test_isomap.py 文件源码

def test_transform():
    n_samples = 200
    n_components = 10
    noise_scale = 0.01

    # Create S-curve dataset
    X, y = datasets.samples_generator.make_s_curve(n_samples, random_state=0)

    # Compute isomap embedding
    iso = manifold.Isomap(n_components, 2)
    X_iso = iso.fit_transform(X)

    # Re-embed a noisy version of the points
    rng = np.random.RandomState(0)
    noise = noise_scale * rng.randn(*X.shape)
    X_iso2 = iso.transform(X + noise)

    # Make sure the rms error on re-embedding is comparable to noise_scale
    assert_less(np.sqrt(np.mean((X_iso - X_iso2) ** 2)), 2 * noise_scale)