We are glad to announce release 2.6 of the Modular toolkit for Data
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 2.6?
- Several new classifier nodes have been added.
- A new node extension mechanism makes it possible to dynamically
add methods or attributes for specific features to node classes,
enabling aspect-oriented programming in MDP. Several MDP features
(like parallelization) are now based on this mechanism, and users
can add their own custom node extensions.
- BiMDP is a large new package in MDP that introduces bidirectional
data flows to MDP, including backpropagation and even loops. BiMDP
also enables the transportation of additional data in flows via
- BiMDP includes a new flow inspection tool, that runs as as a
graphical debugger in the webrowser to step through complex flows.
It can be extended by users for the analysis and visualization of
- As usual, tons of bug fixes
The new additions in the library have been thoroughly tested but, as
usual after a public release, we especially welcome user's feedback
and bug reports.
MDP Sprint 2010
Following our tradition of sprint-driven development, the team of the
core developers decided to organize a programming sprint open to
external participants. We invite in particular all users who
implemented new algorithms and would like to see them integrated in
MDP: you will work together with a core developer!
More info: http://sourceforge.net/apps/mediawiki/mdp-toolkit/index.php?title=MDP_Sprint_2010