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 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. 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