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

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

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* 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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* Implementation of the suffix tree data structure

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

* updating DIRECTORY.md

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

---------

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

44 lines
1.3 KiB
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!
from data_structures.kd_tree.kd_node import KDNode
def build_kdtree(points: list[list[float]], depth: int = 0) -> KDNode | None:
"""
Builds a KD-Tree from a list of points.
Args:
points: The list of points to build the KD-Tree from.
depth: The current depth in the tree
(used to determine axis for splitting).
Returns:
The root node of the KD-Tree,
or None if no points are provided.
"""
if not points:
return None
k = len(points[0]) # Dimensionality of the points
axis = depth % k
# Sort point list and choose median as pivot element
points.sort(key=lambda point: point[axis])
median_idx = len(points) // 2
# Create node and construct subtrees
left_points = points[:median_idx]
right_points = points[median_idx + 1 :]
return KDNode(
point=points[median_idx],
left=build_kdtree(left_points, depth + 1),
right=build_kdtree(right_points, depth + 1),
)