We are glad to announce release 2.4 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, 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. Old MDP scripts can be turned into their parallelized equivalent with one simple command. - 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 VanderPlas 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 -- http://mail.python.org/mailman/listinfo/python-announce-list Support the Python Software Foundation: http://www.python.org/psf/donations.html