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* Implemented KD-Tree Data Structure * Implemented KD-Tree Data Structure. updated DIRECTORY.md. * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * Create __init__.py * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * Replaced legacy `np.random.rand` call with `np.random.Generator` in kd_tree/example_usage.py * Replaced legacy `np.random.rand` call with `np.random.Generator` in kd_tree/hypercube_points.py * added typehints and docstrings * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * docstring for search() * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * Added tests. Updated docstrings/typehints * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * updated tests and used | for type annotations * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * E501 for build_kdtree.py, hypercube_points.py, nearest_neighbour_search.py * I001 for example_usage.py and test_kdtree.py * I001 for example_usage.py and test_kdtree.py * Update data_structures/kd_tree/build_kdtree.py Co-authored-by: Christian Clauss <cclauss@me.com> * Update data_structures/kd_tree/example/hypercube_points.py Co-authored-by: Christian Clauss <cclauss@me.com> * Update data_structures/kd_tree/example/hypercube_points.py Co-authored-by: Christian Clauss <cclauss@me.com> * Added new test cases requested in Review. Refactored the test_build_kdtree() to include various checks. * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * Considered ruff errors * Considered ruff errors * Apply suggestions from code review * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * Update kd_node.py * imported annotations from __future__ * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * Implementation of the suffix tree data structure * Adding data to DIRECTORY.md * Minor file renaming * minor correction * renaming in DIRECTORY.md * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * Considering ruff part-1 * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * Considering ruff part-2 * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * Considering ruff part-3 * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * Considering ruff part-4 * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * Considering ruff part-5 * Implemented Suffix Tree Data Structure. Added some comments to my files in #11532, #11554. * updating DIRECTORY.md * Implemented Suffix Tree Data Structure. Added some comments to my files in #11532, #11554. --------- Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com> Co-authored-by: Christian Clauss <cclauss@me.com> Co-authored-by: Ramy-Badr-Ahmed <Ramy-Badr-Ahmed@users.noreply.github.com>
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 (
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len(kdtree.point) == num_dimensions
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), f"Expected point dimension {num_dimensions}, got {len(kdtree.point)}"
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# Check that the tree is balanced to some extent (simplistic check)
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assert isinstance(
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kdtree, KDNode
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), f"Expected KDNode instance, got {type(kdtree)}"
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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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