Reservoir Computing and Echo State Networks
Takes two nodes and applies python operator >>
Function to create some node
Load data from the Japanese vowels or the Mackey-Glass
Install reservoirpy
Link two :py:class:~.Node instances to form a :py:class:~.Modelins...
plot.reservoir_predict_seq
plot_2x2_perf
plot_marginal_perf
plot_perf_22
Run the node-forward function on a sequence of data
reservoirR_fit print summary
random_search_hyperparam
Offline fitting method of a Node
rloguniform
summary.reservoir_predict_seq
reservoirR_fit summary
A simple user-friendly library based on the 'python' module 'reservoirpy'. It provides a flexible interface to implement efficient Reservoir Computing (RC) architectures with a particular focus on Echo State Networks (ESN). Some of its features are: offline and online training, parallel implementation, sparse matrix computation, fast spectral initialization, advanced learning rules (e.g. Intrinsic Plasticity) etc. It also makes possible to easily create complex architectures with multiple reservoirs (e.g. deep reservoirs), readouts, and complex feedback loops. Moreover, graphical tools are included to easily explore hyperparameters. Finally, it includes several tutorials exploring time series forecasting, classification and hyperparameter tuning. For more information about 'reservoirpy', please see Trouvain et al. (2020) <doi:10.1007/978-3-030-61616-8_40>. This package was developed in the framework of the University of Bordeaux’s IdEx "Investments for the Future" program / RRI PHDS.