test_locally_linear.py 文件源码

def test_lle_simple_grid():
    # note: ARPACK is numerically unstable, so this test will fail for
    #       some random seeds.  We choose 2 because the tests pass.
    rng = np.random.RandomState(2)

    # grid of equidistant points in 2D, n_components = n_dim
    X = np.array(list(product(range(5), repeat=2)))
    X = X + 1e-10 * rng.uniform(size=X.shape)
    n_components = 2
    clf = manifold.LocallyLinearEmbedding(n_neighbors=5,
                                          n_components=n_components,
                                          random_state=rng)
    tol = 0.1

    N = barycenter_kneighbors_graph(X, clf.n_neighbors).toarray()
    reconstruction_error = linalg.norm(np.dot(N, X) - X, 'fro')
    assert_less(reconstruction_error, tol)

    for solver in eigen_solvers:
        clf.set_params(eigen_solver=solver)
        clf.fit(X)
        assert_true(clf.embedding_.shape[1] == n_components)
        reconstruction_error = linalg.norm(
            np.dot(N, clf.embedding_) - clf.embedding_, 'fro') ** 2

        assert_less(reconstruction_error, tol)
        assert_almost_equal(clf.reconstruction_error_,
                            reconstruction_error, decimal=1)

    # re-embed a noisy version of X using the transform method
    noise = rng.randn(*X.shape) / 100
    X_reembedded = clf.transform(X + noise)
    assert_less(linalg.norm(X_reembedded - clf.embedding_), tol)