Nimfa
-----
[](https://travis-ci.org/marinkaz/nimfa)
[](https://coveralls.io/github/marinkaz/nimfa?branch=master)
[](https://GitHub.com/marinkaz/nimfa/releases/)
[](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}
}
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"description": "Nimfa\n-----\n\n[](https://travis-ci.org/marinkaz/nimfa)\n[](https://coveralls.io/github/marinkaz/nimfa?branch=master)\n[](https://GitHub.com/marinkaz/nimfa/releases/)\n[](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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