[FIX] Minor modifications to update to julia 0.5

Author: frapac

src/SDPoptimize.jl

@@ -527,14 +527,19 @@ function sdp_forward_single_simulation(model::StochDynProgModel,
                                         V,
                                         display=true::Bool)
 
+    if VERSION.minor < 5
+        scenario = reshape(scenario, size(scenario, 1), size(scenario, 3))
+    end
     TF = model.stageNumber
     law = model.noises
     u_bounds = model.ulim
     x_bounds = model.xlim
     x_steps = param.stateSteps
 
     #Compute cartesian product spaces
-    product_states, product_controls = generate_grid(model, param)
+    p_states, p_controls = generate_grid(model, param)
+    product_states = collect(p_states)
+    product_controls = collect(p_controls)
 
     controls = Inf*ones(TF-1, 1, model.dimControls)
     states = Inf*ones(TF, 1, model.dimStates)
@@ -591,7 +596,7 @@ function sdp_forward_single_simulation(model::StochDynProgModel,
                 current_V = current_V/count_admissible_w
                 if (current_V < best_V)&(count_admissible_w>0)
                     best_control = u
-                    best_state = model.dynamics(t, x, u, scenario[t,1,:])
+                    best_state = model.dynamics(t, x, u, scenario[t,:])
                     best_V = current_V
                 end
             end
@@ -607,7 +612,7 @@ function sdp_forward_single_simulation(model::StochDynProgModel,
                 index_state = index_state +1
                 states[t+1,1,index_state] = xj
             end
-            J += model.costFunctions(t, x, best_control, scenario[t,1,:])
+            J += model.costFunctions(t, x, best_control, scenario[t,:])
         end
 
     else
@@ -622,15 +627,15 @@ function sdp_forward_single_simulation(model::StochDynProgModel,
 
             for u = product_controls
 
-                next_state = model.dynamics(t, x, u, scenario[t,1,:])
+                next_state = model.dynamics(t, x, u, scenario[t,:])
 
                 if model.constraints(t, x, u, scenario[t])&&SDPutils.is_next_state_feasible(next_state, model.dimStates, model.xlim)
                     ind_next_state = SDPutils.real_index_from_variable(next_state, x_bounds, x_steps)
                     next_V = Vitp[ind_next_state...]
-                    current_V = model.costFunctions(t, x, u, scenario[t,1,:]) + next_V
+                    current_V = model.costFunctions(t, x, u, scenario[t,:]) + next_V
                     if (current_V < best_V)
                         best_control = u
-                        best_state = model.dynamics(t, x, u, scenario[t,1,:])
+                        best_state = model.dynamics(t, x, u, scenario[t,:])
                         best_V = current_V
                     end
                 end
@@ -647,7 +652,7 @@ function sdp_forward_single_simulation(model::StochDynProgModel,
                 index_state = index_state +1
                 states[t+1,1,index_state] = xj
             end
-            J += model.costFunctions(t, x, best_control, scenario[t,1,:])
+            J += model.costFunctions(t, x, best_control, scenario[t,:])
         end
     end
 

src/objects.jl

@@ -35,7 +35,8 @@ type LinearSPModel <: SPModel
 
     initialState::Array{Float64, 1}
 
-    costFunctions::Union{Function, Vector{Function}}
+    #FIXME: add a correct typage for costFunctions that dont break in 0.5
+    costFunctions
     dynamics::Function
     noises::Vector{NoiseLaw}
 
@@ -115,7 +116,8 @@ type StochDynProgModel <: SPModel
     function StochDynProgModel(model::LinearSPModel, final, cons)
         if isa(model.costFunctions, Function)
             cost = model.costFunctions
-        elseif isa(model.costFunctions, Vector{Function})
+        #FIXME: broken test since 0.5 release
+        else
             function cost(t,x,u,w)
                 current_cost = -Inf
                 for aff_func in model.costFunctions

test/sdp.jl

@@ -66,17 +66,9 @@ facts("SDP algorithm") do
     end
 
     # Define cost corresponding to each timestep:
-    function cost_t(t, x, u, w)
-        return COST[t] * (u[1])
-    end
-
-    function constraints(t, x, u, w)
-        return true
-    end
-
-    function finalCostFunction(x)
-        return 0.
-    end
+    cost_t(t, x, u, w) = COST[t] * (u[1])
+    constraints(t, x, u, w) = true
+    finalCostFunction(x) = 0.
 
     """Build admissible scenarios for water inflow over the time horizon."""
     function build_scenarios(n_scenarios::Int64, N_STAGES)
@@ -145,9 +137,9 @@ facts("SDP algorithm") do
             set_state_bounds(modelSDPPiecewise, x_bounds)
 
             modelSDPLinear = StochDynamicProgramming.LinearSPModel(TF,
-            u_bounds, x0,
-            cost_t,
-            dynamic, aleas)
+                                                                   u_bounds, x0,
+                                                                   cost_t,
+                                                                   dynamic, aleas)
 
             set_state_bounds(modelSDPLinear, x_bounds)