From the user's perspective, MDP is a collection of supervised and unsupervised learning algorithms and other data processing units that can be combined into data processing sequences and more complex feed-forward network architectures.
News: all of the few remaining calls to scipy have been replaced with calls to numpy. Versions 0.1.8 and above do not require scipy as a dependency. Introduction This library provides Python functions for agglomerative clustering. Its features include * generating hierarchical clusters from distance matrices * computing distance matrices from observation vectors * computing statistics on clusters * cutting linkages to generate flat clusters * and visualizing clusters with dendrograms. The interface is very similar to MATLAB's Statistics Toolbox API to make code easier to port from MATLAB to Python/Numpy. The core implementation of this library is in C for efficiency.
ClusterViz is a software to visualize the clustering process using the family of k-means algorithms. The program is free software under the GNU General Public License (GPL). ClusterViz allows to cluster data while visualizing an up to three dimensional projection. The clustering process is visualized using OpenGL. As clustering algorithms the family of k-means algorithms is implemented, including mixture models.
OSCAR allows users, regardless of their experience level with a *nix environment, to install a Beowulf type high performance computing cluster. It also contains everything needed to administer and program this type of HPC cluster. OSCAR's flexible package management system has a rich set of pre-packaged applications and utilities which means you can get up and running without laboriously installing and configuring complex cluster administration and communication packages. It also lets administrators create customized packages for any kind of distributed application or utility, and to distribute those packages from an online package repository, either on or off site.