Project nimfa - A Python Library for Nonnegative Matrix Factorization Techniques documentation and further details are available at nimfa site.
Please refer to that site.
Nimfa is a Python scripting library which includes a number of published matrix factorization algorithms, initialization methods, quality and performance measures and facilitates the combination of these to produce new strategies. The library represents a unified and efficient interface to matrix factorization algorithms and methods.
The nimfa library works with numpy dense matrices and scipy sparse matrices (where this is possible to save on space). The library has support for multiple runs of the algorithms which can be used for some quality measures. By setting runtime specific options tracking the residuals error within one (or more) run or tracking fitted factorization model is possible. Extensive documentation with working examples which demonstrate real applications, commonly used benchmark data and visualization methods are provided to help with the interpretation and comprehension of the results.
Marinka Zitnik, Blaz Zupan. Nimfa: A Python Library for Nonnegative Matrix Factorization, Journal of Machine Learning Research, 13, 849--853, 2012.
nimfa - A Python Library for Nonnegative Matrix Factorization Techniques Copyright (C) 2011-2012 Marinka Zitnik and Blaz Zupan
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