Welcome to som-pbc’s documentation!

som-pbc

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Authors: Alex Müller

Copyright: (c) 2017 - 2021; 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

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