Metadata-Version: 2.1
Name: nimfa
Version: 1.4.0
Summary: A Python module for nonnegative matrix factorization
Home-page: http://nimfa.biolab.si
Author: Marinka Zitnik
Author-email: marinka@cs.stanford.edu
Maintainer: Marinka Zitnik
Maintainer-email: marinka@cs.stanford.edu
License: BSD
Download-URL: http://github.com/marinkaz/nimfa
Description: Nimfa
        -----
        
        [![build: passing](https://img.shields.io/travis/marinkaz/nimfa.svg)](https://travis-ci.org/marinkaz/nimfa)
        [![build: passing](https://coveralls.io/repos/marinkaz/nimfa/badge.svg)](https://coveralls.io/github/marinkaz/nimfa?branch=master)
        [![GitHub release](https://img.shields.io/github/release/marinkaz/nimfa.svg)](https://GitHub.com/marinkaz/nimfa/releases/)
        [![BSD license](https://img.shields.io/badge/License-BSD-blue.svg)](https://opensource.org/licenses/BSD-3-Clause)
        
        Nimfa is a Python module that implements many algorithms for nonnegative matrix factorization. Nimfa is distributed under the BSD license.
        
        The project was started in 2011 by Marinka Zitnik as a Google Summer of Code project, and since
        then many volunteers have contributed. See AUTHORS file for a complete list of contributors.
        
        It is currently maintained by a team of volunteers.
        
        Important links
        ---------------
        
        - Official source code repo: https://github.com/marinkaz/nimfa
        - HTML documentation (stable release): http://ai.stanford.edu/~marinka/nimfa
        - Download releases: http://github.com/marinkaz/nimfa/releases
        - Issue tracker: http://github.com/marinkaz/nimfa/issues
        
        Dependencies
        ------------
        
        Nimfa is tested to work under Python 2.7 and Python 3.4.
        
        The required dependencies to build the software are NumPy >= 1.7.0,
        SciPy >= 0.12.0.
        
        For running the examples Matplotlib >= 1.1.1 is required.
        
        Install
        -------
        
        This package uses setuptools, which is a common way of installing
        python modules. To install in your home directory, use:
        
            python setup.py install --user
        
        To install for all users on Unix/Linux:
            
            sudo python setup.py install
        
        For more detailed installation instructions,
        see the web page http://ai.stanford.edu/~marinka/nimfa
        
        Use
        ---
        
        Run alternating least squares nonnegative matrix factorization with projected gradients and Random Vcol initialization algorithm on medulloblastoma gene expression data::
        
            >>> import nimfa
            >>> V = nimfa.examples.medulloblastoma.read(normalize=True)
            >>> lsnmf = nimfa.Lsnmf(V, seed='random_vcol', rank=50, max_iter=100)
            >>> lsnmf_fit = lsnmf()
            >>> print('Rss: %5.4f' % lsnmf_fit.fit.rss())
            Rss: 0.2668
            >>> print('Evar: %5.4f' % lsnmf_fit.fit.evar())
            Evar: 0.9997
            >>> print('K-L divergence: %5.4f' % lsnmf_fit.distance(metric='kl'))
            K-L divergence: 38.8744
            >>> print('Sparseness, W: %5.4f, H: %5.4f' % lsnmf_fit.fit.sparseness())
            Sparseness, W: 0.7297, H: 0.8796
        
        
        Cite
        ----
        
            @article{Zitnik2012,
              title     = {Nimfa: A Python Library for Nonnegative Matrix Factorization},
              author    = {Zitnik, Marinka and Zupan, Blaz},
              journal   = {Journal of Machine Learning Research},
              volume    = {13},
              pages     = {849-853},
              year      = {2012}
            }
        
Keywords: matrix factorization,nonnegative matrix factorization,bioinformatics,data mining,machine learning
Platform: UNKNOWN
Classifier: Intended Audience :: Science/Research
Classifier: Intended Audience :: Developers
Classifier: License :: OSI Approved
Classifier: Programming Language :: Python
Classifier: Topic :: Software Development
Classifier: Topic :: Scientific/Engineering
Classifier: Topic :: Scientific/Engineering :: Artificial Intelligence
Classifier: Topic :: Scientific/Engineering :: Bio-Informatics
Classifier: Programming Language :: Python :: 2
Classifier: Programming Language :: Python :: 3
Description-Content-Type: text/markdown
