Beispiel #1
0
def test_lle_simple_grid():
    # note: ARPACK is numerically unstable, so this test will fail for
    #       some random seeds.  We choose 20 because the tests pass.
    rng = np.random.RandomState(20)
    tol = 0.1
    # 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
    G = geom.Geometry(adjacency_kwds={'radius': 3})
    G.set_data_matrix(X)
    tol = 0.1
    distance_matrix = G.compute_adjacency_matrix()
    N = lle.barycenter_graph(distance_matrix, X).todense()
    reconstruction_error = np.linalg.norm(np.dot(N, X) - X, 'fro')
    print('*************')
    print('Reconstruction error for {}: {}'.format('numpy',
                                                   reconstruction_error))
    print('*************')
    assert (reconstruction_error < tol)
    for eigen_solver in EIGEN_SOLVERS:
        clf = lle.LocallyLinearEmbedding(n_components=n_components,
                                         geom=G,
                                         eigen_solver=eigen_solver,
                                         random_state=rng)
        clf.fit(X)
        assert (clf.embedding_.shape[1] == n_components)
        reconstruction_error = np.linalg.norm(
            np.dot(N, clf.embedding_) - clf.embedding_, 'fro')**2
        print('*************')
        print('Reconstruction error for {}: {}'.format(eigen_solver,
                                                       reconstruction_error))
        print('*************')
        assert (reconstruction_error < tol)
Beispiel #2
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def test_barycenter_kneighbors_graph():
    X = np.array([[0, 1], [1.01, 1.], [2, 0]])
    distance_matrix = squareform(pdist(X))
    A = lle.barycenter_graph(distance_matrix, X)
    # check that columns sum to one
    assert_array_almost_equal(np.sum(A.toarray(), 1), np.ones(3))
    pred = np.dot(A.toarray(), X)
    assert(np.linalg.norm(pred - X) / X.shape[0] < 1)
Beispiel #3
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def test_barycenter_kneighbors_graph():
    X = np.array([[0, 1], [1.01, 1.], [2, 0]])
    distance_matrix = squareform(pdist(X))
    A = lle.barycenter_graph(distance_matrix, X)
    # check that columns sum to one
    assert_array_almost_equal(np.sum(A.toarray(), 1), np.ones(3))
    pred = np.dot(A.toarray(), X)
    assert (np.linalg.norm(pred - X) / X.shape[0] < 1)
Beispiel #4
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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
    G = geom.Geometry(adjacency_kwds = {'radius':3})
    G.set_data_matrix(X)
    distance_matrix = G.compute_adjacency_matrix()
    tol = 1.5
    N = lle.barycenter_graph(distance_matrix, X).todense()
    reconstruction_error = np.linalg.norm(np.dot(N, X) - X)
    assert(reconstruction_error < tol)
    for eigen_solver in EIGEN_SOLVERS:
        clf = lle.LocallyLinearEmbedding(n_components = n_components, geom = G,
                                eigen_solver = eigen_solver, random_state = rng)
        clf.fit(X)
        assert(clf.embedding_.shape[1] == n_components)
        reconstruction_error = np.linalg.norm(
            np.dot(N, clf.embedding_) - clf.embedding_, 'fro') ** 2
        assert(reconstruction_error < tol)
Beispiel #5
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def test_ltsa_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
    G = geom.Geometry(adjacency_kwds = {'radius':3})
    G.set_data_matrix(X)
    distance_matrix = G.compute_adjacency_matrix()
    tol = 1.5
    N = barycenter_graph(distance_matrix, X).todense()
    reconstruction_error = np.linalg.norm(np.dot(N, X) - X)
    assert(reconstruction_error < tol)
    for eigen_solver in EIGEN_SOLVERS:
        clf = ltsa.LTSA(n_components = n_components, geom = G,
                        eigen_solver = eigen_solver, random_state = rng)
        clf.fit(X)
        assert(clf.embedding_.shape[1] == n_components)
        reconstruction_error = np.linalg.norm(
            np.dot(N, clf.embedding_) - clf.embedding_, 'fro') ** 2
        assert(reconstruction_error < tol)
Beispiel #6
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def test_lle_simple_grid():
    # note: ARPACK is numerically unstable, so this test will fail for
    #       some random seeds.  We choose 20 because the tests pass.
    rng = np.random.RandomState(20)
    tol = 0.1
    # 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
    G = geom.Geometry(adjacency_kwds = {'radius':3})
    G.set_data_matrix(X)
    tol = 0.1
    distance_matrix = G.compute_adjacency_matrix()
    N = lle.barycenter_graph(distance_matrix, X).todense()
    reconstruction_error = np.linalg.norm(np.dot(N, X) - X, 'fro')
    assert(reconstruction_error < tol)
    for eigen_solver in EIGEN_SOLVERS:
        clf = lle.LocallyLinearEmbedding(n_components = n_components, geom = G,
                                eigen_solver = eigen_solver, random_state = rng)
        clf.fit(X)
        assert(clf.embedding_.shape[1] == n_components)
        reconstruction_error = np.linalg.norm(
        np.dot(N, clf.embedding_) - clf.embedding_, 'fro') ** 2
        assert(reconstruction_error < tol)