We are glad to announce release 2.4 of the Modular toolkit for Data
MDP is a 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.
- 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
VandePlas for his contribution!)
- Some more bug fixes, useful features, and code migration towards Python 3.0
Mailing list: http://sourceforge.net/mail/?group_id=116959
Volen Center for Complex Systems
Waltham, MA, USA
Institute for Theoretical Biology
Bernstein Center for Computational Neuroscience
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