SOMPY

A Python Library for Self Organizing Map (SOM)

As much as possible, the structure of SOM is similar to somtoolbox in Matlab. It has the following functionalities:

1. Only Batch training, which is faster than online training. It has parallel processing option similar to sklearn format and it speeds up the training procedure, but it depends on the data size and mainly the size of the SOM grid.I couldn't manage the memory problem and therefore, I recommend single core processing at the moment. But nevertheless, the implementation of the algorithm is carefully done for all those important matrix calculations, such as scipy sparse matrix and numexpr for calculation of Euclidean distance.
2. PCA (or RandomPCA (default)) initialization, using sklearn or random initialization.
3. component plane visualization (different modes).
4. Hitmap.
5. U-Matrix visualization.
6. 1-d or 2-d SOM with only rectangular, planar grid. (works well in comparison with hexagonal shape, when I was checking in Matlab with somtoolbox).
7. Different methods for function approximation and predictions (mostly using Sklearn).

Dependencies:

SOMPY has the following dependencies (tested only with Python 2.7x):

• numpy
• scipy
• scikit-learn
• numexpr
• matplotlib
• pandas
• ipdb

Installation:

python setup.py install

Many thanks to @sebastiandev, the library is now standardized in a pythonic tradition. Below you can see some basic examples, showing how to use the library. But I recommend you to go through the codes. There are several functionalities already implemented, but not documented. I would be very happy to add your new examples here.

Basic Example

Citation

There is no published paper about this library. However if possible, please cite the library as follows:

Main Contributers:
Vahid Moosavi @sevamoo
Sebastian Packmann @sebastiandev
Iván Vallés @ivallesp

For more information, you can contact me via sevamoo@gmail.com or svm@arch.ethz.ch, but please report an issue first.

Thanks a lot. Best Vahid Moosavi