About the toolbox
The OrGanic Environment for Reservoir computing (Oger) toolbox is a Python toolbox, released under the LGPL, for rapidly building, training and evaluating modular learning architectures on large datasets. It builds functionality on top of the Modular toolkit for Data Processing (MDP). Using MDP, Oger provides:
- Easily building, training and using modular structures of learning algorithms
- A wide variety of state-of-the-art machine learning methods, such as PCA, ICA, SFA, RBMs, ... You can find the full list here.
The Oger toolbox builds functionality on top of MDP, such as:
In addition, several additional MDP nodes are provided by Oger, such as a:
- Reservoir node
- Leaky reservoir node
- Ridge regression node
- Conditional Restricted Boltzmann Machine (CRBM) node
- Perceptron node
See here for instructions on downloading and installing the toolbox.
There is a general tutorial and examples highlighting some key functions of Oger here. A pdf version of the tutorial pages is here.
You can find an automatically generated API documentation here.
Bugs and feature requests
You can submit bugs or requests for additional features using the issue tracking system at github.ugent.be for this repository.