/
NikolayIvkin
/
TheAlgorithms_Python
Обзор
Документация
Войти
/
NikolayIvkin
/
TheAlgorithms_Python
Код
Запросы
2
Задачи
Вики
Пакеты
0
Релизы
0
Аналитика
Безопасность
master
data_structures/kd_tree/example/example_usage.py
46 строк
2 KB
Ramy
Implemented Suffix Tree Data Structure (#11554)
28 сен 2024, 16:37
Не верифицирован
28 сен 2024, 16:37
976e385
Код
Авторство
О чём код?
# 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()