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README.txt 2011-10-24 1.6 kB
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MDP-tutorial.pdf 2011-10-24 892.1 kB
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MDP-3.2.tar.gz 2011-10-24 469.0 kB
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MDP-3.2.zip 2011-10-24 530.1 kB
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MDP-3.2.Python2.exe 2011-10-24 553.5 kB
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Totals: 5 Items   2.4 MB 0 
We are glad to announce release 3.2 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 signal processing methods (Principal Component Analysis,
Independent Component Analysis, Slow Feature Analysis),
manifold learning methods ([Hessian] Locally Linear Embedding),
several classifiers, probabilistic methods (Factor Analysis, RBM),
data pre-processing methods, and many others.

What's new in version 3.2?
--------------------------

- improved sklearn wrappers
- update sklearn, shogun, and pp wrappers to new versions
- do not leave temporary files around after testing
- refactoring and cleaning up of HTML exporting features
- improve export of signature and doc-string to public methods
- fixed and updated FastICANode to closely resemble the original
  Matlab version (thanks to Ben Willmore)
- support for new numpy version
- new NeuralGasNode (thanks to Michael Schmuker)
- several bug fixes and improvements

We recommend all users to upgrade.

Resources
---------
Download: http://sourceforge.net/projects/mdp-toolkit/files
Homepage: http://mdp-toolkit.sourceforge.net
Mailing list: http://lists.sourceforge.net/mailman/listinfo/mdp-toolkit-users

Acknowledgments
---------------
We thank the contributors to this release: Michael Schmuker, Ben Willmore.


The MDP developers,
Pietro Berkes
Zbigniew Jędrzejewski-Szmek
Rike-Benjamin Schuppner
Niko Wilbert
Tiziano Zito
Source: README.txt, updated 2011-10-24