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debian/3.2-1

Tag for MDP debian package to be uploaded to sid

MDP-3.2

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 wrapeprs to new versions
- do not leave temporary files around after testing
- refactoring and cleaning up of HTML exporting features
- improve export of signature and docstring 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 recomment 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

MDP-3.1

We are glad to announce release 3.1 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 3.1?
--------------------------

This is a bug fix release.

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: Sven Dähne, Fabian Pedregosa.

The MDP developers,
Pietro Berkes
Zbigniew Jędrzejewski-Szmek
Rike-Benjamin Schuppner
Niko Wilbert
Tiziano Zito

debian/2.5-1

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