Oger: Modular Learning Architectures For Large-Scale Sequential Processing
Oger (OrGanic Environment for Reservoir computing) is a Python toolbox for building, training and evaluating modular learning architectures on large datasets. It builds on MDP for its modularity, and adds processing of sequential datasets, several cross-validation schemes and parameter optimization methods. Additionally, several learning algorithms are implemented, such as different reservoir implementations (both sigmoid and spiking), ridge regression, Conditional Restricted Boltzmann Machine (CRBM) and others. Oger is released under the GNU LGPL, and is available from http://organic.elis.ugent.be/oger.