example_usage.py

# 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

from data_structures.kd_tree.build_kdtree import build_kdtree
from data_structures.kd_tree.example.hypercube_points import hypercube_points
from data_structures.kd_tree.nearest_neighbour_search import nearest_neighbour_search


def main() -> None:
 """
 Demonstrates the use of KD-Tree by building it from random points
 in a 10-dimensional hypercube and performing a nearest neighbor search.
 """
 num_points: int = 5000
 cube_size: float = 10.0 # Size of the hypercube (edge length)
 num_dimensions: int = 10

 # Generate random points within the hypercube
 points: np.ndarray = hypercube_points(num_points, cube_size, num_dimensions)
 hypercube_kdtree = build_kdtree(points.tolist())

 # Generate a random query point within the same space
 rng = np.random.default_rng()
 query_point: list[float] = rng.random(num_dimensions).tolist()

 # Perform nearest neighbor search
 nearest_point, nearest_dist, nodes_visited = nearest_neighbour_search(
 hypercube_kdtree, query_point
 )

 # Print the results
 print(f"Query point: {query_point}")
 print(f"Nearest point: {nearest_point}")
 print(f"Distance: {nearest_dist:.4f}")
 print(f"Nodes visited: {nodes_visited}")


if __name__ == "__main__":
 main()