Welcome to scikit-learning vector quantization’s documentation!

Scikit-learning vector quantization (sklvq) is a scikit-learn compatible and expandable implementation of Learning Vector Quantization (LVQ) algorithms. The main purpose is to make it easier to compare results by providing a central point for the implementations of the LVQ algorithms.

Currently the package implements three algorithms from the LVQ family, all based on the generalized learning objective, i.e., Generalized Learning Vector Quantization (GLVQ), Generalized Matrix LVQ (GMLVQ) and Local Generalized Matrix LVQ (LGMLVQ).

The package provides a number of activation, discriminant, distance, and solver methods. Please see the Getting started, Documentation, and Tutorial - Examples sections on the left side.