ReservoirComputing.jl

ReservoirComputing.jl provides an efficient, modular and easy to use implementation of Reservoir Computing models such as Echo State Networks (ESNs). For information on using this package please refer to the stable documentation. Use the in-development documentation to take a look at not yet released features.

Features

ReservoirComputing.jl provides layers, models, and functions to help build and train reservoir computing models. More specifically the software offers:

• Base layers for reservoir computing model construction. Main layers provide high level reservoir computing building blocks, such as ReservoirComputer and ReservoirChain. Additional, lower level layers provide the building blocks for custom reservoir computers, such as LinearReadout, Collect, ESNCell, DelayLayer, NonlinearFeaturesLayer, and more
• Fully built models:
  • Echo state networks ESN
  • Deep echo state networks DeepESN
  • Echo state networks with delayed states DelayESN
  • Edge of stability echo state networks ES2N
  • Hybrid echo state networks HybridESN
  • Next generation reservoir computing NGRC
• 15+ reservoir initializers and 5+ input layer initializers
• 5+ reservoir states modification algorithms
• Sparse matrix computation through SparseArrays.jl
• Multiple training algorithms via LIBSVM.jl and MLJLinearModels.jl

Installation

ReservoirComputing.jl can be installed using either of

julia> ] #actually press the closing square brackets
pkg> add ReservoirComputing

or

using Pkg
Pkg.add("ReservoirComputing")

Quick Example

To illustrate the workflow of this library we will showcase how it is possible to train an ESN to learn the dynamics of the Lorenz system. You can find the same example fully explained in the getting started page.

using OrdinaryDiffEq
using Plots
using Random
using ReservoirComputing

Random.seed!(42)
rng = MersenneTwister(17)

function lorenz(du, u, p, t)
    du[1] = p[1] * (u[2] - u[1])
    du[2] = u[1] * (p[2] - u[3]) - u[2]
    du[3] = u[1] * u[2] - p[3] * u[3]
end

prob = ODEProblem(lorenz, [1.0f0, 0.0f0, 0.0f0], (0.0, 200.0), [10.0f0, 28.0f0, 8/3])
data = Array(solve(prob, ABM54(); dt=0.02))
shift = 300
train_len = 5000
predict_len = 1250

input_data = data[:, shift:(shift + train_len - 1)]
target_data = data[:, (shift + 1):(shift + train_len)]
test = data[:, (shift + train_len):(shift + train_len + predict_len - 1)]

esn = ESN(3, 300, 3; init_reservoir=rand_sparse(; radius=1.2, sparsity=6/300),
    state_modifiers=NLAT2)

ps, st = setup(rng, esn)
ps, st = train!(esn, input_data, target_data, ps, st)
output, st = predict(esn, predict_len, ps, st; initialdata=test[:, 1])

plot(transpose(output)[:, 1], transpose(output)[:, 2], transpose(output)[:, 3];
    label="predicted")
plot!(transpose(test)[:, 1], transpose(test)[:, 2], transpose(test)[:, 3];
    label="actual")

Citing

If you use this library in your work, please cite:

@article{martinuzzi2022reservoircomputing,
  author  = {Francesco Martinuzzi and Chris Rackauckas and Anas Abdelrehim and Miguel D. Mahecha and Karin Mora},
  title   = {ReservoirComputing.jl: An Efficient and Modular Library for Reservoir Computing Models},
  journal = {Journal of Machine Learning Research},
  year    = {2022},
  volume  = {23},
  number  = {288},
  pages   = {1--8},
  url     = {http://jmlr.org/papers/v23/22-0611.html}
}

Acknowledgements

This project was possible thanks to initial funding through the Google summer of code 2020 program. Francesco M. further acknowledges ScaDS.AI and RSC4Earth for supporting further progress on the library. Current developments are possible thanks to research funding for Francesco M. provided by MPIPKS