| Name | sompy-package JSON |
| Version |
1.0.3
JSON |
| download |
| home_page | None |
| Summary | Self Organizing Maps Package |
| upload_time | 2024-09-02 13:43:48 |
| maintainer | None |
| docs_url | None |
| author | None |
| requires_python | >=3.8 |
| license | None |
| keywords |
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| VCS |
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| bugtrack_url |
|
| requirements |
No requirements were recorded.
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SOMPY
-----
This repository was forked from the original repository at https://github.com/sevamoo/SOMPY to modify it in order to make the package installable via pip.
Changes
1. Changed package name to "sompy-package" to avoid conflict with already existing "sompy" package on PyPI
2. Refactored package replacing deprecated setup.py with pyproject.toml
3. Updated dependencies to include scikit-image
4. General code cleanup and formatting
WIP:
1. Upload package to PyPI
All of the following is the original README file from the creator of the repo.
-----
## Original README
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:
- numpy
- scipy
- scikit-learn
- numexpr
- matplotlib
- pandas
- ipdb
### Installation:
```Python
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](https://gist.github.com/sevamoo/035c56e7428318dd3065013625f12a11)
### Citation
There is no published paper about this library. However if possible, please cite the library as follows:
```
@misc{moosavi2014sompy,
title={SOMPY: A Python Library for Self Organizing Map (SOM)},
author={Moosavi, V and Packmann, S and Vall{\'e}s, I},
note={GitHub.[Online]. Available: https://github.com/sevamoo/SOMPY},
year={2014}
}
```
For more information, you can contact me via sevamoo@gmail.com but please report an issue first.
Thanks a lot.
Best Vahid Moosavi
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