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109 lines
3.1 KiB
Python
109 lines
3.1 KiB
Python
# Created by: Ramy-Badr-Ahmed (https://github.com/Ramy-Badr-Ahmed)
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# in Pull Request: #11532
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# https://github.com/TheAlgorithms/Python/pull/11532
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#
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# Please mention me (@Ramy-Badr-Ahmed) in any issue or pull request
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# addressing bugs/corrections to this file.
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# Thank you!
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import numpy as np
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import pytest
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from data_structures.kd_tree.build_kdtree import build_kdtree
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from data_structures.kd_tree.example.hypercube_points import hypercube_points
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from data_structures.kd_tree.kd_node import KDNode
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from data_structures.kd_tree.nearest_neighbour_search import nearest_neighbour_search
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@pytest.mark.parametrize(
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("num_points", "cube_size", "num_dimensions", "depth", "expected_result"),
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[
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(0, 10.0, 2, 0, None), # Empty points list
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(10, 10.0, 2, 2, KDNode), # Depth = 2, 2D points
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(10, 10.0, 3, -2, KDNode), # Depth = -2, 3D points
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],
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)
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def test_build_kdtree(num_points, cube_size, num_dimensions, depth, expected_result):
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"""
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Test that KD-Tree is built correctly.
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Cases:
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- Empty points list.
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- Positive depth value.
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- Negative depth value.
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"""
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points = (
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hypercube_points(num_points, cube_size, num_dimensions).tolist()
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if num_points > 0
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else []
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)
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kdtree = build_kdtree(points, depth=depth)
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if expected_result is None:
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# Empty points list case
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assert kdtree is None, f"Expected None for empty points list, got {kdtree}"
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else:
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# Check if root node is not None
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assert kdtree is not None, "Expected a KDNode, got None"
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# Check if root has correct dimensions
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assert len(kdtree.point) == num_dimensions, (
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f"Expected point dimension {num_dimensions}, got {len(kdtree.point)}"
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)
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# Check that the tree is balanced to some extent (simplistic check)
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assert isinstance(kdtree, KDNode), (
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f"Expected KDNode instance, got {type(kdtree)}"
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)
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def test_nearest_neighbour_search():
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"""
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Test the nearest neighbor search function.
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"""
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num_points = 10
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cube_size = 10.0
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num_dimensions = 2
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points = hypercube_points(num_points, cube_size, num_dimensions)
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kdtree = build_kdtree(points.tolist())
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rng = np.random.default_rng()
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query_point = rng.random(num_dimensions).tolist()
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nearest_point, nearest_dist, nodes_visited = nearest_neighbour_search(
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kdtree, query_point
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)
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# Check that nearest point is not None
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assert nearest_point is not None
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# Check that distance is a non-negative number
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assert nearest_dist >= 0
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# Check that nodes visited is a non-negative integer
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assert nodes_visited >= 0
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def test_edge_cases():
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"""
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Test edge cases such as an empty KD-Tree.
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"""
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empty_kdtree = build_kdtree([])
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query_point = [0.0] * 2 # Using a default 2D query point
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nearest_point, nearest_dist, nodes_visited = nearest_neighbour_search(
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empty_kdtree, query_point
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)
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# With an empty KD-Tree, nearest_point should be None
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assert nearest_point is None
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assert nearest_dist == float("inf")
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assert nodes_visited == 0
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if __name__ == "__main__":
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import pytest
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pytest.main()
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