def test_add(self):
        prio_set_list = event._PrioritizedSetList()
        objs = [(i,) for i in range(5)]

        prio_set_list.add(0, objs[0])
        assert prio_set_list.list == [(0, {objs[0]})]

        prio_set_list.add(0, objs[1])
        assert prio_set_list.list == [(0, {objs[0], objs[1]})]

        prio_set_list.add(10, objs[2])
        assert prio_set_list.list == [(10, {objs[2]}),
                                      (0,  {objs[0], objs[1]})]

        prio_set_list.add(-10, objs[3])
        assert prio_set_list.list == [( 10, {objs[2]}),           # noqa: E201
                                      (  0, {objs[0], objs[1]}),  # noqa: E201
                                      (-10, {objs[3]})]
        prio_set_list.add(-1, objs[4])
        assert prio_set_list.list == [( 10, {objs[2]}),           # noqa: E201
                                      (  0, {objs[0], objs[1]}),  # noqa: E201
                                      ( -1, {objs[4]}),           # noqa: E201
                                      (-10, {objs[3]})]
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()