About: The bufferkdtree package is a Python library that aims at accelerating nearest neighbor computations using both k-d trees and modern many-core devices such as graphics processing units (GPUs).

Initial Announcement on mloss.org.

About: DRVQ is a C++ library implementation of dimensionality-recursive vector quantization, a fast vector quantization method in high-dimensional Euclidean spaces under arbitrary data distributions. It is an approximation of k-means that is practically constant in data size and applies to arbitrarily high dimensions but can only scale to a few thousands of centroids. As a by-product of training, a tree structure performs either exact or approximate quantization on trained centroids, the latter being not very precise but extremely fast.

Initial Announcement on mloss.org.

About: FLANN is a library for performing fast approximate nearest neighbor searches in high dimensional spaces. It contains a collection of algorithms we found to work best for nearest neighbor search.

See project page for changes.

About: Epistatic miniarray profiles (E-MAPs) are a high-throughput approach capable of quantifying aggravating or alleviating genetic interactions between gene pairs. The datasets resulting from E-MAP experiments typically take the form of a symmetric pairwise matrix of interaction scores. These datasets have a significant number of missing values - up to 35% - that can reduce the effectiveness of some data analysis techniques and prevent the use of others. This project contains nearest neighbor based tools for the imputation and prediction of these missing values. The code is implemented in Python and uses a nearest neighbor based approach. Two variants are used - a simple weighted nearest neighbors, and a local least squares based regression.

Initial Announcement on mloss.org.