Implemented Suffix Tree Data Structure (#11554)
* Implemented KD-Tree Data Structure
* Implemented KD-Tree Data Structure. updated DIRECTORY.md.
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* 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>
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* Implementation of the suffix tree data structure
* Adding data to DIRECTORY.md
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* 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>
2024-09-28 13:37:00 +00:00
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# 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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kd tree data structure implementation (#11532)
* 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
---------
Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
Co-authored-by: Christian Clauss <cclauss@me.com>
2024-09-03 12:39:09 +00:00
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from data_structures.kd_tree.kd_node import KDNode
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def build_kdtree(points: list[list[float]], depth: int = 0) -> KDNode | None:
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"""
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Builds a KD-Tree from a list of points.
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Args:
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points: The list of points to build the KD-Tree from.
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depth: The current depth in the tree
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(used to determine axis for splitting).
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Returns:
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The root node of the KD-Tree,
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or None if no points are provided.
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"""
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if not points:
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return None
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k = len(points[0]) # Dimensionality of the points
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axis = depth % k
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# Sort point list and choose median as pivot element
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points.sort(key=lambda point: point[axis])
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median_idx = len(points) // 2
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# Create node and construct subtrees
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left_points = points[:median_idx]
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right_points = points[median_idx + 1 :]
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return KDNode(
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point=points[median_idx],
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left=build_kdtree(left_points, depth + 1),
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right=build_kdtree(right_points, depth + 1),
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)
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