We are glad to announce release 3.0 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 3.0?
- Python 3 support
- New extensions: caching and gradient
- Automatically generated wrappers for scikits.learn algorithms
- Shogun and libsvm wrappers
- New algorithms: convolution, several classifiers and several
- Several new examples on the homepage
- Improved and expanded tutorial
- Several improvements and bug fixes
- New license: MDP goes BSD!
We thank the contributors to this release: Sven Dähne, Alberto Escalante,
Valentin Haenel, Yaroslav Halchenko, Sebastian Höfer, Michael Hull,
Samuel John, José Quesada, Ariel Rokem, Benjamin Schrauwen, David
Verstraeten, Katharina Maria Zeiner.
The MDP developers,