def test_view_of_view(matrix_type, subset_func, subset_func2):
    adata = gen_adata((30, 15), X_type=matrix_type)
    adata.raw = adata
    if subset_func is single_subset:
        pytest.xfail("Other subset generating functions have trouble with this")
    var_s1 = subset_func(adata.var_names, min_size=4)
    var_view1 = adata[:, var_s1]
    var_s2 = subset_func2(var_view1.var_names)
    var_view2 = var_view1[:, var_s2]
    assert var_view2._adata_ref is adata
    obs_s1 = subset_func(adata.obs_names, min_size=4)
    obs_view1 = adata[obs_s1, :]
    obs_s2 = subset_func2(obs_view1.obs_names)
    assert adata[obs_s1, :][:, var_s1][obs_s2, :]._adata_ref is adata

    view_of_actual_copy = adata[:, var_s1].copy()[obs_s1, :].copy()[:, var_s2].copy()

    view_of_view_copy = adata[:, var_s1][obs_s1, :][:, var_s2].copy()

    assert_equal(view_of_actual_copy, view_of_view_copy, exact=True)
def compute_svm_subjects(K, y, n_folds=5):
    """
    """
    cv = KFold(len(K)/2, n_folds)
    scores = np.zeros(n_folds)
    for i, (train, test) in enumerate(cv):
        train_ids = np.concatenate((train, len(K)/2+train))
        test_ids = np.concatenate((test, len(K)/2+test))
        clf = SVC(kernel='precomputed')
        clf.fit(K[train_ids, :][:, train_ids], y[train_ids])
        scores[i] = clf.score(K[test_ids, :][:, train_ids], y[test_ids])

    return scores.mean()