test_neighbors.py 文件源码

def test_kneighbors_regressor(n_samples=40,
                              n_features=5,
                              n_test_pts=10,
                              n_neighbors=3,
                              random_state=0):
    # Test k-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]:
            knn = neighbors.KNeighborsRegressor(n_neighbors=n_neighbors,
                                                weights=weights,
                                                algorithm=algorithm)
            knn.fit(X, y)
            epsilon = 1E-5 * (2 * rng.rand(1, n_features) - 1)
            y_pred = knn.predict(X[:n_test_pts] + epsilon)
            assert_true(np.all(abs(y_pred - y_target) < 0.3))