Modular toolkit for Data Processing MDP / News: Recent posts

We are glad to announce release 3.3 of the Modular toolkit for Data
Processing (MDP). This a bug-fix release, all current users are
invited to upgrade.

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.... read more

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.... read more

We are glad to announce release 3.1 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.... read more

We are glad to announce release 3.0 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.... read more

We are glad to announce release 2.5 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, JADE, and XSFA), Slow Feature Analysis, Restricted Boltzmann
Machine, and Locally Linear Embedding.... read more

We are glad to announce release 2.4 of the Modular toolkit for Data
Processing (MDP).

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.... read more

This new MDP release features enhanced PCA nodes (SVD and
iterative algorithms), a brand new FastICA matching the latest
available official version, a JADE node for ICA, and
Restricted Boltzmann Machine nodes. A new subpackage "hinet"
allows arbitrary feed-forward network architectures. As usual
a bunch of bug-fixes.

This new MDP release implements some new nodes, a renewed
symeig package, an updated tutorial, and is finally compatible
with numpy 1.0. Have a look at the list of changes:
http://mdp-toolkit.sourceforge.net/CHANGES

MDP 2.0 introduces some important structural changes.

It is now possible to implement nodes with multiple training phases and even nodes with an undetermined number of phases. This allows for example the implementation of algorithms that need to collect some statistics on the whole input before proceeding with the actual training, or others that need to iterate over a training phase until a convergence criterion is satisfied. The ability to train each phase using chunks of input data is maintained if the chunks are generated with iterators. ... read more

MDP-1.1.0 has been released! Check this page:
http://mdp-toolkit.sourceforge.net/CHANGES
for an overview of the changes since MDP-1.0.0 .