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'))

Peer review:

Datasets:
  • dfCovid - Datagouv covid-19 dataset

On CRAN:

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

3.18 score 8 scripts 140 downloads 12 exports 96 dependencies

Last updated 2 years agofrom:70e064ea45. Checks:OK: 7. Indexed: yes.

TargetResultDate
Doc / VignettesOKOct 26 2024
R-4.5-winOKOct 26 2024
R-4.5-linuxOKOct 26 2024
R-4.4-winOKOct 26 2024
R-4.4-macOKOct 26 2024
R-4.3-winOKOct 26 2024
R-4.3-macOKOct 26 2024

Exports:%>>%createNodegenerate_datainstall_reservoirpylinkplot_2x2_perfplot_marginal_perfplot_perf_22predict_seqrandom_search_hyperparamreservoirR_fitrloguniform

Dependencies:abindbackportsbootbriobroomcallrcarcarDataclicolorspacecorrplotcowplotcpp11crayonDerivdescdiffobjdigestdoBydplyrevaluatefansifarverFormulafsgenericsggplot2ggpubrggrepelggsciggsignifgluegridExtragtableherehmsisobandjanitorjsonlitelabelinglatticelifecyclelme4lubridatemagrittrMASSMatrixMatrixModelsmgcvmicrobenchmarkminqamodelrmunsellnlmenloptrnnetnumDerivpbkrtestpillarpkgbuildpkgconfigpkgloadpngpolynompraiseprocessxpspurrrquantregR6rappdirsRColorBrewerRcppRcppEigenRcppTOMLrematch2reticulaterlangrprojrootrstatixscalessnakecaseSparseMstringistringrsurvivaltestthattibbletidyrtidyselecttimechangeutf8vctrsviridisLitewaldowithr

01 - The basics first, you should learn

Rendered frombasic_usage_01.Rmdusingknitr::rmarkdownon Oct 26 2024.

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

02 - Hyperparameter tuning with random search

Rendered fromhyperparameter_tuning_02.Rmdusingknitr::rmarkdownon Oct 26 2024.

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

Classification with Reservoir Computing

Rendered fromClassification_with_RC.Rmdusingknitr::rmarkdownon Oct 26 2024.

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