def test_laplacian_eigenmap_deterministic():
# Test that laplacian eigenmap is deterministic
random_state = np.random.RandomState(36)
data = random_state.randn(10, 30)
sims = rbf_kernel(data)
embedding_1 = laplacian_eigenmap(sims)
embedding_2 = laplacian_eigenmap(sims)
assert_array_almost_equal(embedding_1, embedding_2)
def test_laplacian_eigenmap_unnormalized():
# Test that laplacian_eigenmap is also processing unnormalized laplacian correctly
random_state = np.random.RandomState(36)
data = random_state.randn(10, 30)
sims = rbf_kernel(data)
n_components = 8
embedding_1 = laplacian_eigenmap(sims,
norm_laplacian=False,
n_components=n_components,
drop_first=False)
# Verify using manual computation with denembedding eigh
laplacian, dd = graph_laplacian(sims, normed=False, return_diag=True)
_, diffusion_map = eigh(laplacian)
embedding_2 = diffusion_map.T[:n_components] * dd
embedding_2 = _deterministic_vector_sign_flip(embedding_2).T
assert_array_almost_equal(embedding_1, embedding_2)
