Metadata-Version: 1.0
Name: MDP
Version: 2.5
Summary: Modular toolkit for Data Processing (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. Implemented algorithms include Principal Component Analysis (PCA), Independent Component Analysis (ICA), Slow Feature Analysis (SFA), and many more.
Home-page: http://mdp-toolkit.sourceforge.net
Author: Pietro Berkes, Niko Wilbert, and Tiziano Zito
Author-email: berkes@brandeis.edu, mail@nikowilbert.de, tiziano.zito@bccn-berlin.de
License: http://www.gnu.org/licenses/lgpl.html
Download-URL: http://sourceforge.net/project/showfiles.php?group_id=116959
Description: 
        The Modular toolkit for Data Processing (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.
        
        From the user's perspective, MDP consists of a collection of
        supervised and unsupervised learning algorithms, and other data
        processing units (nodes) that can be combined into data processing
        sequences (flows) and more complex feed-forward network
        architectures. Given a set of input data, MDP takes care of
        successively training or executing all nodes in the network. This
        allows the user to specify complex algorithms as a series of simpler
        data processing steps in a natural way.
        
        The base of available algorithms is steadily increasing and 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, Gaussian
        Classifiers, Restricted Boltzmann Machine, and Locally Linear
        Embedding.
        
        Particular care has been taken to make computations efficient in terms
        of speed and memory.  To reduce memory requirements, it is possible to
        perform learning using batches of data, and to define the internal
        parameters of the nodes to be single precision, which makes the usage
        of very large data sets possible.  Moreover, the 'parallel' subpackage
        offers a parallel implementation of the basic nodes and flows.
        
        From the developer's perspective, MDP is a framework that makes the
        implementation of new supervised and unsupervised learning algorithms
        easy and straightforward.  The basic class, 'Node', takes care of
        tedious tasks like numerical type and dimensionality checking, leaving
        the developer free to concentrate on the implementation of the
        learning and execution phases. Because of the common interface, the
        node then automatically integrates with the rest of the library and
        can be used in a network together with other nodes. A node can have
        multiple training phases and even an undetermined number of phases.
        This allows the implementation of algorithms that need to collect some
        statistics on the whole input before proceeding with the actual
        training, and 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. Moreover, crash recovery is optionally available: in
        case of failure, the current state of the flow is saved for later
        inspection.
        
        MDP has been written in the context of theoretical 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
        make it also a valid educational tool.
        
        http://mdp-toolkit.sourceforge.net
        
Platform: Any
Classifier: Development Status :: 5 - Production/Stable
Classifier: Intended Audience :: Developers
Classifier: Intended Audience :: Education
Classifier: Intended Audience :: Science/Research
Classifier: License :: OSI Approved :: GNU Library or Lesser General Public License (LGPL)
Classifier: Operating System :: OS Independent
Classifier: Programming Language :: Python
Classifier: Topic :: Scientific/Engineering :: Information Analysis
Classifier: Topic :: Scientific/Engineering :: Mathematics
