Welcome to som-pbc’s documentation!

som-pbc

Authors: Alex Müller

Copyright: (c) 2017 - 2022; Alex Müller

This package contains a simple self-organizing map implementation in Python with periodic boundary conditions.

Self-organizing maps are also called Kohonen maps and were invented by Teuvo Kohonen.[1] They are an unsupervised machine learning technique to efficiently create spatially organized internal representations of various types of data. For example, SOMs are well-suited for the visualization of high-dimensional data.

This is a simple implementation of SOMs in Python. This SOM has periodic boundary conditions and therefore can be imagined as a “donut”. The implementation uses numpy, scipy, scikit-learn and matplotlib.

The project’s GitHub page can be found here: http://github.com/alexarnimueller/som

Table of Contents:

• 1. som-pbc README
  • 1.1. Installation
  • 1.2. Usage
  • 1.3. Methods / Functions
  • 1.4. References:
  • 1.5. Documentation:
• 2. Documentation for the som module
  • SOM
  • man_dist_pbc()
• 3. Example Scripts
• 4. LICENSE
• 5. TODOs

Indices and tables

• Index

• Module Index

• Search Page