mlpack: a scalable C++ machine learning library

Python wrapper for Boost Geometry Rtree

Finding nearest city to a position in android using k-d trees

Near neighbor searching and clustering using LSH

An image similarity search engine has been developed that finds images similar to the image selected by the user from the dataset provided by the user. The latent vectors of the images are generated by a CNN based autoencoder model. KNN is used to find images similar to the selected image. An image similarity search engine platform has been created

Nearest Neighbor and Range Queries using 2d-trees.

A project based off of the multiple vehicle routing problem with time windows and constraints.

v2vk(Vevtor2Vector Kit), annoy server (or faiss server in future).

Nearest neighbor search (NNS)

Simple standalone multi-threaded locality sensitive hashing implementation in Rust

Second Attempt for a project involving the use of algorithms for a business solution, package delivery tracker.

Vector space modeling of MovieLens & IMDB movie data

📈|Time Series - Nearest neighbor search and Clustering using LSH, Hypercube (and Lloyd's only at the clustering) algorithms with metrics: L2, Discrete and Continuous Fréchet.

First assignment for the University Senior Project course

Lab works on Information Processes Analysis subject. Taught on 1st sem of applied mathematics master programm.

A 2020 University Project.

K-Nearest Neighbors Algorithm (KNN) is a non-parametric classification method First Developed by Evelyn Fix and Joseph Hodges in 1951, and later expanded by Thomas Cover. It is used for Classification and Regression. In both cases, the input consists of the k closest training examples in a data set.