https-github.com-felixriese-susi
https://github.com/dileepdkumar/https-github.com-felixriese-susi
Science Score: 67.0%
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✓DOI references
Found 15 DOI reference(s) in README -
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Links to: mdpi.com, zenodo.org -
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Low similarity (12.1%) to scientific vocabulary
Last synced: 6 months ago
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Repository
Basic Info
- Host: GitHub
- Owner: dileepdkumar
- License: bsd-3-clause
- Language: Python
- Default Branch: main
- Size: 2.86 MB
Statistics
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 31
- Releases: 0
Created almost 4 years ago
· Last pushed 9 months ago
Metadata Files
Readme
Changelog
License
Code of conduct
Citation
README.rst
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:alt: Documentation Status
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:target: https://codecov.io/gh/felixriese/susi
:alt: Codecov
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.. image:: https://anaconda.org/conda-forge/susi/badges/version.svg
:target: https://anaconda.org/conda-forge/susi
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:target: https://github.com/felixriese/susi
:align: right
:alt: SuSi logo
SuSi: Supervised Self-organizing maps in Python
===============================================
Python package for unsupervised, supervised and semi-supervised self-organizing maps (SOM)
Description
-----------
We present the SuSi package for Python.
It includes a fully functional SOM for unsupervised, supervised and semi-supervised tasks:
- SOMClustering: Unsupervised SOM for clustering
- SOMRegressor: (Semi-)Supervised Regression SOM
- SOMClassifier: (Semi-)Supervised Classification SOM
:License:
`3-Clause BSD license `_
:Author:
`Felix M. Riese `_
:Citation:
see `Citation`_ and in the `bibtex `_ file
:Documentation:
`Documentation `_
:Installation:
`Installation guidelines `_
:Paper:
`F. M. Riese, S. Keller and S. Hinz in Remote Sensing, 2020 `_
Installation
------------
Pip
~~~
.. code:: bash
pip3 install susi
.. image:: https://static.pepy.tech/personalized-badge/susi?period=total&units=international_system&left_color=black&right_color=blue&left_text=Downloads
:target: https://pepy.tech/project/susi
:alt: PyPi Downloads
Conda
~~~~~
.. code:: bash
conda install -c conda-forge susi
More information can be found in the `installation guidelines `_.
.. image:: https://img.shields.io/conda/dn/conda-forge/susi.svg
:target: https://anaconda.org/conda-forge/susi
:alt: Conda-Forge Downloads
Examples
--------
A collection of code examples can be found in `the documentation `_.
Code examples as Jupyter Notebooks can be found here:
* `examples/SOMClustering `_
* `examples/SOMRegressor `_
* `examples/SOMRegressor_semisupervised `_
* `examples/SOMRegressor_multioutput `_
* `examples/SOMClassifier `_
* `examples/SOMClassifier_semisupervised `_
FAQs
-----
- **How should I set the initial hyperparameters of a SOM?** For more details
on the hyperparameters, see in `documentation/hyperparameters
`_.
- **How can I optimize the hyperparameters?** The SuSi hyperparameters
can be optimized, for example, with `scikit-learn.model_selection.GridSearchCV
`_,
since the SuSi package is developed according to several scikit-learn
guidelines.
------------
Citation
--------
The bibtex file including both references is available in `bibliography.bib
`_.
**Paper:**
F. M. Riese, S. Keller and S. Hinz, "Supervised and Semi-Supervised Self-Organizing
Maps for Regression and Classification Focusing on Hyperspectral Data",
*Remote Sensing*, vol. 12, no. 1, 2020. `DOI:10.3390/rs12010007
`_
.. code:: bibtex
@article{riese2020supervised,
author = {Riese, Felix~M. and Keller, Sina and Hinz, Stefan},
title = {{Supervised and Semi-Supervised Self-Organizing Maps for
Regression and Classification Focusing on Hyperspectral Data}},
journal = {Remote Sensing},
year = {2020},
volume = {12},
number = {1},
article-number = {7},
URL = {https://www.mdpi.com/2072-4292/12/1/7},
ISSN = {2072-4292},
DOI = {10.3390/rs12010007}
}
**Code:**
Felix M. Riese, "SuSi: SUpervised Self-organIzing maps in Python",
Zenodo, 2019. `DOI:10.5281/zenodo.2609130
`_
.. image:: https://zenodo.org/badge/DOI/10.5281/zenodo.2609130.svg
:target: https://doi.org/10.5281/zenodo.2609130
.. code:: bibtex
@misc{riese2019susicode,
author = {Riese, Felix~M.},
title = {{SuSi: Supervised Self-Organizing Maps in Python}},
year = {2019},
DOI = {10.5281/zenodo.2609130},
publisher = {Zenodo},
howpublished = {\href{https://doi.org/10.5281/zenodo.2609130}{doi.org/10.5281/zenodo.2609130}}
}
-------------
License
-------
This project is published under the `3-Clause BSD `_ license.
.. image:: https://img.shields.io/pypi/l/susi.svg
:target: https://github.com/felixriese/susi/blob/main/LICENSE
:alt: PyPI - License
Owner
- Login: dileepdkumar
- Kind: user
- Repositories: 50
- Profile: https://github.com/dileepdkumar
Citation (CITATION.cff)
cff-version: 1.2.0
message: "If you use this software, please cite both the article from preferred-citation and the software itself."
authors:
- family-names: Riese
given-names: Felix M.
orcid: https://orcid.org/0000-0003-0596-9585
title: "SuSi: SUpervised Self-organIzing maps in Python"
version: 1.2.2
doi: "10.5281/zenodo.2609130"
date-released: 2021-12-11
repository-code: https://github.com/felixriese/susi
license: BSD-3-Clause
preferred-citation:
authors:
- family-names: Riese
given-names: Felix M.
- family-names: Hinz
given-names: Stefan
- family-names: Keller
given-names: Sina
title: "Supervised and Semi-Supervised Self-Organizing Maps for Regression and Classification Focusing on Hyperspectral Data"
type: article
year: 2020
doi: "10.3390/rs12010007"
journal: "Remote Sensing"
number: 7
volume: 12
issue: 1
url: https://www.mdpi.com/2072-4292/12/1/7
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