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.
A detailed README file describes the usage of the software, including license, requirements, installation, file formats, sample data, tools, and options. With the sample data provided and the default options, it is possible to test the code immediately as a demo.
DRVQ has a 2-clause BSD license. Please refer to the DRVQ software home page, the research project, or the original publication for more information. The latest code is available at github.
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