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kd tree data structure implementation (#11532)

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* Implemented KD-Tree Data Structure

* Implemented KD-Tree Data Structure. updated DIRECTORY.md.

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* Create __init__.py

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* 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

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* docstring for search()

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* Added tests. Updated docstrings/typehints

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* updated tests and used | for type annotations

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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

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* 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.

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* Considered ruff errors

* Considered ruff errors

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* Update kd_node.py

* imported annotations from __future__

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---------

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Co-authored-by: Christian Clauss <cclauss@me.com>
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Ramy
2024-09-03 14:39:09 +02:00
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data_structures/kd_tree/example/hypercube_points.py Normal file
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import numpy as np
def hypercube_points(
num_points: int, hypercube_size: float, num_dimensions: int
) -> np.ndarray:
"""
Generates random points uniformly distributed within an n-dimensional hypercube.
Args:
num_points: Number of points to generate.
hypercube_size: Size of the hypercube.
num_dimensions: Number of dimensions of the hypercube.
Returns:
An array of shape (num_points, num_dimensions)
with generated points.
"""
rng = np.random.default_rng()
shape = (num_points, num_dimensions)
return hypercube_size * rng.random(shape)