Python/data_structures/kd_tree/example/hypercube_points.py
Ramy 976e385c1d
Implemented Suffix Tree Data Structure ()
* Implemented KD-Tree Data Structure

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Co-authored-by: Christian Clauss <cclauss@me.com>

* Update data_structures/kd_tree/example/hypercube_points.py

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* Implemented Suffix Tree Data Structure.
Added some comments to my files in , .

* updating DIRECTORY.md

* Implemented Suffix Tree Data Structure.
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---------

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 15:37:00 +02:00

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Python

# Created by: Ramy-Badr-Ahmed (https://github.com/Ramy-Badr-Ahmed)
# in Pull Request: #11532
# https://github.com/TheAlgorithms/Python/pull/11532
#
# Please mention me (@Ramy-Badr-Ahmed) in any issue or pull request
# addressing bugs/corrections to this file.
# Thank you!
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)