test_spectral.py 文件源码

def test_discretize(seed=8):
    # Test the discretize using a noise assignment matrix
    random_state = np.random.RandomState(seed)
    for n_samples in [50, 100, 150, 500]:
        for n_class in range(2, 10):
            # random class labels
            y_true = random_state.random_integers(0, n_class, n_samples)
            y_true = np.array(y_true, np.float)
            # noise class assignment matrix
            y_indicator = sparse.coo_matrix((np.ones(n_samples),
                                             (np.arange(n_samples),
                                              y_true)),
                                            shape=(n_samples,
                                                   n_class + 1))
            y_true_noisy = (y_indicator.toarray()
                            + 0.1 * random_state.randn(n_samples,
                                                       n_class + 1))
            y_pred = discretize(y_true_noisy, random_state)
            assert_greater(adjusted_rand_score(y_true, y_pred), 0.8)