install_reservoirpy

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.

Thomas Ferte

reservoirnet

Reservoir Computing and Echo State Networks

Kalidou Ba

Nathan Trouvain

Rodolphe Thiebaut

Xavier Hinaut

Boris Hejblum

install_reservoirpy function

<dl><dt>envname</dt>
<dd><code>str</code> name of environment. Default is R-reticulate</dd>
<dt>method</dt>
<dd><code>str</code> type of environment type <code>(virtualenv, conda)</code>. 
Default is auto <code>(virtualenv is not available on Windows)</code></dd></dl>

Arguments

Install reservoirpy — install_reservoirpy

<dl>

<dt>envname</dt>
<dd><code>str</code> name of environment. Default is R-reticulate</dd>


<dt>method</dt>
<dd><code>str</code> type of environment type <code>(virtualenv, conda)</code>. 
Default is auto <code>(virtualenv is not available on Windows)</code></dd>

</dl>

Install reservoirpy

install_reservoirpy: Install reservoirpy

Description

Usage

Value

Arguments

Examples