ReservoirComputing

Reservoir computing utilities for scientific machine learning (SciML)

https://github.com/sciml/reservoircomputing.jl

Science Score: 54.0%

This score indicates how likely this project is to be science-related based on various indicators:

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CITATION.cff file
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Academic publication links
Links to: arxiv.org
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Scientific vocabulary similarity
Low similarity (17.8%) to scientific vocabulary

Keywords

differential-equations echo-state-networks julia machine-learning reservoir-computing rnn scientific-machine-learning sciml

Keywords from Contributors

matrix-exponential pdes julialang ode dynamical-systems numerical sde stochastic-processes bayesian-inference neural-sde

Last synced: 6 months ago · JSON representation ·

Repository

Reservoir computing utilities for scientific machine learning (SciML)

Basic Info

Host: GitHub
Owner: SciML
License: mit
Language: Julia
Default Branch: master
Homepage: https://docs.sciml.ai/ReservoirComputing/stable/
Size: 19.3 MB

Statistics

Stars: 215
Watchers: 10
Forks: 41
Open Issues: 36
Releases: 35

Topics

differential-equations echo-state-networks julia machine-learning reservoir-computing rnn scientific-machine-learning sciml

Created about 6 years ago · Last pushed 6 months ago

Metadata Files

Readme License Citation

[![Join the chat at https://julialang.zulipchat.com #sciml-bridged](https://img.shields.io/static/v1?label=Zulip&message=chat&color=9558b2&labelColor=389826)](https://julialang.zulipchat.com/#narrow/stream/279055-sciml-bridged) [![Global Docs](https://img.shields.io/badge/docs-SciML-blue.svg)](https://docs.sciml.ai/ReservoirComputing/stable/) [![arXiv](https://img.shields.io/badge/arXiv-2204.05117-00b300.svg)](https://arxiv.org/abs/2204.05117) [![codecov](https://codecov.io/gh/SciML/ReservoirComputing.jl/branch/master/graph/badge.svg)](https://codecov.io/gh/SciML/ReservoirComputing.jl) [![Build Status](https://github.com/SciML/ReservoirComputing.jl/workflows/CI/badge.svg)](https://github.com/SciML/ReservoirComputing.jl/actions?query=workflow%3ACI) [![Build status](https://badge.buildkite.com/db8f91b89a10ad79bbd1d9fdb1340e6f6602a1c0ed9496d4d0.svg)](https://buildkite.com/julialang/reservoircomputing-dot-jl) [![ColPrac: Contributor's Guide on Collaborative Practices for Community Packages](https://img.shields.io/badge/ColPrac-Contributor%27s%20Guide-blueviolet)](https://github.com/SciML/ColPrac) [![SciML Code Style](https://img.shields.io/static/v1?label=code%20style&message=SciML&color=9558b2&labelColor=389826)](https://github.com/SciML/SciMLStyle) [![Aqua QA](https://raw.githubusercontent.com/JuliaTesting/Aqua.jl/master/badge.svg)](https://github.com/JuliaTesting/Aqua.jl) [![Julia](https://img.shields.io/badge/julia-v1.10+-blue.svg)](https://julialang.org/)

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.

Citing

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

bibtex @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} }

Installation

ReservoirComputing.jl can be installed using either of

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

julia 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. As a first step we gather the data. For the Generative prediction we need the target data to be one step ahead of the training data:

```julia using ReservoirComputing, OrdinaryDiffEq, Random Random.seed!(42) rng = MersenneTwister(17)

lorenz system parameters

u0 = [1.0, 0.0, 0.0] tspan = (0.0, 200.0) p = [10.0, 28.0, 8 / 3]

define lorenz system

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

solve and take data

prob = ODEProblem(lorenz, u0, tspan, p) data = Array(solve(prob, ABM54(); dt=0.02))

shift = 300 trainlen = 5000 predictlen = 1250

one step ahead for generative prediction

inputdata = data[:, shift:(shift + trainlen - 1)] targetdata = data[:, (shift + 1):(shift + trainlen)]

test = data[:, (shift + trainlen):(shift + trainlen + predict_len - 1)] ```

Now that we have the data we can initialize the ESN with the chosen parameters. Given that this is a quick example we are going to change the least amount of possible parameters:

julia input_size = 3 res_size = 300 esn = ESN(input_data, input_size, res_size; reservoir=rand_sparse(; radius=1.2, sparsity=6 / res_size), input_layer=weighted_init, nla_type=NLAT2(), rng=rng)

The echo state network can now be trained and tested. If not specified, the training will always be ordinary least squares regression:

julia output_layer = train(esn, target_data) output = esn(Generative(predict_len), output_layer)

The data is returned as a matrix, output in the code above, that contains the predicted trajectories. The results can now be easily plotted:

julia using Plots plot(transpose(output); layout=(3, 1), label="predicted") plot!(transpose(test); layout=(3, 1), label="actual")

lorenz_basic

One can also visualize the phase space of the attractor and the comparison with the actual one:

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

lorenz_attractor

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 the current progress on the library.

Owner

Name: SciML Open Source Scientific Machine Learning
Login: SciML
Kind: organization
Email: contact@chrisrackauckas.com

Website: https://sciml.ai
Twitter: SciML_Org
Repositories: 170
Profile: https://github.com/SciML

Open source software for scientific machine learning

Citation (CITATION.bib)

@article{JMLR:v23:22-0611,
  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}
}

GitHub Events

Total

Create event: 63
Commit comment event: 29
Issues event: 32
Release event: 12
Watch event: 12
Delete event: 45
Issue comment event: 94
Push event: 213
Pull request review event: 1
Pull request review comment event: 1
Pull request event: 91
Fork event: 3

Last Year

Create event: 63
Commit comment event: 29
Issues event: 32
Release event: 12
Watch event: 12
Delete event: 45
Issue comment event: 94
Push event: 213
Pull request review event: 1
Pull request review comment event: 1
Pull request event: 91
Fork event: 3

Committers

Last synced: 8 months ago

All Time

Total Commits: 558
Total Committers: 21
Avg Commits per committer: 26.571
Development Distribution Score (DDS): 0.265

Past Year

Commits: 136
Committers: 4
Avg Commits per committer: 34.0
Development Distribution Score (DDS): 0.022

Top Committers

Name	Email	Commits
MartinuzziFrancesco	m**o@g**m	410
Christopher Rackauckas	a**s@c**m	28
Anas	a**m@g**m	25
github-actions[bot]	4****]	20
CompatHelper Julia	c**y@j**g	19
Arno Strouwen	a**n@t**e	13
Jay-sanjay	1****y	9
Anant Thazhemadam	a**m@g**m	8
dependabot[bot]	4****]	7
jamesjscully	j**y@g**m	4
Kanav Gupta	3****9	3
Chris de Graaf	me@c****v	2
Krishna Bhogaonker	c**q@g**m	2
AnasAbdelR	7****R	1
Christos	m**i@g**m	1
David Widmann	d****n	1
Emanuele Natale	n****a	1
Páll Haraldsson	P**n@g**m	1
Viral B. Shah	V****h	1
maja.k.gwozdz@gmail.com	m**z@g**m	1
Hendrik Ranocha	m**l@r**e	1

Committer Domains (Top 20 + Academic)

ranocha.de: 1 cdg.dev: 1 telenet.be: 1 julialang.org: 1 chrisrackauckas.com: 1

Issues and Pull Requests

Last synced: 6 months ago

All Time

Total issues: 60
Total pull requests: 234
Average time to close issues: 5 months
Average time to close pull requests: 23 days
Total issue authors: 16
Total pull request authors: 17
Average comments per issue: 4.38
Average comments per pull request: 0.49
Merged pull requests: 175
Bot issues: 0
Bot pull requests: 77

Past Year

Issues: 20
Pull requests: 103
Average time to close issues: 6 days
Average time to close pull requests: 1 day
Issue authors: 2
Pull request authors: 5
Average comments per issue: 0.7
Average comments per pull request: 0.24
Merged pull requests: 79
Bot issues: 0
Bot pull requests: 12

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Top Authors

Issue Authors

MartinuzziFrancesco (39)
sdwfrost (4)
AnasAbdelR (2)
gbaraldi (2)
ngiann (2)
timothyslau (1)
AnHeuermann (1)
rylanperumal (1)
Datseris (1)
njk7062 (1)
spin0r (1)
abrari-itk (1)
AraujoH (1)
jClugstor (1)
ArnoStrouwen (1)

Pull Request Authors

MartinuzziFrancesco (112)
github-actions[bot] (64)
ArnoStrouwen (13)
dependabot[bot] (13)
ChrisRackauckas (8)
thazhemadam (7)
Jay-sanjay (4)
AnasAbdelR (3)
00krishna (2)
jamesjscully (1)
natema (1)
ranocha (1)
christopher-dG (1)
PallHaraldsson (1)
Chronum94 (1)

Top Labels

Issue Labels

good first issue (15) inits (10) enhancement (9) bug (8) documentation (4) new model (4) states (3) Model Variation (2) v0.10 (2) question (2) breaking (1)

Pull Request Labels

dependencies (13) github_actions (4)

Packages

Total packages: 3
Total downloads:
- julia 19 total

Total dependent packages: 0
(may contain duplicates)
Total dependent repositories: 0
(may contain duplicates)
Total versions: 110

proxy.golang.org: github.com/SciML/ReservoirComputing.jl

Documentation: https://pkg.go.dev/github.com/SciML/ReservoirComputing.jl#section-documentation
License: mit
Latest release: v0.11.4
published 7 months ago

Versions: 35
Dependent Packages: 0
Dependent Repositories: 0

Rankings

Dependent packages count: 7.0%

Average: 8.2%

Dependent repos count: 9.3%

Last synced: 6 months ago

proxy.golang.org: github.com/sciml/reservoircomputing.jl

Documentation: https://pkg.go.dev/github.com/sciml/reservoircomputing.jl#section-documentation
License: mit
Latest release: v0.11.4
published 7 months ago

Versions: 35
Dependent Packages: 0
Dependent Repositories: 0

Rankings

Dependent packages count: 7.0%

Average: 8.2%

Dependent repos count: 9.3%

Last synced: 6 months ago

juliahub.com: ReservoirComputing

Reservoir computing utilities for scientific machine learning (SciML)

Homepage: https://docs.sciml.ai/ReservoirComputing/stable/
Documentation: https://docs.juliahub.com/General/ReservoirComputing/stable/
License: MIT
Latest release: 0.11.4
published 7 months ago

Versions: 40
Dependent Packages: 0
Dependent Repositories: 0
Downloads: 19 Total

Rankings

Stargazers count: 4.4%

Forks count: 4.9%

Dependent repos count: 9.9%

Average: 14.5%

Dependent packages count: 38.9%

Last synced: 6 months ago

Dependencies

.github/workflows/CI.yml actions

actions/cache v1 composite
actions/checkout v2 composite
codecov/codecov-action v1 composite
julia-actions/julia-buildpkg v1 composite
julia-actions/julia-processcoverage v1 composite
julia-actions/julia-runtest v1 composite
julia-actions/setup-julia v1 composite

.github/workflows/FormatCheck.yml actions

actions/checkout v1 composite
julia-actions/setup-julia latest composite

.github/workflows/Invalidations.yml actions

actions/checkout v3 composite
julia-actions/julia-buildpkg v1 composite
julia-actions/julia-invalidations v1 composite
julia-actions/setup-julia v1 composite

.github/workflows/TagBot.yml actions

JuliaRegistries/TagBot v1 composite

.github/workflows/CompatHelper.yml actions

.github/workflows/Downgrade.yml actions

actions/checkout v4 composite
cjdoris/julia-downgrade-compat-action v1 composite
julia-actions/julia-buildpkg v1 composite
julia-actions/julia-runtest v1 composite
julia-actions/setup-julia v1 composite

ReservoirComputing

Science Score: 54.0%

Keywords

Keywords from Contributors

Repository

Basic Info

Statistics

Topics

Metadata Files

README.md

ReservoirComputing.jl

Citing

Installation

Quick Example

lorenz system parameters

define lorenz system

solve and take data

one step ahead for generative prediction

Acknowledgements

Owner

Citation (CITATION.bib)

GitHub Events

Total

Last Year

Committers

All Time

Past Year

Top Committers

Committer Domains (Top 20 + Academic)

Issues and Pull Requests

All Time

Past Year

Top Authors

Issue Authors

Pull Request Authors

Top Labels

Issue Labels

Pull Request Labels

Packages

proxy.golang.org: github.com/SciML/ReservoirComputing.jl

Rankings

proxy.golang.org: github.com/sciml/reservoircomputing.jl

Rankings

juliahub.com: ReservoirComputing

Rankings

Dependencies