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        "author": "Marinka Zitnik",
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        "classifiers": [
            "Intended Audience :: Developers",
            "Intended Audience :: Science/Research",
            "License :: OSI Approved",
            "Programming Language :: Python",
            "Programming Language :: Python :: 2",
            "Programming Language :: Python :: 3",
            "Topic :: Scientific/Engineering",
            "Topic :: Scientific/Engineering :: Artificial Intelligence",
            "Topic :: Scientific/Engineering :: Bio-Informatics",
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        "description": "Nimfa\n-----\n\n[![build: passing](https://img.shields.io/travis/marinkaz/nimfa.svg)](https://travis-ci.org/marinkaz/nimfa)\n[![build: passing](https://coveralls.io/repos/marinkaz/nimfa/badge.svg)](https://coveralls.io/github/marinkaz/nimfa?branch=master)\n[![GitHub release](https://img.shields.io/github/release/marinkaz/nimfa.svg)](https://GitHub.com/marinkaz/nimfa/releases/)\n[![BSD license](https://img.shields.io/badge/License-BSD-blue.svg)](https://opensource.org/licenses/BSD-3-Clause)\n\nNimfa is a Python module that implements many algorithms for nonnegative matrix factorization. Nimfa is distributed under the BSD license.\n\nThe project was started in 2011 by Marinka Zitnik as a Google Summer of Code project, and since\nthen many volunteers have contributed. See AUTHORS file for a complete list of contributors.\n\nIt is currently maintained by a team of volunteers.\n\nImportant links\n---------------\n\n- Official source code repo: https://github.com/marinkaz/nimfa\n- HTML documentation (stable release): http://ai.stanford.edu/~marinka/nimfa\n- Download releases: http://github.com/marinkaz/nimfa/releases\n- Issue tracker: http://github.com/marinkaz/nimfa/issues\n\nDependencies\n------------\n\nNimfa is tested to work under Python 2.7 and Python 3.4.\n\nThe required dependencies to build the software are NumPy >= 1.7.0,\nSciPy >= 0.12.0.\n\nFor running the examples Matplotlib >= 1.1.1 is required.\n\nInstall\n-------\n\nThis package uses setuptools, which is a common way of installing\npython modules. To install in your home directory, use:\n\n    python setup.py install --user\n\nTo install for all users on Unix/Linux:\n\n    sudo python setup.py install\n\nFor more detailed installation instructions,\nsee the web page http://ai.stanford.edu/~marinka/nimfa\n\nUse\n---\n\nRun alternating least squares nonnegative matrix factorization with projected gradients and Random Vcol initialization algorithm on medulloblastoma gene expression data::\n\n    >>> import nimfa\n    >>> V = nimfa.examples.medulloblastoma.read(normalize=True)\n    >>> lsnmf = nimfa.Lsnmf(V, seed='random_vcol', rank=50, max_iter=100)\n    >>> lsnmf_fit = lsnmf()\n    >>> print('Rss: %5.4f' % lsnmf_fit.fit.rss())\n    Rss: 0.2668\n    >>> print('Evar: %5.4f' % lsnmf_fit.fit.evar())\n    Evar: 0.9997\n    >>> print('K-L divergence: %5.4f' % lsnmf_fit.distance(metric='kl'))\n    K-L divergence: 38.8744\n    >>> print('Sparseness, W: %5.4f, H: %5.4f' % lsnmf_fit.fit.sparseness())\n    Sparseness, W: 0.7297, H: 0.8796\n\n\nCite\n----\n\n    @article{Zitnik2012,\n      title     = {Nimfa: A Python Library for Nonnegative Matrix Factorization},\n      author    = {Zitnik, Marinka and Zupan, Blaz},\n      journal   = {Journal of Machine Learning Research},\n      volume    = {13},\n      pages     = {849-853},\n      year      = {2012}\n    }\n\n\n",
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