def test_radius_neighbors_regressor(n_samples=40,
                                    n_features=3,
                                    n_test_pts=10,
                                    radius=0.5,
                                    random_state=0):
    # Test radius-based neighbors regression
    rng = np.random.RandomState(random_state)
    X = 2 * rng.rand(n_samples, n_features) - 1
    y = np.sqrt((X ** 2).sum(1))
    y /= y.max()

    y_target = y[:n_test_pts]

    weight_func = _weight_func

    for algorithm in ALGORITHMS:
        for weights in ['uniform', 'distance', weight_func]:
            neigh = neighbors.RadiusNeighborsRegressor(radius=radius,
                                                       weights=weights,
                                                       algorithm=algorithm)
            neigh.fit(X, y)
            epsilon = 1E-5 * (2 * rng.rand(1, n_features) - 1)
            y_pred = neigh.predict(X[:n_test_pts] + epsilon)
            assert_true(np.all(abs(y_pred - y_target) < 2 / radius))
dataset/ETHPy150Open scikit-learn/scikit-learn/sklearn/neighbors/tests/test_neighbors.py/test_radius_neighbors_regressor/swapped operands of `/`