def METHOD_NAME(self):
    np.random.seed(1)
    A = np.eye(50)
    x = np.ones(50)
    r = functools.partial(
        adaptive._tikhonov,
        alpha=0.)
    b = A.dot(x)
    noise = .5 * np.ones(50)
    b_noise = b + noise
    regression = lambda A, b: x
    error_eq = lambda data, model: np.linalg.norm(data - model)/data.shape[0]
    eps = adaptive._k_fold_cv_regression(
        A, b_noise,
        regression,
        error_eq=error_eq,
        k=10)
    assert np.isclose(eps, .5)