MDP 2.4 released!

We are glad to announce release 2.4 of the Modular toolkit for Data
Processing (MDP).

MDP is a library of widely used data processing algorithms that can be
combined according to a pipeline analogy to build more complex data
processing software. The base of available algorithms includes, to
name but the most common, Principal Component Analysis (PCA and
NIPALS), several Independent Component Analysis algorithms (CuBICA,
FastICA, TDSEP, and JADE), Slow Feature Analysis, Restricted Boltzmann
Machine, and Locally Linear Embedding.

What's new in version 2.4?
--------------------------------------

- The new version introduces a new parallel package to execute the MDP
algorithms on multiple processors or machines. The package also offers
an interface to develop customized schedulers and parallel algorithms.

- The number of available algorithms is increased with the Locally
Linear Embedding and Hessian eigenmaps algorithms to perform
dimensionality reduction and manifold learning (many thanks to Jake
VandePlas for his contribution!)

- Some more bug fixes, useful features, and code migration towards Python 3.0

Resources
---------
Download: http://sourceforge.net/project/showfiles.php?group_id=116959
Homepage: http://mdp-toolkit.sourceforge.net
Mailing list: http://sourceforge.net/mail/?group_id=116959

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Pietro Berkes
Volen Center for Complex Systems
Brandeis University
Waltham, MA, USA

Niko Wilbert
Institute for Theoretical Biology
Humboldt-University
Berlin, Germany

Tiziano Zito
Bernstein Center for Computational Neuroscience
Humboldt-University
Berlin, Germany

Posted by Tiziano Zito 2008-10-17