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Version\n\n.. image:: https://img.shields.io/pypi/pyversions/susi.svg\n    :target: https://pypi.org/project/susi/\n    :alt: PyPI - Python Version\n\n.. image:: https://readthedocs.org/projects/susi/badge/?version=latest\n    :target: https://susi.readthedocs.io/en/latest/?badge=latest\n    :alt: Documentation Status\n\n.. image:: https://codecov.io/gh/felixriese/susi/branch/master/graph/badge.svg\n    :target: https://codecov.io/gh/felixriese/susi\n    :alt: Codecov\n\n.. image:: https://api.codacy.com/project/badge/Grade/d304689a7364437db1ef998cf7765f5a\n\t:target: https://app.codacy.com/app/felixriese/susi\n\t:alt: Codacy Badge\n\n.. image:: https://anaconda.org/conda-forge/susi/badges/version.svg\n    :target: https://anaconda.org/conda-forge/susi\n    :alt: Conda-forge\n\n|\n\n.. image:: https://raw.githubusercontent.com/felixriese/susi/master/docs/_static/susi_logo_small.png\n    :target: https://github.com/felixriese/susi\n    :align: right\n    :alt: SuSi logo\n\nSuSi: Supervised Self-organizing maps in Python\n===============================================\n\nPython package for unsupervised, supervised and semi-supervised self-organizing maps (SOM)\n\nDescription\n-----------\n\nWe present the SuSi package for Python.\nIt includes a fully functional SOM for unsupervised, supervised and semi-supervised tasks:\n\n- SOMClustering: Unsupervised SOM for clustering\n- SOMRegressor: (Semi-)Supervised Regression SOM\n- SOMClassifier: (Semi-)Supervised Classification SOM\n\n:License:\n    `3-Clause BSD license \u003cLICENSE\u003e`_\n\n:Author:\n    `Felix M. Riese \u003cmailto:github@felixriese.de\u003e`_\n\n:Citation:\n    see `Citation`_ and in the `bibtex \u003chttps://github.com/felixriese/susi/blob/main/bibliography.bib\u003e`_ file\n\n:Documentation:\n    `Documentation \u003chttps://susi.readthedocs.io/en/latest/index.html\u003e`_\n\n:Installation:\n    `Installation guidelines \u003chttps://susi.readthedocs.io/en/latest/install.html\u003e`_\n\n:Paper:\n    `F. M. Riese, S. Keller and S. Hinz in Remote Sensing, 2020 \u003chttps://www.mdpi.com/2072-4292/12/1/7\u003e`_\n\n\nInstallation\n------------\n\nPip\n~~~\n\n.. code:: bash\n\n    pip3 install susi\n    \n.. image:: https://static.pepy.tech/personalized-badge/susi?period=total\u0026units=international_system\u0026left_color=black\u0026right_color=blue\u0026left_text=Downloads\n\t:target: https://pepy.tech/project/susi\n\t:alt: PyPi Downloads\n\nConda\n~~~~~\n\n.. code:: bash\n\n    conda install -c conda-forge susi\n\nMore information can be found in the `installation guidelines \u003chttps://susi.readthedocs.io/en/latest/install.html\u003e`_.\n\n.. image:: https://img.shields.io/conda/dn/conda-forge/susi.svg\n\t:target: https://anaconda.org/conda-forge/susi\n\t:alt: Conda-Forge Downloads\n\nExamples\n--------\n\nA collection of code examples can be found in `the documentation \u003chttps://susi.readthedocs.io/en/latest/examples.html\u003e`_.\nCode examples as Jupyter Notebooks can be found here:\n\n* `examples/SOMClustering \u003chttps://github.com/felixriese/susi/blob/main/examples/SOMClustering.ipynb\u003e`_\n* `examples/SOMRegressor \u003chttps://github.com/felixriese/susi/blob/main/examples/SOMRegressor.ipynb\u003e`_\n* `examples/SOMRegressor_semisupervised \u003chttps://github.com/felixriese/susi/blob/main/examples/SOMRegressor_semisupervised.ipynb\u003e`_\n* `examples/SOMRegressor_multioutput \u003chttps://github.com/felixriese/susi/blob/main/examples/SOMRegressor_multioutput.ipynb\u003e`_\n* `examples/SOMClassifier \u003chttps://github.com/felixriese/susi/blob/main/examples/SOMClassifier.ipynb\u003e`_\n* `examples/SOMClassifier_semisupervised \u003chttps://github.com/felixriese/susi/blob/main/examples/SOMClassifier_semisupervised.ipynb\u003e`_\n\nFAQs\n-----\n\n- **How should I set the initial hyperparameters of a SOM?** For more details\n  on the hyperparameters, see in `documentation/hyperparameters\n  \u003chttps://susi.readthedocs.io/en/latest/hyperparameters.html\u003e`_.\n- **How can I optimize the hyperparameters?** The SuSi hyperparameters\n  can be optimized, for example, with `scikit-learn.model_selection.GridSearchCV\n  \u003chttps://scikit-learn.org/stable/modules/generated/sklearn.model_selection.GridSearchCV.html\u003e`_,\n  since the SuSi package is developed according to several scikit-learn\n  guidelines.\n\n\n------------\n\n\nCitation\n--------\n\nThe bibtex file including both references is available in `bibliography.bib\n\u003chttps://github.com/felixriese/susi/blob/main/bibliography.bib\u003e`_.\n\n**Paper:**\n\nF. M. Riese, S. Keller and S. Hinz, \"Supervised and Semi-Supervised Self-Organizing\nMaps for Regression and Classification Focusing on Hyperspectral Data\",\n*Remote Sensing*, vol. 12, no. 1, 2020. `DOI:10.3390/rs12010007\n\u003chttps://doi.org/10.3390/rs12010007\u003e`_\n\n.. code:: bibtex\n\n    @article{riese2020supervised,\n        author = {Riese, Felix~M. and Keller, Sina and Hinz, Stefan},\n        title = {{Supervised and Semi-Supervised Self-Organizing Maps for\n                  Regression and Classification Focusing on Hyperspectral Data}},\n        journal = {Remote Sensing},\n        year = {2020},\n        volume = {12},\n        number = {1},\n        article-number = {7},\n        URL = {https://www.mdpi.com/2072-4292/12/1/7},\n        ISSN = {2072-4292},\n        DOI = {10.3390/rs12010007}\n    }\n\n**Code:**\n\nFelix M. Riese, \"SuSi: SUpervised Self-organIzing maps in Python\",\nZenodo, 2019. `DOI:10.5281/zenodo.2609130\n\u003chttps://doi.org/10.5281/zenodo.2609130\u003e`_\n\n.. image:: https://zenodo.org/badge/DOI/10.5281/zenodo.2609130.svg\n   :target: https://doi.org/10.5281/zenodo.2609130\n\n.. code:: bibtex\n\n    @misc{riese2019susicode,\n        author = {Riese, Felix~M.},\n        title = {{SuSi: Supervised Self-Organizing Maps in Python}},\n        year = {2019},\n        DOI = {10.5281/zenodo.2609130},\n        publisher = {Zenodo},\n        howpublished = {\\href{https://doi.org/10.5281/zenodo.2609130}{doi.org/10.5281/zenodo.2609130}}\n    }\n\n-------------\n\nLicense\n-------\n\nThis project is published under the `3-Clause BSD \u003cLICENSE\u003e`_ license.\n\n.. image:: https://img.shields.io/pypi/l/susi.svg\n    :target: https://github.com/felixriese/susi/blob/main/LICENSE\n    :alt: PyPI - License\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Ffelixriese%2Fsusi","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Ffelixriese%2Fsusi","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Ffelixriese%2Fsusi/lists"}