Modular toolkit for data processing 2.1

Operating systemsOS : Windows / Linux / Mac OS / BSD / Solaris
Program licensingScript Licensing : LGPL - GNU Lesser General Public License
CreatedCreated : Jun 7, 2007
Size downloadDownloads : 2
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Modular toolkit for Data Processing (MDP) is a Python ...

Modular toolkit for data processing by Pietro Berkes (MDP) is a Python data processing framework. Implemented algorithms include: Principal Component Analysis (PCA), Independent Component analysis (ICA), Slow Feature Analysis (SFA), Growing Neural Gas (GNG), Factor Analysis, Fisher Discriminant Analysis (FDA), and gaussian_classifiers.
From the user's perspective, mdp consists of a collection of trainable supervised and unsupervised algorithms or other data processing units (nodes) that can be combined into data processing flows. Given a sequence of input data, MDP takes care of successively training or executing all nodes in the flow. This structure allows to specify complex algorithms as a sequence of simpler data_processing steps in a natural way. Training can be performed using small chunks of input data, so that the use of very large data sets becomes possible while reducing the memory requirements. Memory usage can also be minimized by defining the internals of the nodes to be single precision.
From the developer's perspective, MDP is a framework to make the implementation of new algorithms easier. 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 training and execution phases. The node then automatically integrates with the rest of the library and can be used in a flow together with other nodes. A node can have multiple training phases and even 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.
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.

Modular toolkit for data processing 2.1 scripting tags: analysis, principal component analysis, nodes, mdp, data, processing, data processing, gaussian, algorithms, training, gaussian classifiers. What is new in Modular toolkit for data processing 2.1 software script? - Unable to find Modular toolkit for data processing 2.1 news. What is improvements are expecting? Newly-made Modular toolkit for data processing 2.2 will be downloaded from here. You may download directly. Please write the reviews of the Modular toolkit for data processing. License limitations are unspecified.