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.
|Depends:||R (≥ 3.6)|
|Imports:||reticulate, testthat (≥ 3.0.0), rlang, ggplot2, ggpubr, janitor, dplyr, magrittr, methods|
|Suggests:||rmarkdown, knitr, covr, kableExtra, slider, tibble, tidyr|
|Author:||Thomas Ferte [aut, cre, trl], Kalidou Ba [aut, trl], Nathan Trouvain [aut], Rodolphe Thiebaut [aut], Xavier Hinaut [aut], Boris Hejblum [aut, trl]|
|Maintainer:||Thomas Ferte <thomas.ferte at u-bordeaux.fr>|
|License:||GPL (≥ 3)|
|SystemRequirements:||Python (>= 3.7)|
|CRAN checks:||reservoirnet results|
Classification with Reservoir Computing
01 - The basics first, you should learn
02 - Hyperparameter tuning with random search
|Windows binaries:||r-devel: reservoirnet_0.2.0.zip, r-release: reservoirnet_0.2.0.zip, r-oldrel: reservoirnet_0.2.0.zip|
|macOS binaries:||r-release (arm64): reservoirnet_0.2.0.tgz, r-oldrel (arm64): reservoirnet_0.2.0.tgz, r-release (x86_64): reservoirnet_0.2.0.tgz, r-oldrel (x86_64): reservoirnet_0.2.0.tgz|
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