Merge pull request #299 from SciML/fm/rccaext

chore: split reca models in ext

Author: MartinuzziFrancesco

Project.toml

@@ -1,11 +1,10 @@
 name = "ReservoirComputing"
 uuid = "7c2d2b1e-3dd4-11ea-355a-8f6a8116e294"
 authors = ["Francesco Martinuzzi"]
-version = "0.11.3"
+version = "0.11.4"
 
 [deps]
 Adapt = "79e6a3ab-5dfb-504d-930d-738a2a938a0e"
-CellularAutomata = "878138dc-5b27-11ea-1a71-cb95d38d6b29"
 Compat = "34da2185-b29b-5c13-b0c7-acf172513d20"
 LinearAlgebra = "37e2e46d-f89d-539d-b4ee-838fcccc9c8e"
 NNlib = "872c559c-99b0-510c-b3b7-b6c96a88d5cd"
@@ -14,19 +13,21 @@ Reexport = "189a3867-3050-52da-a836-e630ba90ab69"
 WeightInitializers = "d49dbf32-c5c2-4618-8acc-27bb2598ef2d"
 
 [weakdeps]
+CellularAutomata = "878138dc-5b27-11ea-1a71-cb95d38d6b29"
 LIBSVM = "b1bec4e5-fd48-53fe-b0cb-9723c09d164b"
 MLJLinearModels = "6ee0df7b-362f-4a72-a706-9e79364fb692"
 SparseArrays = "2f01184e-e22b-5df5-ae63-d93ebab69eaf"
 
 [extensions]
+RCCellularAutomataExt = "CellularAutomata"
 RCLIBSVMExt = "LIBSVM"
 RCMLJLinearModelsExt = "MLJLinearModels"
 RCSparseArraysExt = "SparseArrays"
 
 [compat]
 Adapt = "4.1.1"
 Aqua = "0.8"
-CellularAutomata = "0.0.2"
+CellularAutomata = "0.0.6"
 Compat = "4.16.0"
 DifferentialEquations = "7.16.1"
 LIBSVM = "0.8"
@@ -53,4 +54,4 @@ Statistics = "10745b16-79ce-11e8-11f9-7d13ad32a3b2"
 Test = "8dfed614-e22c-5e08-85e1-65c5234f0b40"
 
 [targets]
-test = ["Aqua", "Test", "SafeTestsets", "DifferentialEquations", "MLJLinearModels", "LIBSVM", "Statistics", "SparseArrays"]
+test = ["Aqua", "Test", "SafeTestsets", "DifferentialEquations", "MLJLinearModels", "LIBSVM", "Statistics", "SparseArrays", "CellularAutomata"]

docs/Project.toml

@@ -12,12 +12,12 @@ ReservoirComputing = "7c2d2b1e-3dd4-11ea-355a-8f6a8116e294"
 StatsBase = "2913bbd2-ae8a-5f71-8c99-4fb6c76f3a91"
 
 [compat]
-CellularAutomata = "0.0.2"
+CellularAutomata = "0.0.6"
 DifferentialEquations = "7.16.1"
 Documenter = "1"
 DocumenterCitations = "1"
 OrdinaryDiffEq = "6"
 Plots = "1"
 PredefinedDynamicalSystems = "1"
-ReservoirComputing = "0.11.0"
+ReservoirComputing = "0.11.4"
 StatsBase = "0.34.4"

docs/src/general/different_training.md

@@ -29,4 +29,4 @@ To change the regularization coefficient in the ridge example, using for example
 
 ## Support Vector Regression
 
-Contrary to the `LinearModel`s, no wrappers are needed for support vector regression. By using [LIBSVM.jl](https://github.com/JuliaML/LIBSVM.jl), LIBSVM wrappers in Julia, it is possible to call both `epsilonSVR()` or `nuSVR()` directly in `train()`. For the full range of kernels provided and the parameters to call, we refer the user to the official [documentation](https://www.csie.ntu.edu.tw/%7Ecjlin/libsvm/). Like before, if one intends to use LIBSVM regressors, it is necessary to specify `using LIBSVM`.
+Contrary to the `LinearModel`s, no wrappers are needed for support vector regression. By using [LIBSVM.jl](https://github.com/JuliaML/LIBSVM.jl), LIBSVM wrappers in Julia, it is possible to call both `epsilonSVR()` or `nuSVR()` directly in `train()`. For the full range of kernels provided and the parameters to call, we refer the user to the official documentation. Like before, if one intends to use LIBSVM regressors, it is necessary to specify `using LIBSVM`.

src/reca/reca_input_encodings.jl renamed to ext/RCCellularAutomataExt.jl

@@ -1,24 +1,49 @@
-abstract type AbstractInputEncoding end
-abstract type AbstractEncodingData end
+module RCCellularAutomataExt
+using ReservoirComputing: RECA, RandomMapping, RandomMaps
+import ReservoirComputing: train, next_state_prediction!, AbstractOutputLayer, NLADefault,
+                           StandardStates, obtain_prediction
+using CellularAutomata
+using Random: randperm
+
+function RECA(train_data,
+        automata;
+        generations = 8,
+        input_encoding = RandomMapping(),
+        nla_type = NLADefault(),
+        states_type = StandardStates())
+    in_size = size(train_data, 1)
+    #res_size = obtain_res_size(input_encoding, generations)
+    state_encoding = create_encoding(input_encoding, train_data, generations)
+    states = reca_create_states(state_encoding, automata, train_data)
+
+    return RECA(train_data, automata, state_encoding, nla_type, states, states_type)
+end
 
-struct RandomMapping{I, T} <: AbstractInputEncoding
-    permutations::I
-    expansion_size::T
+#training dispatch
+function train(reca::RECA, target_data, training_method = StandardRidge; kwargs...)
+    states_new = reca.states_type(reca.nla_type, reca.states, reca.train_data)
+    return train(training_method, Float32.(states_new), Float32.(target_data); kwargs...)
 end
 
-"""
-    RandomMapping(permutations, expansion_size)
-    RandomMapping(permutations; expansion_size=40)
-    RandomMapping(;permutations=8, expansion_size=40)
+#predict dispatch
+function (reca::RECA)(prediction,
+        output_layer::AbstractOutputLayer,
+        initial_conditions = output_layer.last_value,
+        last_state = zeros(reca.input_encoding.ca_size))
+    return obtain_prediction(reca, prediction, last_state, output_layer;
+        initial_conditions = initial_conditions)
+end
 
-Random mapping of the input data directly in the reservoir. The `expansion_size`
-determines the dimension of the single reservoir, and `permutations` determines the
-number of total reservoirs that will be connected, each with a different mapping.
-The detail of this implementation can be found in [1].
+function next_state_prediction!(reca::RECA, x, out, i, args...)
+    rm = reca.input_encoding
+    x = encoding(rm, out, x)
+    ca = CellularAutomaton(reca.automata, x, rm.generations + 1)
+    ca_states = ca.evolution[2:end, :]
+    x_new = reshape(transpose(ca_states), rm.states_size)
+    x = ca.evolution[end, :]
+    return x, x_new
+end
 
-[1] Nichele, Stefano, and Andreas Molund. “Deep reservoir computing using cellular
-automata.” arXiv preprint arXiv:1703.02806 (2017).
-"""
 function RandomMapping(; permutations = 8, expansion_size = 40)
     RandomMapping(permutations, expansion_size)
 end
@@ -27,15 +52,6 @@ function RandomMapping(permutations; expansion_size = 40)
     RandomMapping(permutations, expansion_size)
 end
 
-struct RandomMaps{T, E, G, M, S} <: AbstractEncodingData
-    permutations::T
-    expansion_size::E
-    generations::G
-    maps::M
-    states_size::S
-    ca_size::S
-end
-
 function create_encoding(rm::RandomMapping, input_data, generations)
     maps = init_maps(size(input_data, 1), rm.permutations, rm.expansion_size)
     states_size = generations * rm.expansion_size * rm.permutations
@@ -105,3 +121,5 @@ function mapping(input_size, mapped_vector_size)
     #sample(1:mapped_vector_size, input_size; replace=false)
     return randperm(mapped_vector_size)[1:input_size]
 end
+
+end #module

src/ReservoirComputing.jl

@@ -1,7 +1,6 @@
 module ReservoirComputing
 
 using Adapt: adapt
-using CellularAutomata: CellularAutomaton
 using Compat: @compat
 using LinearAlgebra: eigvals, mul!, I, qr, Diagonal
 using NNlib: fast_act, sigmoid
@@ -15,24 +14,19 @@ abstract type AbstractReservoirComputer end
 @compat(public, (create_states))
 
 #general
-include("states.jl")
-include("predict.jl")
-
-#general training
-include("train/linear_regression.jl")
-
+include("generics/states.jl")
+include("generics/predict.jl")
+include("generics/linear_regression.jl")
+#extensions
+include("extensions/reca.jl")
 #esn
-include("esn/inits_components.jl")
-include("esn/esn_inits.jl")
-include("esn/esn_reservoir_drivers.jl")
-include("esn/esn.jl")
-include("esn/deepesn.jl")
-include("esn/hybridesn.jl")
-include("esn/esn_predict.jl")
-
-#reca
-include("reca/reca.jl")
-include("reca/reca_input_encodings.jl")
+include("inits/inits_components.jl")
+include("inits/esn_inits.jl")
+include("layers/esn_reservoir_drivers.jl")
+include("models/esn.jl")
+include("models/deepesn.jl")
+include("models/hybridesn.jl")
+include("models/esn_predict.jl")
 
 export NLADefault, NLAT1, NLAT2, NLAT3, PartialSquare, ExtendedSquare
 export StandardStates, ExtendedStates, PaddedStates, PaddedExtendedStates
@@ -48,8 +42,9 @@ export add_jumps!, backward_connection!, delay_line!, reverse_simple_cycle!,
 export RNN, MRNN, GRU, GRUParams, FullyGated, Minimal
 export train
 export ESN, HybridESN, KnowledgeModel, DeepESN
+export Generative, Predictive, OutputLayer
+#reca
 export RECA
 export RandomMapping, RandomMaps
-export Generative, Predictive, OutputLayer
 
 end #module

src/extensions/reca.jl

@@ -0,0 +1,55 @@
+abstract type AbstractInputEncoding end
+abstract type AbstractEncodingData end
+
+"""
+    RandomMapping(permutations, expansion_size)
+    RandomMapping(permutations; expansion_size=40)
+    RandomMapping(;permutations=8, expansion_size=40)
+
+Random mapping of the input data directly in the reservoir. The `expansion_size`
+determines the dimension of the single reservoir, and `permutations` determines the
+number of total reservoirs that will be connected, each with a different mapping.
+The detail of this implementation can be found in [1].
+
+[1] Nichele, Stefano, and Andreas Molund. “Deep reservoir computing using cellular
+automata.” arXiv preprint arXiv:1703.02806 (2017).
+"""
+struct RandomMapping{I, T} <: AbstractInputEncoding
+    permutations::I
+    expansion_size::T
+end
+
+struct RandomMaps{T, E, G, M, S} <: AbstractEncodingData
+    permutations::T
+    expansion_size::E
+    generations::G
+    maps::M
+    states_size::S
+    ca_size::S
+end
+
+abstract type AbstractReca <: AbstractReservoirComputer end
+
+"""
+    RECA(train_data,
+        automata;
+        generations = 8,
+        input_encoding=RandomMapping(),
+        nla_type = NLADefault(),
+        states_type = StandardStates())
+
+[1] Yilmaz, Ozgur. “_Reservoir computing using cellular automata._”
+arXiv preprint arXiv:1410.0162 (2014).
+
+[2] Nichele, Stefano, and Andreas Molund. “_Deep reservoir computing using cellular
+automata._” arXiv preprint arXiv:1703.02806 (2017).
+"""
+struct RECA{S, R, E, T, Q} <: AbstractReca
+    #res_size::I
+    train_data::S
+    automata::R
+    input_encoding::E
+    nla_type::ReservoirComputing.NonLinearAlgorithm
+    states::T
+    states_type::Q
+end