def test_affinity_mat_poly(data):
v1_data = data['fit_data'][0]
distances = cdist(v1_data, v1_data)
gamma = 1 / (2 * np.median(distances)**2)
true_kernel = polynomial_kernel(v1_data, gamma=gamma)
spectral = MultiviewSpectralClustering(random_state=RANDOM_STATE,
affinity='poly')
p_kernel = spectral._affinity_mat(v1_data)
assert (p_kernel.shape[0] == data['n_fit'])
assert (p_kernel.shape[1] == data['n_fit'])
for ind1 in range(p_kernel.shape[0]):
for ind2 in range(p_kernel.shape[1]):
assert np.abs(true_kernel[ind1][ind2] -
p_kernel[ind1][ind2]) < 0.000001
def test_affinity_neighbors(data):
v1_data = data['fit_data'][0]
n_neighbors = 10
neighbors = NearestNeighbors(n_neighbors=n_neighbors)
neighbors.fit(v1_data)
true_kernel = neighbors.kneighbors_graph(v1_data).toarray()
spectral = MultiviewSpectralClustering(random_state=RANDOM_STATE,
affinity='nearest_neighbors',
n_neighbors=10)
n_kernel = spectral._affinity_mat(v1_data)
assert (n_kernel.shape[0] == data['n_fit'])
assert (n_kernel.shape[1] == data['n_fit'])
for ind1 in range(n_kernel.shape[0]):
for ind2 in range(n_kernel.shape[1]):
assert np.abs(true_kernel[ind1][ind2] -
n_kernel[ind1][ind2]) < 0.000001
def test_affinity_mat_rbf2(data):
v1_data = data['fit_data'][0]
gamma = 1
spectral = MultiviewSpectralClustering(random_state=RANDOM_STATE,
gamma=gamma)
distances = cdist(v1_data, v1_data)
gamma = 1 / (2 * np.median(distances)**2)
true_kernel = rbf_kernel(v1_data, gamma=1)
g_kernel = spectral._affinity_mat(v1_data)
assert (g_kernel.shape[0] == data['n_fit'])
assert (g_kernel.shape[1] == data['n_fit'])
for ind1 in range(g_kernel.shape[0]):
for ind2 in range(g_kernel.shape[1]):
assert np.abs(true_kernel[ind1][ind2] -
g_kernel[ind1][ind2]) < 0.000001
