def test_DiffusionMap_get_kernel_matrix_symmetry(mixtureNormals):
"make sure the kernel matrix is symmetric"
X = mixtureNormals
df = DiffusionMap(sigma=1, embedding_dim=2)
K = df._get_kernel_matrix(X, k=2)
Q = (K - K.T).toarray() # np.all doesnt work on sparse matrices
assert np.all(Q == 0), 'returned kernel matrix is not symmetric'
def test_DiffusionMap_get_kernel_matrix_number_of_neighbours(mixtureNormals):
"""actually we would like to test for the exact number of neighvours
but due tot the symmetrizing, it can exceed the kNN"""
X = mixtureNormals
embDim = 2
df = DiffusionMap(sigma=1, embedding_dim=embDim)
kNN = 4
K = df._get_kernel_matrix(X, k=kNN)
assert K.shape == (X.shape[0], X.shape[0])
nonzero_elements_per_row = np.sum(K.toarray() != 0, 1)
print(nonzero_elements_per_row)
assert np.all(
nonzero_elements_per_row >= kNN
) # the number of nonzero elements must be kNN or larger (due to the symmetrizing
def test__get_kernel_matrix_sparse(mixtureNormals):
df = DiffusionMap(sigma=1, embedding_dim=2)
K = df._get_kernel_matrix(mixtureNormals, k=10)
assert issparse(K)
