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

KD-Tree Implementation in MATLAB - https://doi.org/10.5281/zenodo.12808902
https://github.com/ramy-badr-ahmed/kd-tree-matlab

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

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KD-Tree Implementation in MATLAB - https://doi.org/10.5281/zenodo.12808902

• Host: GitHub
• URL: https://github.com/ramy-badr-ahmed/kd-tree-matlab
• Owner: Ramy-Badr-Ahmed
• License: apache-2.0
• Created: 2024-07-15T21:44:56.000Z (5 months ago)
• Default Branch: main
• Last Pushed: 2024-08-12T09:35:47.000Z (4 months ago)
• Last Synced: 2024-10-25T04:24:25.790Z (about 2 months ago)
• Topics: binary-space-partitioning, data-structures-and-algorithms, hypercube-points, kd-tree, matlab, nearest-neighbor-search, space-partitioning, spatial-indexing
• Language: MATLAB
• Homepage:
• Size: 12.7 KB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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# KD-Tree Implementation in MATLAB

This repository contains MATLAB scripts for implementing and using a kd-tree data structure and implements k-nearest neighbour search.

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

### About

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

### Scripts

1. `buildKDTree.m`

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

2. `nearestNeighborSearch.m`

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

3. `hypercubePoints.m`

   > Showcases an application to generate n-Dimensional Points Uniformly in an n-Dimensional Hypercube.

### Example Usage

```matlab

numPoints = 5000;

cubeSize = 10;

numDimensions = 10;

points = hypercubePoints(numPoints, cubeSize, numDimensions);

hypercubeKDTree = buildKDTree(points);

queryPoint = rand(1, numDimensions);  % for the k-nearest neighbour search

[nearestPoint, nearestDist, nodesVisited] = nearestNeighbourSearch(hypercubeKDTree, queryPoint);

fprintf('Points in KD-Tree:\n');

fprintf('Query point: (%.2f, %.2f, %.2f)\n', queryPoint);

fprintf('Nearest point: (%.2f, %.2f, %.2f)\n', nearestPoint);

fprintf('Distance: %.4f\n', nearestDist);

fprintf('Nodes visited: %d\n', nodesVisited);

```