EchoBay is a C++, Eigen powered library, for the training and deployment of Echo State Networks, with multiple layers and different topologies. EchoBay employs Limbo library to find the best set of hyper-parameters that maximize a score function. EchoBay is designed to work almost seamlessly on small computers, microcontrollers (tested on ESP32) and large multi-threaded systems.

• Limbo tested on v2.1.0
• yaml-cpp tested on v0.6.2
• spectra tested on v0.7.0

Optional dependencies to read CSV data fast-cpp-csv-parser.

Full documentation is available here.

After configuration and building (see docs for details) the basic usage is:

./echobay train configuration.yml outputFolderName

./echobay compute modelFolderName

• Luca Cerina
• Giuseppe Franco

Other important contributions in this research are from Claudio Gallicchio and Alessio Micheli (Department of Computer Science, University of Pisa) and Marco D. Santambrogio (Dipartimento di Informatica, Elettronica e Bioingegneria, Politecnico di Milano)

If you use EchoBay in a scientific paper, please cite:

Cerina, Luca, Giuseppe Franco, and Marco D. Santambrogio. (2019) Lightweight autonomous bayesian optimization of Echo-State Networks., European Symposium on Artificial Neural Networks, ESANN, 2019.