test_neighbors.py 文件源码

def test_neighbors_regressors_zero_distance():
    # Test radius-based regressor, when distance to a sample is zero.

    X = np.array([[1.0, 1.0], [1.0, 1.0], [2.0, 2.0], [2.5, 2.5]])
    y = np.array([1.0, 1.5, 2.0, 0.0])
    radius = 0.2
    z = np.array([[1.1, 1.1], [2.0, 2.0]])

    rnn_correct_labels = np.array([1.25, 2.0])

    knn_correct_unif = np.array([1.25, 1.0])
    knn_correct_dist = np.array([1.25, 2.0])

    for algorithm in ALGORITHMS:
        # we don't test for weights=_weight_func since user will be expected
        # to handle zero distances themselves in the function.
        for weights in ['uniform', 'distance']:
            rnn = neighbors.RadiusNeighborsRegressor(radius=radius,
                                                     weights=weights,
                                                     algorithm=algorithm)
            rnn.fit(X, y)
            assert_array_almost_equal(rnn_correct_labels, rnn.predict(z))

        for weights, corr_labels in zip(['uniform', 'distance'],
                                        [knn_correct_unif, knn_correct_dist]):
            knn = neighbors.KNeighborsRegressor(n_neighbors=2,
                                                weights=weights,
                                                algorithm=algorithm)
            knn.fit(X, y)
            assert_array_almost_equal(corr_labels, knn.predict(z))