MDP-3.3 released!

2012-10-04T13:15:11Z

We are glad to announce release 3.3 of the Modular toolkit for Data
Processing (MDP). This a bug-fix release, all current users are
invited to upgrade.

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 3.3?
--------------------------
- support sklearn versions up to 0.12
- cleanly support reload
- fail gracefully if pp server does not start
- several bug-fixes and improvements

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: Philip DeBoer, Yaroslav Halchenko.

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

MDP-3.2 released!

2011-10-24T13:40:13Z

We are glad to announce release 3.2 of the Modular toolkit for Data
Processing (MDP).

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.

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 Released!

2011-03-30T17:19:06Z

We are glad to announce release 3.1 of the Modular toolkit for Data
Processing (MDP).

What's new in version 3.1?
--------------------------

This is a bug fix release.

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

MDP-3.0 Released!

2011-01-17T15:32:38Z

We are glad to announce release 3.0 of the Modular toolkit for Data
Processing (MDP).

What's new in version 3.0?
--------------------------

- Python 3 support
- New extensions: caching and gradient
- Automatically generated wrappers for scikits.learn algorithms
- Shogun and libsvm wrappers
- New algorithms: convolution, several classifiers and several
user-contributed nodes
- Several new examples on the homepage
- Improved and expanded tutorial
- Several improvements and bug fixes
- New license: MDP goes BSD!

Acknowledgments
---------------
We thank the contributors to this release: Sven Dähne, Alberto Escalante,
Valentin Haenel, Yaroslav Halchenko, Sebastian Höfer, Michael Hull,
Samuel John, José Quesada, Ariel Rokem, Benjamin Schrauwen, David
Verstraeten, Katharina Maria Zeiner.

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

MDP 2.5 released!

2009-06-30T13:28:57Z

We are glad to announce release 2.5 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, JADE, and XSFA), Slow Feature Analysis, Restricted Boltzmann
Machine, and Locally Linear Embedding.

What's new in version 2.5?
--------------------------------------

- New nodes for XSFA, Linear Regression, Histogram, Cutoffs

- The parallel package has grown more features

- Tons of bug fixes

MDP 2.4 released!

2008-10-17T23:45:22Z

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

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

MDP 2.2 released

2008-03-21T19:19:44Z

This new MDP release features enhanced PCA nodes (SVD and
iterative algorithms), a brand new FastICA matching the latest
available official version, a JADE node for ICA, and
Restricted Boltzmann Machine nodes. A new subpackage "hinet"
allows arbitrary feed-forward network architectures. As usual
a bunch of bug-fixes.

MDP 2.1 released

2007-03-23T18:43:12Z

This new MDP release implements some new nodes, a renewed
symeig package, an updated tutorial, and is finally compatible
with numpy 1.0. Have a look at the list of changes:
http://mdp-toolkit.sourceforge.net/CHANGES

MDP2.0RC released

2006-06-30T10:20:45Z

MDP 2.0 introduces some important structural changes.

It is now possible to implement nodes with multiple training phases and even nodes with an undetermined number of phases. This allows for example the implementation of algorithms that need to collect some statistics on the whole input before proceeding with the actual training, or others that need to iterate over a training phase until a convergence criterion is satisfied. The ability to train each phase using chunks of input data is maintained if the chunks are generated with iterators.

Moreover, it is now possible to define nodes that require supervised training in a very straightforward way by passing additional arguments (e.g., labels or a target output) to the 'train' method.

Furthermore, new algorithms have been added, expanding the base of readily available basic data processing elements. Currently implemented algorithms include Principal Component Analysis, two flavors of Independent Component Analysis, Slow Feature Analysis, Gaussian Classifiers, Growing Neural Gas, Fisher Discriminant Analysis, and Factor Analysis.

As its user base is steadily increasing, MDP appears as a good candidate for becoming a common repository of user-supplied, freely available, Python implemented data processing algorithms.

MDP 1.1.0 released

2004-11-16T09:46:21Z

MDP-1.1.0 has been released! Check this page:
http://mdp-toolkit.sourceforge.net/CHANGES
for an overview of the changes since MDP-1.0.0 .

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