Function test_build_kdtree

data_structures/kd_tree/tests/test_kdtree.py:26–58

Test that KD-Tree is built correctly. Cases: - Empty points list. - Positive depth value. - Negative depth value.

(num_points, cube_size, num_dimensions, depth, expected_result)

    ],
)
def test_build_kdtree(num_points, cube_size, num_dimensions, depth, expected_result):
    """
    Test that KD-Tree is built correctly.

    Cases:
        - Empty points list.
        - Positive depth value.
        - Negative depth value.
    """
    points = (
        hypercube_points(num_points, cube_size, num_dimensions).tolist()
        if num_points > 0
        else []
    )

    kdtree = build_kdtree(points, depth=depth)

    if expected_result is None:
        # Empty points list case
        assert kdtree is None, f"Expected None for empty points list, got {kdtree}"
    else:
        # Check if root node is not None
        assert kdtree is not None, "Expected a KDNode, got None"

        # Check if root has correct dimensions
        assert len(kdtree.point) == num_dimensions, (
            f"Expected point dimension {num_dimensions}, got {len(kdtree.point)}"
        )

        # Check that the tree is balanced to some extent (simplistic check)
        assert isinstance(kdtree, KDNode), (
            f"Expected KDNode instance, got {type(kdtree)}"
        )


def test_nearest_neighbour_search():