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A Python module for nonnegative matrix factorization

Project Description


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.


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 install --user

To install for all users on Unix/Linux:

sudo python install

For more detailed installation instructions, see the web page


Run alternating least squares nonnegative matrix factorization with projected gradients and Random Vcol initialization algorithm on medulloblastoma gene expression data:

>>> import nimfa
>>> V =
>>> lsnmf = nimfa.Lsnmf(V, seed='random_vcol', rank=50, max_iter=100)
>>> lsnmf_fit = lsnmf()
>>> print('Rss: %5.4f' %
Rss: 0.2668
>>> print('Evar: %5.4f' %
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' %
Sparseness, W: 0.7297, H: 0.8796


  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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