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.
- 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
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.
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
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
@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}
}