easyesn is a library for recurrent neural networks using echo state networks (ESNs, also called reservoir computing) with a high level easy to use API that is closely oriented towards sklearn. It aims to make the use of ESN as easy as possible, by providing algorithms for automatic gradient based hyperparameter tuning (of ridge regression penalty, spectral radius, leaking rate and feedback scaling), as well as transient time estimation. Furtheremore, it incorporates the ability to use spatio temporal ESNs for prediction and classification of geometrically extended input signals.

The easyesn library can either use the CPU or make use of the GPU thanks to cupy. At the moment the use of the CPU is recommended though!

This project is based on research results for the gradient based hyperparameter optimization and transient time estimation of Luca Thiede and the spatio temporal prediction and classification techniques of Roland Zimmermann.

Download & more information at GitHub