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_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(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]))
def check_laplacian(method):
lap = compute_laplacian_matrix(aff, method=method)
assert isspmatrix(lap)
assert_equal(lap.shape, (X.shape[0], X.shape[0]))
