A Python module for nonnegative matrix factorization
Project description
Nimfa
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 the AUTHORS.rst 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://nimfa.biolab.si
Download releases: http://github.com/marinkaz/nimfa/releases
Issue tracker: http://github.com/marinkaz/nimfa/issues
Dependencies
Nimfa is tested to work under Python 2.6, 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://nimfa.biolab.si
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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