https://github.com/ramy-badr-ahmed/kd-tree-python

https://github.com/ramy-badr-ahmed/kd-tree-python

binary-space-partitioning data-structures-and-algorithms hypercube-points kd-tree nearest-neighbor-search python space-partitioning spatial-indexing

Last synced: about 1 month ago
JSON representation

• Host: GitHub
• URL: https://github.com/ramy-badr-ahmed/kd-tree-python
• Owner: Ramy-Badr-Ahmed
• License: apache-2.0
• Created: 2024-08-28T10:42:01.000Z (4 months ago)
• Default Branch: main
• Last Pushed: 2024-08-28T19:02:15.000Z (4 months ago)
• Last Synced: 2024-08-29T12:15:44.808Z (4 months ago)
• Topics: binary-space-partitioning, data-structures-and-algorithms, hypercube-points, kd-tree, nearest-neighbor-search, python, space-partitioning, spatial-indexing
• Language: Python
• Homepage:
• Size: 13.7 KB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

Awesome Lists containing this project

README

        
![Python](https://img.shields.io/badge/Python-3670A0?style=plastic&logo=python&logoColor=ffdd54) ![NumPy](https://img.shields.io/badge/Numpy-777BB4.svg?style=plastic&logo=numpy&logoColor=white) ![GitHub](https://img.shields.io/github/license/Ramy-Badr-Ahmed/KD-Tree-Python?style=plastic&cached)

[![DOI](https://zenodo.org/badge/DOI/10.5281/zenodo.13384095.svg)](https://doi.org/10.5281/zenodo.13384095)

# KD-Tree Implementation in Python

This repository contains Python implementation of the kd-tree data structure and performing k-nearest neighbour search.

Its Matlab implementation is located here: [KD-Tree-Matlab](https://github.com/Ramy-Badr-Ahmed/KD-Tree-Matlab)

### About

The kd-tree is a space-partitioning data structure for organizing points in a k-dimensional space.

> [Mathematica Link](https://reference.wolfram.com/language/ref/datastructure/KDTree.html)

### Scripts

1. `build_kdtree.py`

   > Builds a kd-tree from a set of points.

2. `nearest_neighbour_search.py`

   > Performs nearest neighbour search using the built kd-tree.

3. `hypercube_points.py`

   > Generates n-Dimensional Points Uniformly in an n-Dimensional Hypercube.

### Example Usage

```python

from kdtree.build_kdtree import build_kdtree

from kdtree.nearest_neighbor_search import nearest_neighbor_search

from examples.hypercube_points import hypercube_points

num_points = 5000

cube_size = 10

num_dimensions = 10

points = hypercube_points(num_points, cube_size, num_dimensions)

hypercube_kdtree = build_kdtree(points.tolist())

query_point = np.random.rand(num_dimensions).tolist()

nearest_point, nearest_dist, nodes_visited = nearest_neighbor_search(hypercube_kdtree, query_point)

print(f"Query point: {query_point}")

print(f"Nearest point: {nearest_point}")

print(f"Distance: {nearest_dist:.4f}")

print(f"Nodes visited: {nodes_visited}")