def test_spectral_amg_mode():
    # Test the amg mode of SpectralClustering
    centers = np.array([
        [0., 0., 0.],
        [10., 10., 10.],
        [20., 20., 20.],
    ])
    X, true_labels = make_blobs(n_samples=100, centers=centers,
                                cluster_std=1., random_state=42)
    D = pairwise_distances(X)  # Distance matrix
    S = np.max(D) - D  # Similarity matrix
    S = sparse.coo_matrix(S)
    try:
        from pyamg import smoothed_aggregation_solver

        amg_loaded = True
    except __HOLE__:
        amg_loaded = False
    if amg_loaded:
        labels = spectral_clustering(S, n_clusters=len(centers),
                                     random_state=0, eigen_solver="amg")
        # We don't care too much that it's good, just that it *worked*.
        # There does have to be some lower limit on the performance though.
        assert_greater(np.mean(labels == true_labels), .3)
    else:
        assert_raises(ValueError, spectral_embedding, S,
                      n_components=len(centers),
                      random_state=0, eigen_solver="amg")
dataset/ETHPy150Open scikit-learn/scikit-learn/sklearn/cluster/tests/test_spectral.py/test_spectral_amg_mode