fixing imports

Project.toml

@@ -6,13 +6,10 @@ version = "0.10.5"
 [deps]
 Adapt = "79e6a3ab-5dfb-504d-930d-738a2a938a0e"
 CellularAutomata = "878138dc-5b27-11ea-1a71-cb95d38d6b29"
-Distances = "b4f34e82-e78d-54a5-968a-f98e89d6e8f7"
 LinearAlgebra = "37e2e46d-f89d-539d-b4ee-838fcccc9c8e"
 NNlib = "872c559c-99b0-510c-b3b7-b6c96a88d5cd"
-PartialFunctions = "570af359-4316-4cb7-8c74-252c00c2016b"
 Random = "9a3f8284-a2c9-5f02-9a11-845980a1fd5c"
 Reexport = "189a3867-3050-52da-a836-e630ba90ab69"
-Statistics = "10745b16-79ce-11e8-11f9-7d13ad32a3b2"
 StatsBase = "2913bbd2-ae8a-5f71-8c99-4fb6c76f3a91"
 WeightInitializers = "d49dbf32-c5c2-4618-8acc-27bb2598ef2d"
 
@@ -29,12 +26,10 @@ Adapt = "4.1.1"
 Aqua = "0.8"
 CellularAutomata = "0.0.2"
 DifferentialEquations = "7.15.0"
-Distances = "0.10"
 LIBSVM = "0.8"
 LinearAlgebra = "1.10"
 MLJLinearModels = "0.9.2, 0.10"
 NNlib = "0.9.26"
-PartialFunctions = "1.2"
 Random = "1.10"
 Reexport = "1.2.2"
 SafeTestsets = "0.1"
@@ -51,7 +46,9 @@ LIBSVM = "b1bec4e5-fd48-53fe-b0cb-9723c09d164b"
 MLJLinearModels = "6ee0df7b-362f-4a72-a706-9e79364fb692"
 Random = "9a3f8284-a2c9-5f02-9a11-845980a1fd5c"
 SafeTestsets = "1bc83da4-3b8d-516f-aca4-4fe02f6d838f"
+Statistics = "10745b16-79ce-11e8-11f9-7d13ad32a3b2"
 Test = "8dfed614-e22c-5e08-85e1-65c5234f0b40"
 
 [targets]
-test = ["Aqua", "Test", "SafeTestsets", "Random", "DifferentialEquations", "MLJLinearModels", "LIBSVM"]
+test = ["Aqua", "Test", "SafeTestsets", "Random", "DifferentialEquations",
+    "MLJLinearModels", "LIBSVM", "Statistics"]

src/ReservoirComputing.jl

@@ -1,14 +1,11 @@
 module ReservoirComputing
 
-using Adapt
-using CellularAutomata
-using Distances
-using LinearAlgebra
-using NNlib
-using PartialFunctions
-using Random
+using Adapt: adapt
+using CellularAutomata: CellularAutomaton
+using LinearAlgebra: eigvals, mul!, I
+using NNlib: fast_act, sigmoid
+using Random: Random, AbstractRNG
 using Reexport: Reexport, @reexport
-using Statistics
 using StatsBase: sample
 using WeightInitializers: DeviceAgnostic, PartialFunction, Utils
 @reexport using WeightInitializers

src/esn/deepesn.jl

@@ -77,7 +77,7 @@ function DeepESN(train_data,
         matrix_type=typeof(train_data))
     if states_type isa AbstractPaddedStates
         in_size = size(train_data, 1) + 1
-        train_data = vcat(Adapt.adapt(matrix_type, ones(1, size(train_data, 2))),
+        train_data = vcat(adapt(matrix_type, ones(1, size(train_data, 2))),
             train_data)
     end
 

src/esn/esn.jl

@@ -66,7 +66,7 @@ function ESN(train_data,
         matrix_type=typeof(train_data))
     if states_type isa AbstractPaddedStates
         in_size = size(train_data, 1) + 1
-        train_data = vcat(Adapt.adapt(matrix_type, ones(1, size(train_data, 2))),
+        train_data = vcat(adapt(matrix_type, ones(1, size(train_data, 2))),
             train_data)
     end
 

src/esn/esn_predict.jl

@@ -93,16 +93,16 @@ end
 
 function allocate_outpad(hesn::HybridESN, states_type, out)
     pad_length = length(out) + size(hesn.model.model_data[:, 1], 1)
-    out_tmp = Adapt.adapt(typeof(out), zeros(pad_length))
+    out_tmp = adapt(typeof(out), zeros(pad_length))
     return allocate_singlepadding(states_type, out_tmp)
 end
 
 function allocate_singlepadding(::AbstractPaddedStates, out)
-    Adapt.adapt(typeof(out), zeros(size(out, 1) + 1))
+    adapt(typeof(out), zeros(size(out, 1) + 1))
 end
 function allocate_singlepadding(::StandardStates, out)
-    Adapt.adapt(typeof(out), zeros(size(out, 1)))
+    adapt(typeof(out), zeros(size(out, 1)))
 end
 function allocate_singlepadding(::ExtendedStates, out)
-    Adapt.adapt(typeof(out), zeros(size(out, 1)))
+    adapt(typeof(out), zeros(size(out, 1)))
 end

src/esn/esn_reservoir_drivers.jl

@@ -30,9 +30,9 @@ function create_states(reservoir_driver::AbstractReservoirDriver,
     train_len = size(train_data, 2) - washout
     res_size = size(reservoir_matrix, 1)
 
-    states = Adapt.adapt(typeof(train_data), zeros(res_size, train_len))
+    states = adapt(typeof(train_data), zeros(res_size, train_len))
     tmp_array = allocate_tmp(reservoir_driver, typeof(train_data), res_size)
-    _state = Adapt.adapt(typeof(train_data), zeros(res_size, 1))
+    _state = adapt(typeof(train_data), zeros(res_size, 1))
 
     for i in 1:washout
         yv = @view train_data[:, i]
@@ -59,9 +59,9 @@ function create_states(reservoir_driver::AbstractReservoirDriver,
     train_len = size(train_data, 2) - washout
     res_size = sum([size(reservoir_matrix[i], 1) for i in 1:length(reservoir_matrix)])
 
-    states = Adapt.adapt(typeof(train_data), zeros(res_size, train_len))
+    states = adapt(typeof(train_data), zeros(res_size, train_len))
     tmp_array = allocate_tmp(reservoir_driver, typeof(train_data), res_size)
-    _state = Adapt.adapt(typeof(train_data), zeros(res_size))
+    _state = adapt(typeof(train_data), zeros(res_size))
 
     for i in 1:washout
         for j in 1:length(reservoir_matrix)
@@ -108,7 +108,7 @@ echo state networks (`ESN`).
   - `leaky_coefficient`: The leaky coefficient used in the RNN.
     Defaults to 1.0.
 """
-function RNN(; activation_function=NNlib.fast_act(tanh), leaky_coefficient=1.0)
+function RNN(; activation_function=fast_act(tanh), leaky_coefficient=1.0)
     RNN(activation_function, leaky_coefficient)
 end
 
@@ -142,7 +142,7 @@ function next_state!(out, rnn::RNN, x, y, W::Vector, W_in, b, tmp_array)
 end
 
 function allocate_tmp(::RNN, tmp_type, res_size)
-    return [Adapt.adapt(tmp_type, zeros(res_size, 1)) for i in 1:2]
+    return [adapt(tmp_type, zeros(res_size, 1)) for i in 1:2]
 end
 
 #multiple RNN driver
@@ -210,7 +210,7 @@ function next_state!(out, mrnn::MRNN, x, y, W, W_in, b, tmp_array)
 end
 
 function allocate_tmp(::MRNN, tmp_type, res_size)
-    return [Adapt.adapt(tmp_type, zeros(res_size, 1)) for i in 1:2]
+    return [adapt(tmp_type, zeros(res_size, 1)) for i in 1:2]
 end
 
 abstract type AbstractGRUVariant end
@@ -280,7 +280,7 @@ This driver is based on the GRU architecture [^Cho2014].
     "_Learning phrase representations using RNN encoder-decoder for statistical machine translation._"
     arXiv preprint arXiv:1406.1078 (2014).
 """
-function GRU(; activation_function=[NNlib.sigmoid, NNlib.sigmoid, tanh],
+function GRU(; activation_function=[sigmoid, sigmoid, tanh],
         inner_layer=fill(scaled_rand, 2),
         reservoir=fill(rand_sparse, 2),
         bias=fill(scaled_rand, 2),
@@ -344,7 +344,7 @@ function next_state!(out, gru::GRUParams, x, y, W, W_in, b, tmp_array)
 end
 
 function allocate_tmp(::GRUParams, tmp_type, res_size)
-    return [Adapt.adapt(tmp_type, zeros(res_size, 1)) for i in 1:9]
+    return [adapt(tmp_type, zeros(res_size, 1)) for i in 1:9]
 end
 
 #W=U, W_in=W in papers. x=h, and y=x. I know, it's confusing. ( on the left our notation)

src/esn/hybridesn.jl

@@ -109,7 +109,7 @@ function HybridESN(model,
 
     if states_type isa AbstractPaddedStates
         in_size = size(train_data, 1) + 1
-        train_data = vcat(Adapt.adapt(matrix_type, ones(1, size(train_data, 2))),
+        train_data = vcat(adapt(matrix_type, ones(1, size(train_data, 2))),
             train_data)
     else
         in_size = size(train_data, 1)

src/predict.jl

@@ -116,7 +116,7 @@ end
 
 #single matrix for other training methods
 function output_storing(training_method, out_size, prediction_len, storing_type)
-    return Adapt.adapt(storing_type, zeros(out_size, prediction_len))
+    return adapt(storing_type, zeros(out_size, prediction_len))
 end
 
 #general storing -> single matrix