def test_spectral_embedding_two_components(seed=36):
    # Test spectral embedding with two components
    random_state = np.random.RandomState(seed)
    n_sample = 100
    affinity = np.zeros(shape=[n_sample * 2, n_sample * 2])
    # first component
    affinity[0:n_sample,
             0:n_sample] = np.abs(random_state.randn(n_sample, n_sample)) + 2
    # second component
    affinity[n_sample::,
             n_sample::] = np.abs(random_state.randn(n_sample, n_sample)) + 2

    # Test of internal _graph_connected_component before connection
    component = _graph_connected_component(affinity, 0)
    assert_true(component[:n_sample].all())
    assert_true(not component[n_sample:].any())
    component = _graph_connected_component(affinity, -1)
    assert_true(not component[:n_sample].any())
    assert_true(component[n_sample:].all())

    # connection
    affinity[0, n_sample + 1] = 1
    affinity[n_sample + 1, 0] = 1
    affinity.flat[::2 * n_sample + 1] = 0
    affinity = 0.5 * (affinity + affinity.T)

    true_label = np.zeros(shape=2 * n_sample)
    true_label[0:n_sample] = 1

    se_precomp = SpectralEmbedding(n_components=1, affinity="precomputed",
                                   random_state=np.random.RandomState(seed))
    embedded_coordinate = se_precomp.fit_transform(affinity)
    # Some numpy versions are touchy with types
    embedded_coordinate = \
        se_precomp.fit_transform(affinity.astype(np.float32))
    # thresholding on the first components using 0.
    label_ = np.array(embedded_coordinate.ravel() < 0, dtype="float")
    assert_equal(normalized_mutual_info_score(true_label, label_), 1.0)
dataset/ETHPy150Open scikit-learn/scikit-learn/sklearn/manifold/tests/test_spectral_embedding.py/test_spectral_embedding_two_components/original