[FIX] clean and comments before pull request

Author: Henri-Gerard

examples/stock-example.jl

@@ -61,7 +61,7 @@ end
 ######## Setting up the SPmodel
 s_bounds = [(0, 1)] 			# bounds on the state
 u_bounds = [(CONTROL_MIN, CONTROL_MAX)] # bounds on controls
-spmodel = LinearSPModel(N_STAGES,u_bounds,[S0],cost_t,dynamic,xi_laws, ConvexCombi(0,0))
+spmodel = LinearSPModel(N_STAGES,u_bounds,[S0],cost_t,dynamic,xi_laws, Expectation())
 set_state_bounds(spmodel, s_bounds) 	# adding the bounds to the model
 println("Model set up")
 

src/forwardBackwardIterations.jl

@@ -316,7 +316,7 @@ function compute_cuts_hd!(model::SPModel, param::SDDPparameters,
     # It is initialized at 0. If all problem are infeasible for
     # current timestep, then proba remains equal to 0 and not cut is added.
     proba = zeros(model.noises[t].supportSize)
-    
+
     # We iterate other the possible realization of noise:
     for w in 1:model.noises[t].supportSize
 
@@ -343,9 +343,10 @@ function compute_cuts_hd!(model::SPModel, param::SDDPparameters,
     if sum(proba) > 0
         # Scale probability (useful when some problems where infeasible):
         proba /= sum(proba)
-         
+
+        #Modify the probability vector to compute the value of the risk measure
         proba = change_proba_risk(proba,model.riskMeasure,sortperm(costs,rev = true))
-        
+
         # Compute expectation of subgradient λ:
         subgradient = vec(sum(proba' .* subgradient_array, 2))
         # ... expectation of cost: