[FIX] Fix some tests and examples

Author: Henri-Gerard

examples/risk-example.jl

@@ -15,7 +15,7 @@ using StochDynamicProgramming, Clp
 println("library loaded")
 
 run_expectation = true # false if you don't want to test expectation
-run_CVaR        = true # false if you don't want to test CVaR
+run_AVaR        = true # false if you don't want to test AVaR
 run_WorstCase   = true # false if you don't want to test WorstCase
 
 ######## Optimization parameters  ########
@@ -87,13 +87,13 @@ if run_expectation
     toc(); println();
 end
 
-######### Solving the problem via SDDP with CVaR
-if run_CVaR
+######### Solving the problem via SDDP with AVaR
+if run_AVaR
     tic()
-    spmodel = LinearSPModel(N_STAGES,u_bounds,[S0],cost_t,dynamic,xi_laws, CVaR(0.5))
+    spmodel = LinearSPModel(N_STAGES,u_bounds,[S0],cost_t,dynamic,xi_laws, riskMeasure = AVaR(0.5))
     set_state_bounds(spmodel, s_bounds) 	# adding the bounds to the model
-    println("CVaR's model set up")
-    println("Starting resolution with CVaR")
+    println("AVaR's model set up")
+    println("Starting resolution with AVaR")
     # 10 forward pass, stop at MAX_ITER
     paramSDDP = SDDPparameters(SOLVER,
                                passnumber=10,

src/objects.jl

@@ -116,13 +116,13 @@ type LinearSPModel <: SPModel
                            x0,                  # initial state
                            cost,                # cost function
                            dynamic,             # dynamic
-                           aleas,               # modelling of noises
+                           aleas;               # modelling of noises
                            Vfinal=nothing,      # final cost
                            eqconstr=nothing,    # equality constraints
                            ineqconstr=nothing,  # inequality constraints
                            info=:HD,            # information structure
                            control_cat=nothing, # category of controls
-                           riskMeasure = Expectation();
+                           riskMeasure = Expectation()
                            )
 
         # infer the problem's dimension

src/risk.jl

@@ -80,7 +80,7 @@ Return a dirac on the worst cost as a probability distribution.
 """
 function argsup_proba_risk(prob,riskMeasure::WorstCase,costs)
     proba = zeros(length(prob))
-    proba[indmax(prob)] = 1
+    proba[indmax(costs)] = 1
     return proba
 end
 

test/changeprob.jl

@@ -32,7 +32,7 @@ end
     @testset "Equality Expectation AVaR(1)" begin
         n = 100
         prob = 1/n*ones(n)
-        @test sum(abs.(argsup_proba_risk(prob,AVaR(0),1:n)-argsup_proba_risk(prob,Expectation(),1:n)) .<= EPSILON*ones(n)) == n
+        @test sum(abs.(argsup_proba_risk(prob,AVaR(1),1:n)-argsup_proba_risk(prob,Expectation(),1:n)) .<= EPSILON*ones(n)) == n
     end
 
     @testset "Equality Expectation ConvexCombi(beta,1)" begin
@@ -54,7 +54,7 @@ end
         betamin = rand()/2+0.5
         n = 100
         prob = 1/n*ones(n)
-        @test argsup_proba_risk(prob, AVaR(betamax), 1:n)'*(n:-1:1) - argsup_proba_risk(prob, AVaR(betamin), 1:n)'*(n:-1:1) >= 0
+        @test argsup_proba_risk(prob, AVaR(betamin), 1:n)'*(n:-1:1) - argsup_proba_risk(prob, AVaR(betamax), 1:n)'*(n:-1:1) >= 0
     end
 end
 
@@ -77,7 +77,7 @@ end
 
     status = solve(m)
 
-    probaAVaR = argsup_proba_risk(prob, AVaR(beta), sortperm(X, rev = true))
+    probaAVaR = argsup_proba_risk(prob, AVaR(beta), X)
 
     @test abs(probaAVaR'*X - getobjectivevalue(m)) <= EPSILON
 end

test/sddp.jl

@@ -189,7 +189,6 @@ using Base.Test
                                                       u_bounds, x0,
                                                       cost,
                                                       dynamic, laws,
-                                                      Expectation(),
                                                       control_cat=controlCat)
         set_state_bounds(model2, x_bounds)
         @test_throws ErrorException solve_SDDP(model2, param, 0)