[SciPy-user] MDP-2.0 released

Tiziano Zito t.zito at biologie.hu-berlin.de
Fri Jun 30 07:51:38 CDT 2006


MDP version 2.0 has been released!

What is it?
-----------
Modular toolkit for Data Processing (MDP) is a data processing
framework written in Python.

 From the user's perspective, MDP consists of a collection of trainable
supervised and unsupervised algorithms that can be combined into data
processing flows. The base of readily available algorithms includes
Principal Component Analysis, two flavors of Independent Component
Analysis, Slow Feature Analysis, Gaussian Classifiers, Growing Neural
Gas, Fisher Discriminant Analysis, and Factor Analysis.

 From the developer's perspective, MDP is a framework to make the
implementation of new algorithms easier. MDP takes care of tedious
tasks like numerical type and dimensionality checking, leaving the
developer free to concentrate on the implementation of the training
and execution phases. The new elements then automatically integrate
with the rest of the library.

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

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

What's new in version 2.0?
--------------------------
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.

 Nodes that require supervised training can be defined in a very
straightforward way by passing additional arguments (e.g., labels or a
target output) to the 'train' method.
 
 New algorithms have been added, expanding the base of
readily available basic data processing elements. 

 MDP is now based exclusively on the NumPy Python numerical extension. 

--

 Tiziano Zito
 Institute for Theoretical Biology
 Humboldt-Universitaet zu Berlin  
 Invalidenstrasse, 43
 D-10115 Berlin, Germany

 Pietro Berkes
 Gatsby Computational Neuroscience Unit
 Alexandra House, 17 Queen Square
 London WC1N 3AR, United Kingdom



More information about the SciPy-user mailing list