We are glad to announce release 3.2 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 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.
Mailing list: http://lists.sourceforge.net/mailman/listinfo/mdp-toolkit-users
We thank the contributors to this release: Michael Schmuker, Ben Willmore.
The MDP developers,
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