Package: reservoirnet 0.2.0

reservoirnet: Reservoir Computing and Echo State Networks

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.

Authors:Thomas Ferte [aut, cre, trl], Kalidou Ba [aut, trl], Nathan Trouvain [aut], Rodolphe Thiebaut [aut], Xavier Hinaut [aut], Boris Hejblum [aut, trl]

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reservoirnet.pdf |reservoirnet.html
reservoirnet/json (API)

# Install 'reservoirnet' in R:
install.packages('reservoirnet', repos = c('https://thomasferte.r-universe.dev', 'https://cloud.r-project.org'))
Datasets:
  • dfCovid - Datagouv covid-19 dataset

On CRAN:

Conda:

This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.

3.26 score 12 scripts 148 downloads 12 exports 98 dependencies

Last updated 2 years agofrom:70e064ea45. Checks:9 OK. Indexed: yes.

TargetResultLatest binary
Doc / VignettesOKMar 25 2025
R-4.5-winOKMar 25 2025
R-4.5-macOKMar 25 2025
R-4.5-linuxOKMar 25 2025
R-4.4-winOKMar 25 2025
R-4.4-macOKMar 25 2025
R-4.4-linuxOKMar 25 2025
R-4.3-winOKMar 25 2025
R-4.3-macOKMar 25 2025

Exports:%>>%createNodegenerate_datainstall_reservoirpylinkplot_2x2_perfplot_marginal_perfplot_perf_22predict_seqrandom_search_hyperparamreservoirR_fitrloguniform

Dependencies:abindbackportsbootbriobroomcallrcarcarDataclicolorspacecorrplotcowplotcpp11crayonDerivdescdiffobjdigestdoBydplyrevaluatefansifarverFormulafsgenericsggplot2ggpubrggrepelggsciggsignifgluegridExtragtableherehmsisobandjanitorjsonlitelabelinglatticelifecyclelme4lubridatemagrittrMASSMatrixMatrixModelsmgcvmicrobenchmarkminqamodelrmunsellnlmenloptrnnetnumDerivpbkrtestpillarpkgbuildpkgconfigpkgloadpngpolynompraiseprocessxpspurrrquantregR6rappdirsrbibutilsRColorBrewerRcppRcppEigenRcppTOMLRdpackreformulasreticulaterlangrprojrootrstatixscalessnakecaseSparseMstringistringrsurvivaltestthattibbletidyrtidyselecttimechangeutf8vctrsviridisLitewaldowithr

01 - The basics first, you should learn

Rendered frombasic_usage_01.Rmdusingknitr::rmarkdownon Mar 25 2025.

Last update: 2023-04-04
Started: 2023-04-04

02 - Hyperparameter tuning with random search

Rendered fromhyperparameter_tuning_02.Rmdusingknitr::rmarkdownon Mar 25 2025.

Last update: 2023-04-04
Started: 2023-04-04

Classification with Reservoir Computing

Rendered fromClassification_with_RC.Rmdusingknitr::rmarkdownon Mar 25 2025.

Last update: 2023-04-04
Started: 2023-04-04