test_neighbors.py 文件源码

def test_neighbors_metrics(n_samples=20, n_features=3,
                           n_query_pts=2, n_neighbors=5):
    # Test computing the neighbors for various metrics
    # create a symmetric matrix
    V = rng.rand(n_features, n_features)
    VI = np.dot(V, V.T)

    metrics = [('euclidean', {}),
               ('manhattan', {}),
               ('minkowski', dict(p=1)),
               ('minkowski', dict(p=2)),
               ('minkowski', dict(p=3)),
               ('minkowski', dict(p=np.inf)),
               ('chebyshev', {}),
               ('seuclidean', dict(V=rng.rand(n_features))),
               ('wminkowski', dict(p=3, w=rng.rand(n_features))),
               ('mahalanobis', dict(VI=VI))]
    algorithms = ['brute', 'ball_tree', 'kd_tree']
    X = rng.rand(n_samples, n_features)

    test = rng.rand(n_query_pts, n_features)

    for metric, metric_params in metrics:
        results = []
        p = metric_params.pop('p', 2)
        for algorithm in algorithms:
            # KD tree doesn't support all metrics
            if (algorithm == 'kd_tree' and
                    metric not in neighbors.KDTree.valid_metrics):
                assert_raises(ValueError,
                              neighbors.NearestNeighbors,
                              algorithm=algorithm,
                              metric=metric, metric_params=metric_params)
                continue

            neigh = neighbors.NearestNeighbors(n_neighbors=n_neighbors,
                                               algorithm=algorithm,
                                               metric=metric, p=p,
                                               metric_params=metric_params)
            neigh.fit(X)
            results.append(neigh.kneighbors(test, return_distance=True))

        assert_array_almost_equal(results[0][0], results[1][0])
        assert_array_almost_equal(results[0][1], results[1][1])