moving stuff out of ReservoirComputing.jl file

src/ReservoirComputing.jl

@@ -13,83 +13,7 @@ using StatsBase: sample
 using WeightInitializers: DeviceAgnostic, PartialFunction, Utils
 @reexport using WeightInitializers
 
-#define global types
 abstract type AbstractReservoirComputer end
-abstract type AbstractOutputLayer end
-abstract type AbstractPrediction end
-#should probably move some of these
-abstract type AbstractGRUVariant end
-
-#general output layer struct
-struct OutputLayer{T, I, S, L} <: AbstractOutputLayer
-    training_method::T
-    output_matrix::I
-    out_size::S
-    last_value::L
-end
-
-#prediction types
-"""
-    Generative(prediction_len)
-
-A prediction strategy that enables models to generate autonomous multi-step
-forecasts by recursively feeding their own outputs back as inputs for
-subsequent prediction steps.
-
-# Parameters
-
-  - `prediction_len::Int`: The number of future steps to predict.
-
-# Description
-
-The `Generative` prediction method allows a model to perform multi-step
-forecasting by using its own previous predictions as inputs for future predictions.
-This approach is especially useful in time series analysis, where each prediction
-depends on the preceding data points.
-
-At each step, the model takes the current input, generates a prediction,
-and then incorporates that prediction into the input for the next step.
-This recursive process continues until the specified
-number of prediction steps (`prediction_len`) is reached.
-"""
-struct Generative{T} <: AbstractPrediction
-    prediction_len::T
-end
-
-struct Predictive{I, T} <: AbstractPrediction
-    prediction_data::I
-    prediction_len::T
-end
-
-"""
-    Predictive(prediction_data)
-
-A prediction strategy for supervised learning tasks,
-where a model predicts labels based on a provided set
-of input features (`prediction_data`).
-
-# Parameters
-
-  - `prediction_data`: The input data used for prediction, typically structured as a matrix
-    where each column represents a sample, and each row represents a feature.
-
-# Description
-
-The `Predictive` prediction method is a standard approach
-in supervised machine learning tasks. It uses the provided input data
-(`prediction_data`) to produce corresponding labels or outputs based
-on the learned relationships in the model. Unlike generative prediction,
-this method does not recursively feed predictions into the model;
-instead, it operates on fixed input data to produce a single batch of predictions.
-
-This method is suitable for tasks like classification,
-regression, or other use cases where the input features
-and the number of steps are predefined.
-"""
-function Predictive(prediction_data)
-    prediction_len = size(prediction_data, 2)
-    Predictive(prediction_data, prediction_len)
-end
 
 #fallbacks for initializers #eventually to remove once migrated to WeightInitializers.jl
 for initializer in (:rand_sparse, :delay_line, :delay_line_backward, :cycle_jumps,
@@ -155,9 +79,7 @@ export scaled_rand, weighted_init, informed_init, minimal_init
 export rand_sparse, delay_line, delay_line_backward, cycle_jumps, simple_cycle, pseudo_svd
 export RNN, MRNN, GRU, GRUParams, FullyGated, Minimal
 export train
-export ESN
-export HybridESN, KnowledgeModel
-export DeepESN
+export ESN, HybridESN, KnowledgeModel, DeepESN
 export RECA, sample
 export RandomMapping, RandomMaps
 export Generative, Predictive, OutputLayer

src/esn/esn_input_layers.jl

@@ -94,7 +94,7 @@ end
     informed_init([rng::AbstractRNG=Utils.default_rng()], [T=Float32], dims...;
         scaling=0.1, model_in_size, gamma=0.5)
 
-Create an input layer for informed echo state networks.
+Create an input layer for informed echo state networks [^Pathak2018].
 
 # Arguments
 
@@ -109,6 +109,10 @@ Create an input layer for informed echo state networks.
   - `gamma`: The gamma value. Default is 0.5.
 
 # Examples
+
+[^Pathak2018]: Pathak, Jaideep, et al. "Hybrid forecasting of chaotic processes:
+    Using machine learning in conjunction with a knowledge-based model."
+    Chaos: An Interdisciplinary Journal of Nonlinear Science 28.4 (2018).
 """
 function informed_init(rng::AbstractRNG, ::Type{T}, dims::Integer...;
         scaling=T(0.1), model_in_size, gamma=T(0.5)) where {T <: Number}

src/esn/esn_reservoir_drivers.jl

@@ -222,6 +222,7 @@ function allocate_tmp(::MRNN, tmp_type, res_size)
     return [Adapt.adapt(tmp_type, zeros(res_size, 1)) for i in 1:2]
 end
 
+abstract type AbstractGRUVariant end
 #GRU-based driver
 struct GRU{F, L, R, V, B} #not an abstractreservoirdriver
     activation_function::F

src/predict.jl

@@ -1,3 +1,78 @@
+abstract type AbstractOutputLayer end
+abstract type AbstractPrediction end
+
+#general output layer struct
+struct OutputLayer{T, I, S, L} <: AbstractOutputLayer
+    training_method::T
+    output_matrix::I
+    out_size::S
+    last_value::L
+end
+
+#prediction types
+"""
+    Generative(prediction_len)
+
+A prediction strategy that enables models to generate autonomous multi-step
+forecasts by recursively feeding their own outputs back as inputs for
+subsequent prediction steps.
+
+# Parameters
+
+  - `prediction_len::Int`: The number of future steps to predict.
+
+# Description
+
+The `Generative` prediction method allows a model to perform multi-step
+forecasting by using its own previous predictions as inputs for future predictions.
+This approach is especially useful in time series analysis, where each prediction
+depends on the preceding data points.
+
+At each step, the model takes the current input, generates a prediction,
+and then incorporates that prediction into the input for the next step.
+This recursive process continues until the specified
+number of prediction steps (`prediction_len`) is reached.
+"""
+struct Generative{T} <: AbstractPrediction
+    prediction_len::T
+end
+
+struct Predictive{I, T} <: AbstractPrediction
+    prediction_data::I
+    prediction_len::T
+end
+
+"""
+    Predictive(prediction_data)
+
+A prediction strategy for supervised learning tasks,
+where a model predicts labels based on a provided set
+of input features (`prediction_data`).
+
+# Parameters
+
+  - `prediction_data`: The input data used for prediction, typically structured as a matrix
+    where each column represents a sample, and each row represents a feature.
+
+# Description
+
+The `Predictive` prediction method is a standard approach
+in supervised machine learning tasks. It uses the provided input data
+(`prediction_data`) to produce corresponding labels or outputs based
+on the learned relationships in the model. Unlike generative prediction,
+this method does not recursively feed predictions into the model;
+instead, it operates on fixed input data to produce a single batch of predictions.
+
+This method is suitable for tasks like classification,
+regression, or other use cases where the input features
+and the number of steps are predefined.
+"""
+function Predictive(prediction_data)
+    prediction_len = size(prediction_data, 2)
+    Predictive(prediction_data, prediction_len)
+end
+
+
 function obtain_prediction(rc::AbstractReservoirComputer,
         prediction::Generative,
         x,