Connectionists: Modular toolkit for Data Processing 2.4 released!

[Tiziano Zito]

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

MDP is a Python 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.
  Old MDP scripts can be turned into their parallelized equivalent with
  one simple command.

- 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
  VanderPlas 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

--

 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