Displaying 1 to 9 from 9 results

kmcuda - Large scale K-means and K-nn implementation on NVIDIA GPU / CUDA

  •    Jupyter

K-means implementation is based on "Yinyang K-Means: A Drop-In Replacement of the Classic K-Means with Consistent Speedup". While it introduces some overhead and many conditional clauses which are bad for CUDA, it still shows 1.6-2x speedup against the Lloyd algorithm. K-nearest neighbors employ the same triangle inequality idea and require precalculated centroids and cluster assignments, similar to the flattened ball tree. Technically, this project is a shared library which exports two functions defined in kmcuda.h: kmeans_cuda and knn_cuda. It has built-in Python3 and R native extension support, so you can from libKMCUDA import kmeans_cuda or dyn.load("libKMCUDA.so").

Non-Metric Space Library (NMSLIB) - An efficient similarity search library and a toolkit for evaluation of k-NN methods for generic non-metric spaces.

  •    C++

Non-Metric Space Library (NMSLIB) is an efficient cross-platform similarity search library and a toolkit for evaluation of similarity search methods. The core-library does not have any third-party dependencies. It has been gaining popularity recently. In particular, it has become a part of Amazon Elasticsearch Service.

knn4qa - k-nearest neighbor search for question answering (QA) and information retrieval (IR)

  •    Java

This is a learning-to-rank pipeline, which is a part of the project where we study applicability of k-nearest neighbor search methods in IR and QA applications. This project is supported primarily by the NSF grant #1618159 : "Matching and Ranking via Proximity Graphs: Applications to Question Answering and Beyond". For more details, please, check the Wiki page.

NearestNeighbors.jl - High performance nearest neighbor data structures and algorithms for Julia.

  •    Julia

NearestNeighbors.jl is a package written in Julia to perform high performance nearest neighbor searches in arbitrarily high dimensions. All trees in NearestNeighbors.jl are static which means that points can not be added or removed from an already created tree.


  •    CSharp

.Net library for fast approximate nearest neighbours search. Exact k nearest neighbours search algorithms tend to perform poorly in high-dimensional spaces. To overcome curse of dimensionality the ANN algorithms come in place. This library implements one of such algorithms described in the "Efficient and robust approximate nearest neighbor search using Hierarchical Navigable Small World graphs" article. It provides simple API for building nearest neighbours graphs, (de)serializing them and running k-NN search queries.

vpsearch - C library for finding nearest (most similar) element in a set

  •    Rust

A relatively simple and readable Rust implementation of Vantage Point tree search algorithm. The VP tree algorithm doesn't need to know coordinates of items, only distances between them. It can efficiently search multi-dimensional spaces and abstract things as long as you can define similarity between them (e.g. points, colors, and even images).

kdtree - Absolute balanced kdtree for fast kNN search.

  •    C

This is a (nearly absolute) balanced kdtree for fast kNN search with bad performance for dynamic addition and removal. In fact we adopt quick sort to rebuild the whole tree after changes of the nodes. We cache the added or the deleted nodes which will not be actually mapped into the tree until the rebuild method to be invoked. The good thing is we can always keep the tree balanced, and the bad thing is we have to wait some time for the finish of tree rebuild. Moreover duplicated samples are allowed to be added with the tree still kept balanced. The thought of the implementation is posted here.

NGT - Neighborhood Graph and Tree for Indexing High-dimensional Data

  •    C++

NGT provides commands and a library for performing high-speed approximate nearest neighbor searches against a large volume of data (several million to several 10 million items of data) in high dimensional vector data space (several ten to several thousand dimensions). The index can be placed in shared memory. Using shared memory can reduce the amount of memory needed when multiple processes are using the same index. It can also improve the boot-up speed of an index for a large volume of registration data. Since changes become necessary at build time, please add the following parameter when executing "cmake" in order to use shared memory.

knn-cpp - A header-only C++ library for k nearest neighbor search with Eigen3.

  •    C++

knn-cpp is a header-only C++ library for k nearest neighbor search using the Eigen3 library. The library requires Eigen3 to be installed on your system.

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