feat: add next generation reservoir computing

Project.toml

@@ -1,7 +1,7 @@
 name = "ReservoirComputing"
 uuid = "7c2d2b1e-3dd4-11ea-355a-8f6a8116e294"
 authors = ["Francesco Martinuzzi"]
-version = "0.12.3"
+version = "0.12.4"
 
 [deps]
 ArrayInterface = "4fba245c-0d91-5ea0-9b3e-6abc04ee57a9"

README.md

@@ -42,6 +42,7 @@ reservoir computing models. More specifically the software offers:
     + Deep echo state networks `DeepESN`
     + Echo state networks with delayed states `DelayESN`
     + 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

docs/pages.jl

@@ -3,6 +3,7 @@ pages = [
     "Tutorials" => Any[
         "Building a model from scratch" => "tutorials/scratch.md",
         "Chaos forecasting with an ESN" => "tutorials/lorenz_basic.md",
+        "Fitting a Next Generation Reservoir Computer" => "tutorials/ngrc.md",
         #"Using Different Training Methods" => "esn_tutorials/different_training.md",
         "Deep Echo State Networks" => "tutorials/deep_esn.md",
         #"Hybrid Echo State Networks" => "tutorials/hybrid.md",

docs/src/api/models.md

@@ -9,6 +9,16 @@
     HybridESN
 ```
 
+## Next generation reservoir computing
+
+```@docs
+    NGRC
+```
+
+```@docs
+    polynomial_monomials
+```
+
 ### Utilities
 
 ```@docs

docs/src/index.md

@@ -82,6 +82,7 @@ or `dev` the package.
     + Deep echo state networks [`DeepESN`](@ref)
     + Echo state networks with delayed states [`DelayESN`](@ref)
     + Hybrid echo state networks [`HybridESN`](@ref)
+    + Next generation reservoir computing [`NGRC`](@ref)
 - 15+ reservoir initializers and 5+ input layer initializers
 - 5+ reservoir states modification algorithms
 - Sparse matrix computation through

docs/src/refs.bib

@@ -355,3 +355,17 @@ @article{Fleddermann2025
   year = {2025},
   month = may 
 }
+
+@article{Gauthier2021,
+  title = {Next generation reservoir computing},
+  volume = {12},
+  ISSN = {2041-1723},
+  url = {http://dx.doi.org/10.1038/s41467-021-25801-2},
+  DOI = {10.1038/s41467-021-25801-2},
+  number = {1},
+  journal = {Nature Communications},
+  publisher = {Springer Science and Business Media LLC},
+  author = {Gauthier,  Daniel J. and Bollt,  Erik and Griffith,  Aaron and Barbosa,  Wendson A. S.},
+  year = {2021},
+  month = sep 
+}

docs/src/tutorials/ngrc.md

@@ -0,0 +1,171 @@
+# Next Generation Reservoir Computing
+
+This tutorial shows how to use next generation reservoir computing [NGRC](@ref)
+in ReservoirComputing.jl to model the chaotic Lorenz system.
+
+NGRC works differently compared to traditional reservoir computing. In NGRC
+the reservoir is replaced with:
+  - A delay embedding of the input
+  - A nonlinear feature map
+The model is finally trained through ridge regression, like a normal RC.
+
+
+In this tutorial we will :
+  - simulate the Lorenz system,
+  - build an NGRC model with delayed inputs and polynomial features, following the
+    [original paper](https://doi.org/10.1038/s41467-021-25801-2),
+  - train it on one-step increments,
+  - roll it out generatively and compare with the true trajectory.
+
+## 1. Setup and imports
+
+First we need to load the necessary packages. We are going to use the following:
+
+```@example ngrc
+using OrdinaryDiffEq
+using Random
+using ReservoirComputing
+using Plots
+using Statistics
+```
+
+## 2. Define Lorenz system and generate data
+
+We define the Lorenz system and integrate it to generate a long trajectory:
+
+```@example ngrc
+function lorenz!(du, u, p, t)
+    σ, ρ, β = p
+    du[1] = σ * (u[2] - u[1])
+    du[2] = u[1] * (ρ - u[3]) - u[2]
+    du[3] = u[1] * u[2] - β * u[3]
+end
+
+prob = ODEProblem(
+    lorenz!,
+    Float32[1.0, 0.0, 0.0],
+    (0.0, 200.0),
+    (10.0f0, 28.0f0, 8/3f0),
+)
+
+data = Array(solve(prob, ABM54(); dt = 0.025))  # size: (3, T)
+```
+
+We then split the time series into training and testing segments:
+
+```@example ngrc
+shift = 300
+train_len = 500
+predict_len = 900
+
+input_data = data[:, shift:(shift + train_len - 1)]
+target_data = data[:, (shift + 1):(shift + train_len)]
+test_data = data[:, (shift + train_len):(shift + train_len + predict_len - 1)]
+```
+
+## 3. Normalization
+
+It is good practice to normalize the data, especially for polynomial features:
+
+```@example ngrc
+in_mean = mean(input_data; dims = 2)
+in_std = std(input_data;  dims = 2)
+
+train_norm_x = (input_data  .- in_mean) ./ in_std
+train_norm_y = (target_data .- in_mean) ./ in_std
+test_norm_x  = (test_data   .- in_mean) ./ in_std
+
+# We train an increment (residual) model: Δy = y_{t+1} − y_t
+train_delta_y = train_norm_y .- train_norm_x
+```
+
+## 4. Build the NGRC model
+
+Now that we have the data we can start building the model.
+Following the approach of the paper we first define two feature functions:
+
+  - a constant feature
+  - a second order polynomial monomial 
+
+```@example ngrc
+const_feature = x -> Float32[1.0]
+poly_feature  = x -> polynomial_monomials(x; degrees = 1:2)
+```
+
+Finally, we can construct the NGRC model.
+
+We set the following:
+
+```@example ngrc
+in_dims = 3
+out_dims = 3
+num_delays = 1
+```
+
+With `in_dims=3` and `num_delays=1` the delayed input length is 6.
+Adding the polinomial of degrees 1 and 2 will put give us 21 more. Finally, the constant
+term adds 1 more feature. In total we have 28 features. 
+
+We can pass the number of features to `ro_dims` to initialize the [`LinearReadout`](@ref)
+with the correct dimensions. However, unless one is planning to fry run the model without training,
+the [`train`](@ref) function will take care to adjust the dimensions.
+
+Now we build the NGRC:
+
+```@example ngrc
+rng = MersenneTwister(0)
+
+ngrc = NGRC(in_dims, out_dims; num_delays = num_delays, stride = 1, features = (const_feature, poly_feature),
+    include_input = false,  # we already encode everything in the features
+    ro_dims = 28,
+    readout_activation = identity)
+
+ps, st = setup(rng, ngrc)
+```
+
+At this point, `ngrc` is a fully specified model with:
+  - a [`DelayLayer`](@ref) that builds a 6-dimensional delayed vector from the 3D input,
+  - a [`NonlinearFeaturesLayer`](@ref) that maps that vector to 28 polynomial features,
+  - a [`LinearReadout`](@ref) (28 => 3).
+
+## 5. Training the NGRC readout
+
+We now train the linear readout using ridge regression on the increment `train_delta_y`:
+
+```@example ngrc
+ps, st = train!(ngrc, train_norm_x, train_delta_y, ps, st;
+    train_method = StandardRidge(2.5e-6))
+```
+
+where [`StandardRidge`](@ref) is the ridge regression provided natively by ReservoirComputing.jl.
+
+## 6. Generative prediction
+
+We now perform generative prediction on the increments to obtain the predicted time series:
+
+```@example ngrc
+single_step = copy(test_norm_x[:, 1]) # normalized initial condition
+traj_norm = similar(test_norm_x, 3, predict_len)
+
+for step in 1:predict_len
+    global st
+    delta_step, st = ngrc(single_step, ps, st)
+    single_step .= single_step .+ delta_step # increment update in normalized space
+    traj_norm[:, step] .= single_step
+end
+```
+
+Finally, we unscale back to the original coordinates:
+
+```@example ngrc
+traj = traj_norm .* in_std .+ in_mean # size: (3, predict_len)
+```
+
+## 7. Visualization
+
+We can now compare the predicted trajectory with the true Lorenz data on the test segment:
+
+```@example ngrc
+plot(transpose(test_data)[:, 1], transpose(test_data)[:, 2], transpose(test_data)[:, 3]; label="actual");
+plot!(transpose(traj)[:, 1], transpose(traj)[:, 2], transpose(traj)[:, 3]; label="predicted")
+```

src/ReservoirComputing.jl

@@ -40,6 +40,7 @@ include("models/esn.jl")
 include("models/esn_deep.jl")
 include("models/esn_delay.jl")
 include("models/esn_hybrid.jl")
+include("models/ngrc.jl")
 #extensions
 include("extensions/reca.jl")
 
@@ -57,9 +58,10 @@ export block_diagonal, chaotic_init, cycle_jumps, delay_line, delayline_backward
        selfloop_forwardconnection, simple_cycle, true_doublecycle
 export add_jumps!, backward_connection!, delay_line!, reverse_simple_cycle!,
        scale_radius!, self_loop!, simple_cycle!
-export train, train!, predict, resetcarry!
+export train, train!, predict, resetcarry!, polynomial_monomials
 export ESN, DeepESN, DelayESN, HybridESN
-#reca
+export NGRC
+#ext
 export RECACell, RECA
 export RandomMapping, RandomMaps