Metadata-Version: 1.1
Name: gmr
Version: 1.2
Summary: Gaussian Mixture Regression
Home-page: https://github.com/AlexanderFabisch/gmr
Author: Alexander Fabisch
Author-email: afabisch@googlemail.com
License: new BSD
Description: ===
        gmr
        ===
        
        .. image:: https://api.travis-ci.org/AlexanderFabisch/gmr.png?branch=master
           :target: https://travis-ci.org/AlexanderFabisch/gmr
           :alt: Travis
        .. image:: https://landscape.io/github/AlexanderFabisch/gmr/master/landscape.svg?style=flat
           :target: https://landscape.io/github/AlexanderFabisch/gmr/master
           :alt: Code Health
        
        Gaussian Mixture Models (GMMs) for clustering and regression in Python.
        
        Source code repository: https://github.com/AlexanderFabisch/gmr
        
        .. image:: https://raw.githubusercontent.com/AlexanderFabisch/gmr/master/gmr.png
        
        Example
        -------
        
        Estimate GMM from samples and sample from GMM::
        
            from gmr import GMM
        
            gmm = GMM(n_components=3, random_state=random_state)
            gmm.from_samples(X)
            X_sampled = gmm.sample(100)
        
        
        For more details, see::
        
            help(gmr)
        
        How Does It Compare to scikit-learn?
        ------------------------------------
        
        There is an implementation of Gaussian Mixture Models for clustering in
        `scikit-learn <http://scikit-learn.org/stable/modules/generated/sklearn.mixture.GMM.html>`_
        as well. Regression could not be easily integrated in the interface of
        sklearn. That is the reason why I put the code in a separate repository.
        
        Installation
        ------------
        
        Install from `PyPI`_::
        
            sudo pip install gmr
        
        or from source::
        
            sudo python setup.py install
        
        .. _PyPi: https://pypi.python.org/pypi
        
Platform: UNKNOWN
Classifier: Intended Audience :: Science/Research
Classifier: Intended Audience :: Developers
Classifier: Programming Language :: Python
Classifier: Topic :: Software Development
Classifier: Topic :: Scientific/Engineering
Classifier: Operating System :: Microsoft :: Windows
Classifier: Operating System :: POSIX
Classifier: Operating System :: Unix
Classifier: Operating System :: MacOS
Classifier: Programming Language :: Python :: 2
Classifier: Programming Language :: Python :: 2.7
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.4
Classifier: Programming Language :: Python :: 3.5
Classifier: Programming Language :: Python :: 3.6
Classifier: Programming Language :: Python :: 3.7
Requires: numpy
Requires: scipy
