Metadata-Version: 2.1
Name: susi
Version: 1.0.8
Summary: Python package for unsupervised, supervised and semi-supervised self-organizing maps (SOM)
Home-page: https://github.com/felixriese/susi
Author: Felix M. Riese
Author-email: github@felixriese.de
License: BSD-3-Clause
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        |
        
        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.
        The class structure is set up as follows:
        
        - SOMClustering: Unsupervised SOM for clustering
        
          - SOMEstimator: Base class for supervised and semi-supervised SOMs
        
            - SOMRegressor: Regression SOM
            - SOMClassifier: Classification SOM
        
        :License:
            `3-Clause BSD license <LICENSE>`_
        
        :Author:
            `Felix M. Riese <mailto:github@felixriese.de>`_
        
        :Citation:
            see `Citation`_ and in the `bibtex <https://github.com/felixriese/susi/blob/master/bibliography.bib>`_ file
        
        :Documentation:
            `Documentation <https://susi.readthedocs.io/en/latest/index.html>`_
        
        :Installation:
            `Installation guidelines <https://susi.readthedocs.io/en/latest/install.html>`_
        
        :Paper:
            `F. M. Riese, S. Keller and S. Hinz in Remote Sensing, 2020 <https://www.mdpi.com/2072-4292/12/1/7>`_
        
        
        Installation
        ------------
        
        .. code:: bash
        
            pip install susi
        
        More information can be found in the `installation guidelines <https://susi.readthedocs.io/en/latest/install.html>`_.
        
        Examples
        --------
        
        A collection of code examples can be found in `the documentation <https://susi.readthedocs.io/en/latest/examples.html>`_.
        Code examples as Jupyter Notebooks can be found here:
        
        * `examples/SOMClustering <https://github.com/felixriese/susi/blob/master/examples/SOMClustering.ipynb>`_
        * `examples/SOMRegressor <https://github.com/felixriese/susi/blob/master/examples/SOMRegressor.ipynb>`_
        * `examples/SOMRegressor_semisupervised <https://github.com/felixriese/susi/blob/master/examples/SOMRegressor_semisupervised.ipynb>`_
        * `examples/SOMClassifier <https://github.com/felixriese/susi/blob/master/examples/SOMClassifier.ipynb>`_
        * `examples/SOMClassifier_semisupervised <https://github.com/felixriese/susi/blob/master/examples/SOMClassifier_semisupervised.ipynb>`_
        
        Citation
        --------
        
        The bibtex file including both references is available `here <https://github.com/felixriese/susi/blob/master/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.
        
        .. 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",
        `10.5281/zenodo.2609130 <https://doi.org/10.5281/zenodo.2609130>`_, 2019.
        
        .. 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}}
            }
        
        
        Change Log
        ==========
        
        [1.0.8] - 2020-01-20
        ------------------------
        - [FIXED] Replaced scikit-learn `sklearn.utils.fixes.parallel_helper`, see #12.
        
        [1.0.7] - 2019-11-28
        ------------------------
        - [ADDED] Optional tqdm visualization of the SOM training
        - [ADDED] New `init_mode_supervised` called `random_minmax`.
        - [CHANGED] Official name of package changes from `SUSI` to `SuSi`.
        - [CHANGED] Docstrings for functions are now according to guidelines.
        - [FIXED] Semi-supervised classification handling, sample weights
        - [FIXED] Supervised classification SOM initalization of `n_iter_supervised`
        - [FIXED] Code refactored according to prospector
        - [FIXED] Resolved bug in `get_datapoints_from_node()` for unsupervised SOM.
        
        [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
        - [CHANGED] Requirements for the SuSi package
        - [REMOVED] Support for Python 3.4
        - [FIXED] Code looks better in documentation with sphinx.ext.napoleon
        
        [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
        ------------------------
        - [ADDED] Codecov, Codacy
        - [CHANGED] Moved decreasing_rate() out of SOM classes
        - [FIXED] Removed duplicate constructor for SOMRegressor, fixed fit() params
        
        [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
        
Platform: UNKNOWN
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.5
Classifier: Programming Language :: Python :: 3.6
Classifier: Programming Language :: Python :: 3.7
Classifier: Environment :: Console
Classifier: Intended Audience :: Developers
Classifier: Intended Audience :: Education
Classifier: Intended Audience :: Information Technology
Classifier: Intended Audience :: Science/Research
Classifier: License :: OSI Approved
Classifier: Natural Language :: English
Classifier: Operating System :: OS Independent
Classifier: Topic :: Scientific/Engineering
Description-Content-Type: text/x-rst
Provides-Extra: docs
