nanoflann: a C++11 header-only library for Nearest Neighbor (NN) search with KD-trees

Absolute balanced kdtree for fast kNN search.

CUDA based Iterative Closest Point Algorithm Implementation

Built text and image clustering models using unsupervised machine learning algorithms such as nearest neighbors, k means, LDA , and used techniques such as expectation maximization, locality sensitive hashing, and gibbs sampling in Python

Scalable Nearest Neighbor Algorithms for High Dimensional Data

Policy Iteration for Continuous Dynamics

An efficient Nearest Neighbor Classifier for the MINST dataset. It uses a VP Tree data structure for preprocessing, thus improving query time complexity

Header-only C++ K-d tree library

🌳PHP K-D Tree implementation

KD-tree with RGB colors

Answers to assignments for Princeton University Algorithm Course

Simple kd-Tree interface implemented in C++

Containing all the coding projects and assignments I've completed in COS 226 @ Princeton (Data Structures & Algorithms)

Implementation of Kd 2^n-Tree Structure for the Programming Languages subject.

Projecte de l'assignatura d'algorismia de la FIB, Q1 2023-2024

Kd-Tree

nanoflann: a C++11 header-only library for Nearest Neighbor (NN) search wih KD-trees

This implementation creates a KDTree object using a vector of Point3D objects, and then searches for the nearest neighbor to a query point using the nearestNeighbor function. The nearestNeighbor function uses a recursive search algorithm to traverse the kd-tree and find the closest point to the query point.