test_locally_linear.py 文件源码

def test_lle_manifold():
    rng = np.random.RandomState(0)
    # similar test on a slightly more complex manifold
    X = np.array(list(product(np.arange(18), repeat=2)))
    X = np.c_[X, X[:, 0] ** 2 / 18]
    X = X + 1e-10 * rng.uniform(size=X.shape)
    n_components = 2
    for method in ["standard", "hessian", "modified", "ltsa"]:
        clf = manifold.LocallyLinearEmbedding(n_neighbors=6,
                                              n_components=n_components,
                                              method=method, random_state=0)
        tol = 1.5 if method == "standard" else 3

        N = barycenter_kneighbors_graph(X, clf.n_neighbors).toarray()
        reconstruction_error = linalg.norm(np.dot(N, X) - X)
        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
            details = ("solver: %s, method: %s" % (solver, method))
            assert_less(reconstruction_error, tol, msg=details)
            assert_less(np.abs(clf.reconstruction_error_ -
                               reconstruction_error),
                        tol * reconstruction_error, msg=details)


# Test the error raised when parameter passed to lle is invalid