[UPD] Rescope PR

- add error handling in get_control
- fix some docstrings
- improve naming

Author: frapac

src/SDDPoptimize.jl

@@ -161,7 +161,11 @@ Parameters:
 
 
 Return:
-Float64 (estimation of the upper bound)
+- upb (Float64)
+	estimation of upper bound
+
+- costs (Vector{Float64})
+	Costs along different trajectories
 
 """
 function estimate_upper_bound(model::SPModel, param::SDDPparameters, V::Vector{PolyhedralFunction}, problem::Vector{JuMP.Model}, n_simulation=1000::Int)
@@ -179,7 +183,7 @@ end
 
 
 
-"""Build a collection of cuts initialize at 0"""
+"""Build a collection of cuts initialized at 0"""
 function get_null_value_functions_array(model::SPModel)
 
     V = Vector{PolyhedralFunction}(model.stageNumber)

src/SDPoptimize.jl

@@ -189,7 +189,7 @@ Returns :
 """
 function solve_DP(model::SPModel,
                   param::SDPparameters,
-                  display=true::Int64)
+                  display=0::Int64)
 
     SDPmodel = build_sdpmodel_from_spmodel(model::SPModel)
 
@@ -500,7 +500,7 @@ Returns :
 - costs (Vector{Float64})
     the cost of the optimal control over the scenario provided
 
-- stocks (Array{Float64})
+- states (Array{Float64})
     the state of the controlled system at each time step
 
 - controls (Array{Float64})
@@ -559,6 +559,9 @@ Returns :
 """
 function get_control(model::SPModel,param::SDPparameters,V::Array{Float64}, t::Int64, x::Array)
 
+	if(param.infoStructure != "DH")
+		error("Infostructure must be decision-hazard.")
+	end
     SDPmodel = build_sdpmodel_from_spmodel(model)
 
     product_controls = product([SDPmodel.ulim[i][1]:param.controlSteps[i]:SDPmodel.ulim[i][2] for i in 1:SDPmodel.dimControls]...)
@@ -636,11 +639,15 @@ Parameters:
 the alea realization
 
 Returns :
-- V(x0) (Float64)
+- optimal control (tuple(Float64))
 
 """
 function get_control(model::SPModel,param::SDPparameters,V::Array{Float64}, t::Int64, x::Array, w::Array)
 
+	if(param.infoStructure != "HD")
+		error("Infostructure must be hazard-decision.")
+	end
+
     SDPmodel = build_sdpmodel_from_spmodel(model)
 
     product_controls = product([SDPmodel.ulim[i][1]:param.controlSteps[i]:SDPmodel.ulim[i][2] for i in 1:SDPmodel.dimControls]...)

src/forwardBackwardIterations.jl

@@ -62,14 +62,14 @@ function forward_simulations(model::SPModel,
                             returnCosts=true::Bool,
                             init=false::Bool,
                             display=false::Bool)
-    
+
     T = model.stageNumber
-    nb_forward = size(xi)[2] 
-    
+    nb_forward = size(xi)[2]
+
     if ndims(xi)!=3
         if ndims(xi)==2
             warn("noise scenario are not given in the right shape. Assumed to be real valued noise.")
-            xi = reshape(xi,(T,nb_forward,1)) 
+            xi = reshape(xi,(T,nb_forward,1))
         else
             error("wrong dimension of noise scenarios")
         end
@@ -223,7 +223,7 @@ Add to polyhedral function a cut with shape Vt >= beta + <lambda,.>
 								V::Vector{PolyhedralFunction},
 								solverProblems::Vector{JuMP.Model},
 								stockTrajectories::Array{Float64, 3},
-								law, 
+								law,
 								init=false::Bool)
 
 								T = model.stageNumber
@@ -258,7 +258,7 @@ Add to polyhedral function a cut with shape Vt >= beta + <lambda,.>
 											costs[w] = nextstep.cost
 										end
 
-										# Compute esperancy of subgradient:
+										# Compute expectation of subgradient:
 										subgradient = vec(sum(law[t].proba' .* subgradient_array, 2))
 										# ... and esperancy of cost:
 										costs_npass[k] = dot(law[t].proba, costs)

test/runtests.jl

@@ -485,6 +485,8 @@ facts("SDP algorithm") do
             state_ref[1] = stocks_sdp[2,1,1]
             state_ref[2] = stocks_sdp[2,1,2]
 
+            @fact_throws get_control(modelSDP,paramsSDP,V_sdp3, 1, x)
+            paramsSDP.infoStructure = "DH"
             @fact (get_control(modelSDP,paramsSDP,V_sdp3, 1, x)[1] >= CONTROL_MIN) --> true
             @fact (get_control(modelSDP,paramsSDP,V_sdp3, 1, x)[1] >= CONTROL_MIN) --> true