various test fixes

src/esn/deepesn.jl

@@ -24,6 +24,7 @@ function DeepESN(train_data,
         rng = _default_rng(),
         T = Float64,
         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))),

test/esn/test_drivers.jl

@@ -29,12 +29,13 @@ esn_configs = [
     Dict(:reservoir => rand_sparse(; radius = 1.2),
         :reservoir_driver => GRU(variant = Minimal(),
             reservoir = rand_sparse(; radius = 1.0, sparsity = 0.5),
-            inner_layer = scaled_rand)),
+            inner_layer = scaled_rand,
+            bias = scaled_rand)),
     Dict(:reservoir => rand_sparse(; radius = 1.2),
         :reservoir_driver => MRNN(activation_function = (tanh, sigmoid),
             scaling_factor = (0.8, 0.1))),
 ]
 
-for config in esn_configs
+@testset "Test Drivers: $config" for config in esn_configs
     test_esn(input_data, target_data, training_method, config)
 end

test/esn/test_train.jl

@@ -9,10 +9,12 @@ const input_data = reduce(hcat, data[1:(train_len - 1)])
 const target_data = reduce(hcat, data[2:train_len])
 const test = reduce(hcat, data[(train_len + 1):(train_len + predict_len)])
 const reg = 10e-6
+#test_types = [Float64, Float32, Float16]
 
 Random.seed!(77)
-esn = ESN(input_data;
-    reservoir = RandSparseReservoir(res_size, 1.2, 0.1))
+res = rand_sparse(; radius=1.2, sparsity=0.1)
+esn = ESN(input_data, 1, res_size;
+    reservoir = rand_sparse)
 
 training_methods = [
     StandardRidge(regularization_coeff = reg),
@@ -21,14 +23,9 @@ training_methods = [
     EpsilonSVR(),
 ]
 
-for t in training_methods
-    output_layer = train(esn, target_data, t)
+# TODO check types
+@testset "Training Algo Tests: $ta" for ta in training_methods
+    output_layer = train(esn, target_data, ta)
     output = esn(Predictive(input_data), output_layer)
     @test mean(abs.(target_data .- output)) ./ mean(abs.(target_data)) < 0.22
 end
-
-for t in training_methods
-    output_layer = train(esn, target_data, t)
-    output, states = esn(Predictive(input_data), output_layer, save_states = true)
-    @test size(states) == (res_size, size(input_data, 2))
-end

test/test_states.jl

@@ -3,6 +3,7 @@ using ReservoirComputing
 test_array = [1, 2, 3, 4, 5, 6, 7, 8, 9]
 extension = [0, 0, 0]
 padding = 10.0
+test_types = [Float64, Float32, Float16]
 
 nlas = [(NLADefault(), test_array),
     (NLAT1(), [1, 2, 9, 4, 25, 6, 49, 8, 81]),
@@ -18,20 +19,15 @@ pes = [(StandardStates(), test_array),
             1)),
     (ExtendedStates(), vcat(extension, test_array))]
 
-function test_nla(algo, expected_output)
-    nla_array = ReservoirComputing.nla(algo, test_array)
-    @test nla_array == expected_output
-end
-
-function test_states_type(state_type, expected_output)
-    states_output = state_type(NLADefault(), test_array, extension)
-    @test states_output == expected_output
-end
-
-@testset "Nonlinear Algorithms Testing" for (algo, expected_output) in nlas
-    test_nla(algo, expected_output)
-end
-
-@testset "States Testing" for (state_type, expected_output) in pes
-    test_states_type(state_type, expected_output)
-end
+@testset "States Testing" for T in test_types
+    @testset "Nonlinear Algorithms Testing: $algo $T" for (algo, expected_output) in nlas
+        nla_array = ReservoirComputing.nla(algo, T.(test_array))
+        @test nla_array == expected_output
+        @test eltype(nla_array) == T
+    end
+    @testset "States Testing: $state_type $T" for (state_type, expected_output) in pes
+        states_output = state_type(NLADefault(), T.(test_array), T.(extension))
+        @test states_output == expected_output
+        @test eltype(states_output) == T
+    end
+end