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Scikit-Learn compatible Generalized Learning Vector Quantization (GLVQ) and Robust Soft Learning Vector Quantization implementation.

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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/).

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Installation

To install this module run:

pip install .

or

pip install sklearn-lvq

To also install the extras, use

pip install .[docs,examples,tests]

or

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]

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Source Distribution

sklearn-lvq-1.1.1.tar.gz (20.5 kB view hashes)

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Built Distribution

sklearn_lvq-1.1.1-py3-none-any.whl (38.1 kB view hashes)

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