Move initializers to WeightInitializers.jl

Project.toml

@@ -7,12 +7,11 @@ version = "0.10.2"
 Adapt = "79e6a3ab-5dfb-504d-930d-738a2a938a0e"
 CellularAutomata = "878138dc-5b27-11ea-1a71-cb95d38d6b29"
 Distances = "b4f34e82-e78d-54a5-968a-f98e89d6e8f7"
-Distributions = "31c24e10-a181-5473-b8eb-7969acd0382f"
 LinearAlgebra = "37e2e46d-f89d-539d-b4ee-838fcccc9c8e"
 NNlib = "872c559c-99b0-510c-b3b7-b6c96a88d5cd"
 Optim = "429524aa-4258-5aef-a3af-852621145aeb"
-PartialFunctions = "570af359-4316-4cb7-8c74-252c00c2016b"
 Random = "9a3f8284-a2c9-5f02-9a11-845980a1fd5c"
+Reexport = "189a3867-3050-52da-a836-e630ba90ab69"
 Statistics = "10745b16-79ce-11e8-11f9-7d13ad32a3b2"
 WeightInitializers = "d49dbf32-c5c2-4618-8acc-27bb2598ef2d"
 
@@ -21,23 +20,22 @@ LIBSVM = "b1bec4e5-fd48-53fe-b0cb-9723c09d164b"
 MLJLinearModels = "6ee0df7b-362f-4a72-a706-9e79364fb692"
 
 [extensions]
-RCMLJLinearModelsExt = "MLJLinearModels"
 RCLIBSVMExt = "LIBSVM"
+RCMLJLinearModelsExt = "MLJLinearModels"
 
 [compat]
 Adapt = "3.3.3, 4"
 Aqua = "0.8"
 CellularAutomata = "0.0.2"
 DifferentialEquations = "7"
 Distances = "0.10"
-Distributions = "0.25.36"
 LIBSVM = "0.8"
 LinearAlgebra = "1.10"
 MLJLinearModels = "0.9.2"
 NNlib = "0.8.4, 0.9"
 Optim = "1"
-PartialFunctions = "1.2"
 Random = "1.10"
+Reexport = "1.2.2"
 SafeTestsets = "0.1"
 Statistics = "1.10"
 Test = "1"

src/ReservoirComputing.jl

@@ -3,20 +3,17 @@ module ReservoirComputing
 using Adapt
 using CellularAutomata
 using Distances
-using Distributions
 using LinearAlgebra
 using NNlib
 using Optim
-using PartialFunctions
 using Random
+using Reexport
 using Statistics
-using WeightInitializers
+@reexport using WeightInitializers
 
 export NLADefault, NLAT1, NLAT2, NLAT3
 export StandardStates, ExtendedStates, PaddedStates, PaddedExtendedStates
 export StandardRidge
-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 ESN, train
 export HybridESN, KnowledgeModel
@@ -66,32 +63,6 @@ function Predictive(prediction_data)
     Predictive(prediction_data, prediction_len)
 end
 
-#fallbacks for initializers
-for initializer in (:rand_sparse, :delay_line, :delay_line_backward, :cycle_jumps,
-    :simple_cycle, :pseudo_svd,
-    :scaled_rand, :weighted_init, :informed_init, :minimal_init)
-    NType = ifelse(initializer === :rand_sparse, Real, Number)
-    @eval function ($initializer)(dims::Integer...; kwargs...)
-        return $initializer(WeightInitializers._default_rng(), Float32, dims...; kwargs...)
-    end
-    @eval function ($initializer)(rng::AbstractRNG, dims::Integer...; kwargs...)
-        return $initializer(rng, Float32, dims...; kwargs...)
-    end
-    @eval function ($initializer)(::Type{T},
-            dims::Integer...; kwargs...) where {T <: $NType}
-        return $initializer(WeightInitializers._default_rng(), T, dims...; kwargs...)
-    end
-    @eval function ($initializer)(rng::AbstractRNG; kwargs...)
-        return WeightInitializers.__partial_apply($initializer, (rng, (; kwargs...)))
-    end
-    @eval function ($initializer)(rng::AbstractRNG,
-            ::Type{T}; kwargs...) where {T <: $NType}
-        return WeightInitializers.__partial_apply($initializer, ((rng, T), (; kwargs...)))
-    end
-    @eval ($initializer)(; kwargs...) = WeightInitializers.__partial_apply(
-        $initializer, (; kwargs...))
-end
-
 #general
 include("states.jl")
 include("predict.jl")
@@ -100,8 +71,6 @@ include("predict.jl")
 include("train/linear_regression.jl")
 
 #esn
-include("esn/esn_input_layers.jl")
-include("esn/esn_reservoirs.jl")
 include("esn/esn_reservoir_drivers.jl")
 include("esn/esn.jl")
 include("esn/deepesn.jl")