Metadata-Version: 2.1
Name: sklearn-lvq
Version: 1.1.1
Summary: Scikit-Learn compatible Generalized Learning Vector Quantization (GLVQ) and Robust Soft Learning Vector Quantization implementation.
Home-page: https://github.com/MrNuggelz/sklearn-lvq
Author: Joris Jensun
Author-email: jjensen@techfak.uni-bielefeld.de
License: BSD-3-Clause
Download-URL: https://github.com/MrNuggelz/sklearn-lvq/releases/tag/1.1.0
Description: [![Build Status](https://travis-ci.org/MrNuggelz/sklearn-lvq.svg?branch=stable)](https://travis-ci.org/MrNuggelz/sklearn-lvq)
        [![Build status](https://ci.appveyor.com/api/projects/status/qiwkue1x5lgll382?svg=true)](https://ci.appveyor.com/project/MrNuggelz/sklearn-glvq)
        [![CircleCI](https://circleci.com/gh/MrNuggelz/sklearn-lvq.svg?style=shield)](https://circleci.com/gh/MrNuggelz/sklearn-lvq)
        [![Coverage Status](https://coveralls.io/repos/github/MrNuggelz/sklearn-lvq/badge.svg)](https://coveralls.io/github/MrNuggelz/sklearn-lvq)
        [![Coverage Status](https://readthedocs.org/projects/sklearn-lvq/badge/?version=latest)](https://sklearn-lvq.readthedocs.io/en/latest/?badge=latest)
        # Warning
        
        Repository and Package Name changed to sklearn-lvq!
        
        # Generalized Learning Vector Quantization
        Scikit-learn compatible implementation of GLVQ, GRLVQ, GMLVQ, LGMLVQ
        RSLVQ, MRSLVQ and LMRSLVQ.
        
        Compatible with Python2.7, Python3.6 and above.
        
        This implementation is based on the Matlab implementation
        provided by Biehl, Schneider and Bunte (http://matlabserver.cs.rug.nl/gmlvqweb/web/).
        
        ## Important Links
        - scikit-learn (http://scikit-learn.org/)
        - documentation (https://sklearn-lvq.readthedocs.io/en/latest/?badge=latest)
        
        ## Installation
        To install this module run:
        ```
        pip install .
        ```
        or
        ```
        pip install sklearn-lvq
        ```
        
        To also install the extras, use
        ```bash
        pip install .[docs,examples,tests]
        ```
        or
        ```bash
        pip install -U sklearn-lvq[docs,examples,tests]
        ```
        
        ## Examples
        To run the examples:
        ```
        pip install -U sklearn-lvq[examples]
        ```
        The examples can be found in the examples directory.
        
        ## Testing
        To run testss:
        ```
        pip install -U sklearn-lvq[tests]
        ```
        Tests are located in the `sklearn_lvq/tests` folder
        and can be run with the `nosetests` command in the main directory.
        
        ## Documentation
        To build the documentation locally, ensure that you have sphinx, sphinx-gallery,
        pillow, sphinx_rt_theme, metric_learn and matplotlib by executing:
        
        ```
        pip install -U sklearn-lvq[docs]
        ```
        
Platform: UNKNOWN
Classifier: Intended Audience :: Developers
Classifier: Intended Audience :: Education
Classifier: Intended Audience :: Science/Research
Classifier: License :: OSI Approved :: BSD License
Classifier: Programming Language :: Python :: 2.7
Classifier: Programming Language :: Python :: 3.5
Classifier: Programming Language :: Python :: 3.6
Classifier: Programming Language :: Python :: 3.7
Classifier: Programming Language :: Python :: 3.8
Classifier: Operating System :: OS Independent
Classifier: Topic :: Scientific/Engineering :: Artificial Intelligence
Classifier: Topic :: Software Development :: Libraries
Classifier: Topic :: Software Development :: Libraries :: Python Modules
Description-Content-Type: text/markdown
Provides-Extra: examples
Provides-Extra: tests
Provides-Extra: docs
