[MMTK] [p.berkes@biologie.hu-berlin.de: ANN: MDP 1.1.0]

Eugen Leitl eugen at leitl.org
Tue Jun 14 16:16:22 CEST 2005

----- Forwarded message from p.berkes at biologie.hu-berlin.de -----

From: p.berkes at biologie.hu-berlin.de
Date: Mon, 13 Jun 2005 13:58:40 +0200 (CEST)
To: comp-neuro <comp-neuro at neuroinf.org>
Subject: ANN: MDP 1.1.0
Reply-To: p.berkes at biologie.hu-berlin.de

MDP 1.1.0

Modular toolkit for Data Processing (MDP) is a Python library to
perform data processing. Already implemented algorithms include:
Principal Component Analysis (PCA), Independent Component Analysis
(ICA), Slow Feature Analysis (SFA), and Growing Neural Gas (GNG).

MDP allows to combine different algorithms and other data processing
elements (nodes) into data processing sequences (flows). Moreover, it
provides a framework that makes the implementation of new algorithms
easy and intuitive.

MDP supports the most common numerical extensions to Python, currently
Numeric, Numarray, SciPy. When used together with SciPy and the symeig
package, MDP gives to the scientific programmer the full power of
well-known C and FORTRAN data processing libraries. MDP helps the
programmer to exploit Python object oriented design with C and FORTRAN

MDP has been written for research in neuroscience, but it has been
designed to be helpful in any context where trainable data processing
algorithms are used. Its simplicity on the user side together with the
reusability of the implemented nodes could make it also a valid
educational tool.


    * Python >= 2.3
    * one of the following Python numerical extensions:
      Numeric, Numarray, or SciPy.

    For optimal performance we recommend to use SciPy with LAPACK
    and ATLAS libraries, and to install the symeig module.

(sorry for multiple posting)


 Pietro Berkes
 Institute for Theoretical Biology
 Humboldt-Universitaet zu Berlin
 Invalidenstrasse, 43
 D-10115 Berlin, Germany


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