modified: CHANGELOG.md

	modified:   Project.toml
	modified:   src/sub/design_models.jl
	modified:   test/system_evaluation_test.jl

CHANGELOG.md

@@ -2,6 +2,7 @@
 
 ## v0.1.7
 
+* Add dict for return for _extract_model_from_machine functions.
 * Tests improvement.
 
 ## v0.1.6

Project.toml

@@ -1,7 +1,7 @@
 name = "AutomationLabsSystems"
 uuid = "6d3dfdf0-e107-48c6-a6ff-eced1a5a9334"
 authors = ["Pierre BLAUD <pierre.blaud@ikmail.com> and contributors"]
-version = "0.1.6"
+version = "0.1.7"
 
 [deps]
 AutomationLabsIdentification = "48ff5a6f-d08b-4053-9585-6a9e3e078386"

src/sub/design_models.jl

@@ -43,7 +43,11 @@ function _extract_model_from_machine(
 )
 
     # Extract best model from the machine
-    return MLJ.fitted_params(MLJ.fitted_params(machine_mlj).machine).best_fitted_params[1]
+    f =  MLJ.fitted_params(MLJ.fitted_params(machine_mlj).machine).best_fitted_params[1]
+
+    result = Dict(:f => f)
+
+    return result
 end
 
 """
@@ -53,15 +57,17 @@ A function for design the system (model and constraints) with MathematicalSystem
 function _extract_model_from_machine(
     model_type::MLJMultivariateStatsInterface.MultitargetLinearRegressor,
     machine_mlj,
-    nbr_state,
 )
 
     # Extract model from the machine
     AB_t = MLJ.fitted_params(machine_mlj).coefficients
+    nbr_state = size(AB_t, 2)
+
     AB = copy(AB_t')
     A = AB[:, 1:nbr_state]
     B = AB[:, nbr_state+1:end]
 
+    result = Dict(:A => A, :B => B)
     # Set the system
-    return A, B
+    return result
 end

test/system_evaluation_test.jl

@@ -81,26 +81,26 @@ import AutomationLabsSystems: _extract_model_from_machine
     f_rnn = _extract_model_from_machine(rnn_type, rnn)
     f_lstm = _extract_model_from_machine(lstm_type, lstm)
     f_gru = _extract_model_from_machine(gru_type, gru)
-    f_linear = _extract_model_from_machine(linear_type, linear, 4)
+    f_linear = _extract_model_from_machine(linear_type, linear)
 
     nbr_state = 4
     nbr_input = 2
     variation = "discrete"
 
-    sys_fnn = proceed_system(f_fnn, nbr_state, nbr_input, variation)
-    sys_icnn = proceed_system(f_icnn, nbr_state, nbr_input, variation)
-    sys_resnet = proceed_system(f_resnet, nbr_state, nbr_input, variation)
-    sys_polynet = proceed_system(f_polynet, nbr_state, nbr_input, variation)
-    sys_densenet = proceed_system(f_densenet, nbr_state, nbr_input, variation)
-    sys_rbf = proceed_system(f_rbf, nbr_state, nbr_input, variation)
-    sys_neuralode = proceed_system(f_neuralode, nbr_state, nbr_input, variation)
-    sys_rknn1 = proceed_system(f_rknn1, nbr_state, nbr_input, variation)
-    sys_rknn2 = proceed_system(f_rknn2, nbr_state, nbr_input, variation)
-    sys_rknn4 = proceed_system(f_rknn4, nbr_state, nbr_input, variation)
-    sys_rnn = proceed_system(f_rnn, nbr_state, nbr_input, variation)
-    sys_lstm = proceed_system(f_lstm, nbr_state, nbr_input, variation)
-    sys_gru = proceed_system(f_gru, nbr_state, nbr_input, variation)
-    sys_linear = proceed_system(f_linear[1], f_linear[2], nbr_state, nbr_input, variation)
+    sys_fnn = proceed_system(f_fnn[:f], nbr_state, nbr_input, variation)
+    sys_icnn = proceed_system(f_icnn[:f], nbr_state, nbr_input, variation)
+    sys_resnet = proceed_system(f_resnet[:f], nbr_state, nbr_input, variation)
+    sys_polynet = proceed_system(f_polynet[:f], nbr_state, nbr_input, variation)
+    sys_densenet = proceed_system(f_densenet[:f], nbr_state, nbr_input, variation)
+    sys_rbf = proceed_system(f_rbf[:f], nbr_state, nbr_input, variation)
+    sys_neuralode = proceed_system(f_neuralode[:f], nbr_state, nbr_input, variation)
+    sys_rknn1 = proceed_system(f_rknn1[:f], nbr_state, nbr_input, variation)
+    sys_rknn2 = proceed_system(f_rknn2[:f], nbr_state, nbr_input, variation)
+    sys_rknn4 = proceed_system(f_rknn4[:f], nbr_state, nbr_input, variation)
+    sys_rnn = proceed_system(f_rnn[:f], nbr_state, nbr_input, variation)
+    sys_lstm = proceed_system(f_lstm[:f], nbr_state, nbr_input, variation)
+    sys_gru = proceed_system(f_gru[:f], nbr_state, nbr_input, variation)
+    sys_linear = proceed_system(f_linear[:A], f_linear[:B], nbr_state, nbr_input, variation)
 
     fnn_type_2 = proceed_system_model_evaluation(sys_fnn)
     icnn_type_2 = proceed_system_model_evaluation(sys_icnn)