test_kde.py 文件源码

def test_kde_algorithm_metric_choice():
    # Smoke test for various metrics and algorithms
    rng = np.random.RandomState(0)
    X = rng.randn(10, 2)    # 2 features required for haversine dist.
    Y = rng.randn(10, 2)

    for algorithm in ['auto', 'ball_tree', 'kd_tree']:
        for metric in ['euclidean', 'minkowski', 'manhattan',
                       'chebyshev', 'haversine']:
            if algorithm == 'kd_tree' and metric not in KDTree.valid_metrics:
                assert_raises(ValueError, KernelDensity,
                              algorithm=algorithm, metric=metric)
            else:
                kde = KernelDensity(algorithm=algorithm, metric=metric)
                kde.fit(X)
                y_dens = kde.score_samples(Y)
                assert_equal(y_dens.shape, Y.shape[:1])