=adjacency_method, adjacency_kwds=params[1], affinity_method=affinity_method, affinity_kwds=params[1], laplacian_method=laplacian_method, laplacian_kwds=lapl_params[0]) if input in ['data']: G.set_data_matrix(X) if input in ['adjacency']: G.set_adjacency_matrix(D) else: G.set_affinity_matrix(A) laplacian_queried = G.compute_laplacian_matrix(**kwarg_params) assert_array_almost_equal(laplacian_true.todense(), laplacian_queried.todense(), almost_equal_decimals)
def test_compute_laplacian_matrix_args(almost_equal_decimals=5): input_types = ['data', 'adjacency', 'affinity'] params = [{}, {'radius':4}, {'radius':5}] lapl_params = [{}, {'scaling_epps':4}, {'scaling_epps':10}] adjacency_method = 'auto' affinity_method = 'auto' for laplacian_method in laplacian_methods(): X = np.random.uniform(size=(10, 2)) D = compute_adjacency_matrix(X, adjacency_method, **params[1]) A = compute_affinity_matrix(D, affinity_method, **params[1]) for init_params in lapl_params: for kwarg_params in lapl_params: true_params = init_params.copy() true_params.update(kwarg_params) laplacian_true = compute_laplacian_matrix(A, laplacian_method, **true_params) for input in input_types: G = Geometry(adjacency_method = adjacency_method, adjacency_kwds = params[1], affinity_method = affinity_method, affinity_kwds = params[1], laplacian_method = laplacian_method, laplacian_kwds = lapl_params[0]) if input in ['data']: G.set_data_matrix(X) if input in ['adjacency']: G.set_adjacency_matrix(D) else: G.set_affinity_matrix(A) laplacian_queried = G.compute_laplacian_matrix(**kwarg_params) assert_array_almost_equal(laplacian_true.todense(), laplacian_queried.todense(), almost_equal_decimals)
def check_symmetric(method, adjacency_radius, affinity_radius): adj = compute_adjacency_matrix(X, radius=adjacency_radius) aff = compute_affinity_matrix(adj, radius=affinity_radius) lap, lapsym, w = compute_laplacian_matrix(aff, method=method, full_output=True) sym = w[:, np.newaxis] * (lap.toarray() + np.eye(*lap.shape)) assert_allclose(lapsym.toarray(), sym)
def check_laplacian(input_type, laplacian_method): kwargs = {'scaling_epps': radius} if laplacian_method == 'renormalized': kwargs['renormalization_exponent'] = 1.5 adjacency = input_type(np.sqrt(matlab['S'])) affinity = compute_affinity_matrix(adjacency, radius=radius) laplacian = compute_laplacian_matrix(affinity, method=laplacian_method, **kwargs) if input_type is csr_matrix: laplacian = laplacian.toarray() assert_allclose(laplacian, laplacians[laplacian_method])
def check_laplacian(method): lap = compute_laplacian_matrix(aff, method=method) assert isspmatrix(lap) assert_equal(lap.shape, (X.shape[0], X.shape[0]))