test_spectral.py 文件源码

def test_spectral_clustering():
    S = np.array([[1.0, 1.0, 1.0, 0.2, 0.0, 0.0, 0.0],
                  [1.0, 1.0, 1.0, 0.2, 0.0, 0.0, 0.0],
                  [1.0, 1.0, 1.0, 0.2, 0.0, 0.0, 0.0],
                  [0.2, 0.2, 0.2, 1.0, 1.0, 1.0, 1.0],
                  [0.0, 0.0, 0.0, 1.0, 1.0, 1.0, 1.0],
                  [0.0, 0.0, 0.0, 1.0, 1.0, 1.0, 1.0],
                  [0.0, 0.0, 0.0, 1.0, 1.0, 1.0, 1.0]])

    for eigen_solver in ('arpack', 'lobpcg'):
        for assign_labels in ('kmeans', 'discretize'):
            for mat in (S, sparse.csr_matrix(S)):
                model = SpectralClustering(random_state=0, n_clusters=2,
                                           affinity='precomputed',
                                           eigen_solver=eigen_solver,
                                           assign_labels=assign_labels
                                          ).fit(mat)
                labels = model.labels_
                if labels[0] == 0:
                    labels = 1 - labels

                assert_array_equal(labels, [1, 1, 1, 0, 0, 0, 0])

                model_copy = loads(dumps(model))
                assert_equal(model_copy.n_clusters, model.n_clusters)
                assert_equal(model_copy.eigen_solver, model.eigen_solver)
                assert_array_equal(model_copy.labels_, model.labels_)