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Python package for unsupervised and supervised self-organizing maps (SOM)

Project description

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SUSI: SUpervised Self-organIzing maps in Python

Python package for unsupervised and supervised self-organizing maps (SOM)


We present the SUSI package for Python. It includes a fully functional SOM for unsupervised and supervised tasks. The class structure is set up as follows:

  • SOMClustering: Unsupervised SOM for clustering

    • SOMEstimator: Base class for supervised SOMs

      • SOMRegressor: Regression SOM

      • SOMClassifier: Classification SOM


3-Clause BSD license


Felix M. Riese


see Citation and in the bibtex file




Installation guidelines




pip install susi

More information can be found in the installation guidelines.


A collection of code examples can be found in the documentation. Code examples as Jupyter Notebooks can be found here:


The bibtex file including both references is available here.


Felix M. Riese and S. Keller, “SUSI: Supervised Self-Organizing Maps for Regression and Classification in Python”, arXiv:1903.11114, 2019. Submitted to an ISPRS conference.

    author = {Riese, Felix~M. and Keller, Sina},
    title = {SUSI: Supervised Self-Organizing Maps for Regression and Classification in Python},
    year = {2019},
    archivePrefix = {arXiv},
    eprint = {1903.11114},
    primaryClass = {cs.LG},
    url = {}


Felix M. Riese, “SUSI: SUpervised Self-organIzing maps in Python”, 10.5281/zenodo.2609130, 2019.
    author = {Riese, Felix~M.},
    title = {{SUSI: SUpervised Self-organIzing maps in Python}},
    year = {2019},
    DOI = {10.5281/zenodo.2609130},
    publisher = {Zenodo},
    howpublished = {\href{}{}}

Change Log

[1.0.6] - 2019-09-11

  • [ADDED] Semi-supervised abilities for classifier and regressor

  • [ADDED] Example notebooks for semi-supervised applications

  • [ADDED] Tests for example notebooks

  • [FIXED] Code looks better in documentation with sphinx.ext.napoleon

  • [CHANGED] Requirements for the SUSI package

  • [REMOVED] Support for Python 3.4

[1.0.5] - 2019-04-23

  • [ADDED] PCA initialization of the SOM weights with 2 principal components

  • [ADDED] Variable variance

  • [CHANGED] Moved installation guidelines and examples to documentation

[1.0.4] - 2019-04-21

  • [ADDED] Batch algorithm for unsupervised and supervised SOM

  • [ADDED] Calculation of the unified distance matrix (u-matrix)

  • [FIXED] Added estimator_check of scikit-learn and fixed recognized issues

[1.0.3] - 2019-04-09

  • [ADDED] Link to arXiv paper

  • [ADDED] Mexican-hat neighborhood distance weight

  • [ADDED] Possibility for different initialization modes

  • [CHANGED] Simplified initialization of estimators

  • [FIXED] URLs and styles in documentation

  • [FIXED] Colormap in Salinas example

[1.0.2] - 2019-03-27

  • [CHANGED] Moved decreasing_rate() out of SOM classes

  • [FIXED] Removed duplicate constructor for SOMRegressor, fixed fit() params

  • [ADDED] Codecov, Codacy

[1.0.1] - 2019-03-26

  • [ADDED] Config file for Travis

  • [ADDED] Requirements for read-the-docs documentation

[1.0.0] - 2019-03-26

  • Initial release

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