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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>
30 lines
909 B
Python
30 lines
909 B
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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def hypercube_points(
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num_points: int, hypercube_size: float, num_dimensions: int
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) -> np.ndarray:
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"""
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Generates random points uniformly distributed within an n-dimensional hypercube.
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Args:
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num_points: Number of points to generate.
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hypercube_size: Size of the hypercube.
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num_dimensions: Number of dimensions of the hypercube.
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Returns:
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An array of shape (num_points, num_dimensions)
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with generated points.
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"""
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rng = np.random.default_rng()
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shape = (num_points, num_dimensions)
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return hypercube_size * rng.random(shape)
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