# OpenSource KD tree implementation.

Post about my Open-Source KD tree implementation.

# Introduction

This is a post dedicated to giving a small comment about the implementation of the KD-tree way for space partitioning (also known as spatial indexing) available at: https://github.com/burlachenkok/lw_index_datastructs

This algorithm was proposed by Jon Bentley when he was an undergraduate student at Stanford under the supervision of Donald Knuth. Today, it’s a very suitable algorithm for indexing structures used in Database Management Systems, Graphics, Games, Visualization, Physics simulation, etc. In this video, prof. Jerome Friedman 2004 remembers that his work intersects in terms of motivation with the work of Jon Bentley.

# Thoretical computation demand

KD trees allow todo Insert/Search/Delete points in Euclidian space (\(R^d\)) typically in \(~\log(N)\) iterations. At each iteration:

- You compare specified coordinate \(\mathcal{O}(1)\)
- You evaluate \(L_2\) norm or another norm and perform search pruning based on exploiting properties of the norm (See implementation). So if we take into account dimension \(d\) to evaluate \(L_2\) norm of a vector, then usually (if not exploit some other properties), it will take \(\mathcal{O}(d)\) single operations
- But both these two numbers in case of \(d < \log(N)\) can be hidden into \(\mathcal{O}(1)\)

# Range Count / Range search.

Find all \(R\) points from all \(N\) points that lie in a specific range typically costs \(\sim R+\log(N)\), but the worst case is: \(\sim R+N^{0.5}\)

# Applications

Classical applications are everywhere where we use geometric data:

- Ray-tracing
- 2d range search
- Collision detection
- Nearest neighbors search, etc
- n-body simulation algorithm
- Search in databases
- Computer Graphics

But KD tree generalizes for a higher dimension, and this implementation supports it. Based on my knowledge from Datamining lectures CS246 from prof. Jure Leskovec in high dimensional space is almost true that everything is very far from everything, so if you choose to use for **K Nearest Neighbors**
https://sites.google.com/site/burlachenkok/k-nearest-neighbors-and-things-around-it and you choose for distance function or dissimilarity metric is based on Eculidan norm in \(R^d\) then this library will help you for your C++ implementation (if C++ is your choice).