def test_omni_embed():
"""
We compare the difference of norms of OmniBar and ABar.
ABar is the lowest variance estimate of the latent positions X.
OmniBar should be reasonablly close to ABar when n_vertices is high.
"""
def compute_bar(arr):
n = arr.shape[0] // 2
return (arr[:n] + arr[n:]) / 2
X, A1, A2 = generate_data(1000)
Abar = (A1 + A2) / 2
np.random.seed(11)
omni = OmnibusEmbed(n_components=3)
OmniBar = compute_bar(omni.fit_transform([A1, A2]))
omni = OmnibusEmbed(n_components=3)
ABar = compute_bar(omni.fit_transform([Abar, Abar]))
tol = 1.0e-2
assert allclose(norm(OmniBar, axis=1),
norm(ABar, axis=1),
rtol=tol,
atol=tol)
def run(diag_aug):
X, A1, A2 = generate_data(1000, seed=2)
Abar = (A1 + A2) / 2
omni = OmnibusEmbed(n_components=3, diag_aug=diag_aug)
OmniBar = compute_bar(omni.fit_transform([A1, A2]))
omni = OmnibusEmbed(n_components=3, diag_aug=diag_aug)
ABar = compute_bar(omni.fit_transform([Abar, Abar]))
tol = 1.0e-2
assert allclose(norm(OmniBar, axis=1),
norm(ABar, axis=1),
rtol=tol,
atol=tol)