readme example working, but cpp not

gamma-opt · Aug 30, 2024 · 3928054 · 3928054
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diff --git a/examples/readme_test.jl b/examples/readme_test.jl
@@ -0,0 +1,27 @@
+using DecisionProgramming
+
+diagram = InfluenceDiagram()
+
+add_node!(diagram, DecisionNode("A", [], ["a", "b"]))
+add_node!(diagram, DecisionNode("D", ["B", "C"], ["k", "l"]))
+add_node!(diagram, ChanceNode("B", ["A"], ["x", "y"]))
+add_node!(diagram, ChanceNode("C", ["A"], ["v", "w"]))
+add_node!(diagram, ValueNode("V", ["D"]))
+
+generate_arcs!(diagram)
+
+add_probabilities!(diagram, "B", [0.4 0.6; 0.6 0.4])
+add_probabilities!(diagram, "C", [0.7 0.3; 0.3 0.7])
+add_utilities!(diagram, "V", [1.5, 1.7])
+
+using JuMP
+model, z, variables = generate_model(diagram, model_type="RJT")
+
+using HiGHS
+optimizer = optimizer_with_attributes(
+    () -> HiGHS.Optimizer()
+)
+set_optimizer(model, optimizer)
+optimize!(model)
+
+Z = DecisionStrategy(diagram, z)
diff --git a/src/decision_model.jl b/src/decision_model.jl
@@ -434,15 +434,43 @@ end
 
 
 function factorization_constraints(model::Model, diagram::InfluenceDiagram, name::Name, μ_statevars::Array{VariableRef}, μ_bar_statevars::Array{VariableRef}, z::OrderedDict{Name, DecisionVariable})
-    I_j_mapping_in_cluster = [findfirst(isequal(node), diagram.RJT.clusters[name]) for node in diagram.I_j[name]] # Map the information set to the variables in the cluster
+    #I_j_mapping_in_cluster = [findfirst(isequal(node), diagram.RJT.clusters[name]) for node in diagram.I_j[name]] # Map the information set to the variables in the cluster
+    I_j_mapping_in_cluster = [findfirst(isequal(node), diagram.RJT.clusters[name]) for node in diagram.I_j[name]]
+    I_j_states_mapping_in_cluster = similar(I_j_mapping_in_cluster)
+    for node_index in 1:length(I_j_states_mapping_in_cluster)
+        I_j_states_mapping_in_cluster[node_index] = diagram.S[diagram.RJT.clusters[name][node_index]]
+    end
+    #println("kokeilu:")
+    #println(I_j_mapping_in_cluster)
+    #println(I_j_states_mapping_in_cluster)
+    #println("")
     for index in CartesianIndices(μ_bar_statevars)
         for s_j in 1:length(diagram.States[name])
+            #println(s_j)
+            #println(Tuple(index)[I_j_states_mapping_in_cluster]...,s_j)
+            #println(diagram.X[name])
+            #println(typeof(diagram.X[name]))
             if isa(diagram.Nodes[name], ChanceNode)
                 # μ_{C_v} = μ_{\bar{C}_v}*p
                 @constraint(model, μ_statevars[Tuple(index)...,s_j] == diagram.X[name][Tuple(index)[I_j_mapping_in_cluster]...,s_j]*μ_bar_statevars[index])
             elseif isa(diagram.Nodes[name], DecisionNode)
                 # μ_{C_v} ≤ z
-                @constraint(model, μ_statevars[Tuple(index)...,s_j] <= z[name].z[Tuple(index)[I_j_mapping_in_cluster]...,s_j])
+
+                #println(index)
+                #println(typeof(index))
+                #println(I_j_states_mapping_in_cluster)
+                #println(s_j)
+                #println(z[name])
+                #println(z[name].z)
+                #if I_j_mapping_in_cluster ==[2, 3]
+                #    println(z[name].z[1, 1, 1])
+                #end
+                #println([I_j_states_mapping_in_cluster]...,s_j)
+                #println(Tuple(index)[I_j_states_mapping_in_cluster]...,s_j)
+                #println(z[name].z[Tuple(index)[I_j_states_mapping_in_cluster]...,s_j])
+                #println("")
+
+                @constraint(model, μ_statevars[Tuple(index)...,s_j] <= z[name].z[Tuple(index)[I_j_states_mapping_in_cluster]...,s_j])
             end
         end
     end