Consolidate reported issues

Author: SebastianM-C

test/scalar_dispatch.jl renamed to test/reported_issues.jl

@@ -1,13 +1,7 @@
-using Test
-using ModelingToolkitNeuralNets
-using ModelingToolkit
-using Lux
-using StableRNGs
+using Test, Lux, ModelingToolkitNeuralNets, StableRNGs, ModelingToolkit
 using OrdinaryDiffEqVerner
 
-# Test scalar dispatch for SymbolicNeuralNetwork
-# This tests the fix for issue #83
-@testset "Scalar dispatch" begin
+@testset "Scalar dispatch (issue #83)" begin
     # Create a simple UDE with scalar inputs
     @variables t X(t) Y(t)
     @parameters d
@@ -27,8 +21,8 @@ using OrdinaryDiffEqVerner
     # Now can use: sym_nn(Y, θ)[1]
     Dt = ModelingToolkit.D_nounits
     eqs_ude = [
-        Dt(X) ~ sym_nn(Y, θ)[1] - d*X,
-        Dt(Y) ~ X - d*Y
+        Dt(X) ~ sym_nn(Y, θ)[1] - d * X,
+        Dt(Y) ~ X - d * Y
     ]
 
     @named sys = System(eqs_ude, ModelingToolkit.t_nounits)
@@ -37,9 +31,8 @@ using OrdinaryDiffEqVerner
     # Test that the system can be created and solved
     prob = ODEProblem{true, SciMLBase.FullSpecialize}(
         sys_compiled,
-        [X => 1.0, Y => 1.0],
-        (0.0, 1.0),
-        [d => 0.1]
+        [X => 1.0, Y => 1.0, d => 0.1],
+        (0.0, 1.0)
     )
 
     sol = solve(prob, Vern9(), abstol = 1e-8, reltol = 1e-8)
@@ -48,24 +41,47 @@ using OrdinaryDiffEqVerner
 
     # Also test that the old array syntax still works
     eqs_ude_old = [
-        Dt(X) ~ sym_nn([Y], θ)[1] - d*X,
-        Dt(Y) ~ X - d*Y
+        Dt(X) ~ sym_nn([Y], θ)[1] - d * X,
+        Dt(Y) ~ X - d * Y
     ]
 
     @named sys_old = System(eqs_ude_old, ModelingToolkit.t_nounits)
     sys_old_compiled = mtkcompile(sys_old)
 
     prob_old = ODEProblem{true, SciMLBase.FullSpecialize}(
         sys_old_compiled,
-        [X => 1.0, Y => 1.0],
-        (0.0, 1.0),
-        [d => 0.1]
+        [X => 1.0, Y => 1.0, d => 0.1],
+        (0.0, 1.0)
     )
 
     sol_old = solve(prob_old, Vern9(), abstol = 1e-8, reltol = 1e-8)
 
     @test SciMLBase.successful_retcode(sol_old)
 
     # Both solutions should be the same
-    @test sol.u ≈ sol_old.u
+    @test sol.u == sol_old.u
+end
+
+@testset "Issue #58" begin
+    # Preparation
+    rng = StableRNG(123)
+    chain = Lux.Chain(
+        Lux.Dense(1 => 3, Lux.softplus, use_bias = false),
+        Lux.Dense(3 => 3, Lux.softplus, use_bias = false),
+        Lux.Dense(3 => 1, Lux.sigmoid_fast, use_bias = false)
+    )
+
+    # Default names.
+    NN, NN_p = SymbolicNeuralNetwork(; chain, n_input = 1, n_output = 1, rng)
+    @test ModelingToolkit.getname(NN) == :nn_name
+    @test ModelingToolkit.getname(NN_p) == :p
+
+    # Trying to set specific names.
+    nn_name = :custom_nn_name
+    nn_p_name = :custom_nn_p_name
+    NN, NN_p = SymbolicNeuralNetwork(;
+        chain, n_input = 1, n_output = 1, rng, nn_name, nn_p_name)
+
+    @test ModelingToolkit.getname(NN)==nn_name broken=true # :nn_name # Should be :custom_nn_name
+    @test ModelingToolkit.getname(NN_p) == nn_p_name
 end

test/runtests.jl

@@ -6,5 +6,5 @@ using SafeTestsets
     @safetestset "QA" include("qa.jl")
     @safetestset "Basic" include("lotka_volterra.jl")
     @safetestset "MTK model macro compatibility" include("macro.jl")
-    @safetestset "Scalar dispatch" include("scalar_dispatch.jl")
+    @safetestset "Reported issues" include("reported_issues.jl")
 end