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# 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()