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 :
Have fun !
TODO: Figure out how to actually get changelog content.
Changelog content for this version goes here.
TODO: Brief introduction on what you do with files - including link to relevant help section.
|File Name & Checksum SHA256 Checksum Help||Version||File Type||Upload Date|
|kohonen-1.1.2-py3-none-any.whl (11.7 kB) Copy SHA256 Checksum SHA256||3.4||Wheel||Dec 15, 2014|
|kohonen-1.1.2.tar.gz (10.0 kB) Copy SHA256 Checksum SHA256||–||Source||Dec 15, 2014|