Connectionists: Modular toolkit for Data Processing 2.1 released!

[Tiziano Zito]

MDP version 2.1 and symeig 1.2 have been released!

What's new in version 2.1?
--------------------------

- Fully compatible with NumpPy 1.0, the first stable release
  of the descendant of the Numeric python extension module

- symeig project resumed and updated

- For increased speed, scipy and symeig are automatically used if
  available

- New nodes: Independent Slow Feature Analysis and quadratic forms
  analysis algorithms

- General improvements, several bug fixes, and code cleanups

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, Independent Slow Feature
Analysis, and many more.

 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 seamlessly integrate with
the rest of the library.

 MDP has been written in the context of theoretical research in
neuroscience, but it has been designed to be helpful in any context
where trainable data processing algorithms are used. Its simplicity
on the user side together with the reusability of the implemented
nodes make it also a valid educational tool.

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

 The optional symeig module contains a Python wrapper for the LAPACK
functions to solve the standard and generalized eigenvalue problems
for symmetric (hermitian) positive definite matrices. Those
specialized algorithms give an important speed-up with respect to the
generic LAPACK eigenvalue problem solver used by NumPy.

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

--

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

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