Skip to main content

A library of vector quantizers

Project description

# py-kohonen

This module contains some basic implementations of Kohonen-style vector quantizers: Self-Organizing Map (SOM), Neural Gas, and Growing Neural Gas. Kohonen-style vector quantizers use some sort of explicitly specified topology to encourage good separation among prototype “neurons”.

Vector quantizers are useful for learning discrete representations of a distribution over continuous space, based solely on samples drawn from the distribution. This process is also generally known as density estimation.

The source distribution includes an interactive test module that uses PyGTK and Cairo to render a set of quantizers that move around in real time as samples are drawn from a known distribution and fed to the quantizers. Run this test with :

python kohonen_test.py

Have fun !

Project details


Release history Release notifications

Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Files for kohonen, version 1.1.2
Filename, size File type Python version Upload date Hashes
Filename, size kohonen-1.1.2-py3-none-any.whl (11.7 kB) File type Wheel Python version 3.4 Upload date Hashes View hashes
Filename, size kohonen-1.1.2.tar.gz (10.0 kB) File type Source Python version None Upload date Hashes View hashes

Supported by

Elastic Elastic Search Pingdom Pingdom Monitoring Google Google BigQuery Sentry Sentry Error logging AWS AWS Cloud computing DataDog DataDog Monitoring Fastly Fastly CDN DigiCert DigiCert EV certificate StatusPage StatusPage Status page