[FIX] Fix unit-tests

Author: frapac

src/SDDPoptimize.jl

@@ -164,7 +164,7 @@ Return:
 Float64 (estimation of the upper bound)
 
 """
-function estimate_upper_bound(model::SPmodel, param::SDDPparameters, V::Vector{PolyhedralFunction}, problem::Vector{JuMP.Model}, n_simulation=1000::Int)
+function estimate_upper_bound(model::SPModel, param::SDDPparameters, V::Vector{PolyhedralFunction}, problem::Vector{JuMP.Model}, n_simulation=1000::Int)
 
     aleas = simulate_scenarios(model.noises, n_simulation)
 
@@ -343,7 +343,6 @@ function initialize_value_functions( model::SPModel,
                   solverProblems,
                   stockTrajectories,
                   model.noises,
-                  true,
                   true)
 
     return V, solverProblems
@@ -432,6 +431,7 @@ Parameters:
 
 Return:
 current lower bound of the problem (Float64)
+
 """
 function get_lower_bound(model::SPModel, param::SDDPparameters,
                             V::Vector{PolyhedralFunction})

src/extensiveFormulation.jl

@@ -25,7 +25,7 @@ function extensive_formulation(model,
 
     T = model.stageNumber-1
 
-    mod = Model(solver=params.solver)
+    mod = Model(solver=param.solver)
 
 
     #Calculate the number of nodes n at each step on the scenario tree
@@ -68,14 +68,14 @@ function extensive_formulation(model,
                 #Add bounds constraint on the control
                 @addConstraint(mod,[u[t,DIM_CONTROL*(m-1)+k] for k = 1:DIM_CONTROL] .>= [model.ulim[k][1] for k = 1:DIM_CONTROL])
                 @addConstraint(mod,[u[t,DIM_CONTROL*(m-1)+k] for k = 1:DIM_CONTROL] .<= [model.ulim[k][2] for k = 1:DIM_CONTROL])
-    
+
                 #Add dynamic constraints
                 @addConstraint(mod,
                 [x[t+1,DIM_STATE*(m-1)+k] for k = 1:DIM_STATE] .== model.dynamics(t,
                                                                                     [x[t,DIM_STATE*(n-1)+k] for k = 1:DIM_STATE],
                                                                                     [u[t,DIM_CONTROL*(m-1)+k] for k = 1:DIM_CONTROL],
                                                                                     laws[t].support[xi]))
-                                                                                   
+
                 #Add constraints to define the cost at each node
                 @addConstraint(mod,
                 c[t,m] == model.costFunctions(t,

src/noises.jl

@@ -17,41 +17,21 @@ type NoiseLaw
     support::Array{Float64,2}
     # Probabilities of points:
     proba::Vector{Float64}
-end
-
-
-"""
-Instantiate an element of NoiseLaw
-
-
-Parameters:
-- supportSize (Int64)
-    Number of points in discrete distribution
-
-- support
-    Position of each point
+	function NoiseLaw(supportSize, support, proba)
+		supportSize = convert(Int64,supportSize)
+		if ndims(support)==1
+			support = reshape(support,1,length(support))
+		end
 
-- proba
-    Probabilities of each point
-
-
-Return:
-- NoiseLaw
+		if ndims(proba) == 2
+			proba = vec(proba)
+		elseif  ndims(proba) >= 2
+			proba = squeeze(proba,1)
+		end
 
-"""
-function NoiseLaw(supportSize, support, proba)
-    supportSize = convert(Int64,supportSize)
-    if ndims(support)==1
-        support = reshape(support,1,length(support))
-    end
-
-    if ndims(proba) == 2
-        proba = vec(proba)
-    elseif  ndims(proba) >= 2
-        proba = squeeze(proba,1)
-    end
+		return new(supportSize,support,proba)
+	end
 
-    return NoiseLaw(supportSize,support,proba)
 end