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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>
80 lines
2.7 KiB
Python
80 lines
2.7 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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from data_structures.kd_tree.kd_node import KDNode
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def nearest_neighbour_search(
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root: KDNode | None, query_point: list[float]
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) -> tuple[list[float] | None, float, int]:
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"""
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Performs a nearest neighbor search in a KD-Tree for a given query point.
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Args:
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root (KDNode | None): The root node of the KD-Tree.
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query_point (list[float]): The point for which the nearest neighbor
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is being searched.
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Returns:
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tuple[list[float] | None, float, int]:
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- The nearest point found in the KD-Tree to the query point,
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or None if no point is found.
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- The squared distance to the nearest point.
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- The number of nodes visited during the search.
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"""
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nearest_point: list[float] | None = None
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nearest_dist: float = float("inf")
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nodes_visited: int = 0
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def search(node: KDNode | None, depth: int = 0) -> None:
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"""
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Recursively searches for the nearest neighbor in the KD-Tree.
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Args:
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node: The current node in the KD-Tree.
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depth: The current depth in the KD-Tree.
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"""
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nonlocal nearest_point, nearest_dist, nodes_visited
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if node is None:
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return
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nodes_visited += 1
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# Calculate the current distance (squared distance)
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current_point = node.point
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current_dist = sum(
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(query_coord - point_coord) ** 2
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for query_coord, point_coord in zip(query_point, current_point)
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)
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# Update nearest point if the current node is closer
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if nearest_point is None or current_dist < nearest_dist:
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nearest_point = current_point
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nearest_dist = current_dist
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# Determine which subtree to search first (based on axis and query point)
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k = len(query_point) # Dimensionality of points
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axis = depth % k
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if query_point[axis] <= current_point[axis]:
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nearer_subtree = node.left
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further_subtree = node.right
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else:
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nearer_subtree = node.right
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further_subtree = node.left
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# Search the nearer subtree first
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search(nearer_subtree, depth + 1)
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# If the further subtree has a closer point
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if (query_point[axis] - current_point[axis]) ** 2 < nearest_dist:
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search(further_subtree, depth + 1)
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search(root, 0)
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return nearest_point, nearest_dist, nodes_visited
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