fix the energy mode

Author: GiggleLiu

src/cspmodels.jl

@@ -4,7 +4,7 @@ function generate_tensors(β::T, problem::ConstraintSatisfactionProblem) where T
     objs = ProblemReductions.objectives(problem)
     ixs = vcat([t.variables for t in cons], [t.variables for t in objs])
 	# generate tensors for x = e^β
-    x = exp(β)
+    x = energy_mode(problem) === LargerSizeIsBetter() ? exp(β) : exp(-β)
     tensors = vcat(
         Array{T}[reshape(map(s -> s ? one(x) : zero(x), t.specification), ntuple(i->num_flavors(problem), length(t.variables))) for t in cons],
         Array{T}[reshape(map(s -> x^s, t.specification), ntuple(i->num_flavors(problem), length(t.variables))) for t in objs]

test/cspmodels.jl

@@ -1,5 +1,5 @@
 using Test
-using TensorInference
+using TensorInference, ProblemReductions.Graphs
 using GenericTensorNetworks
 
 @testset "marginals" begin
@@ -25,4 +25,10 @@ using GenericTensorNetworks
     model = MMAPModel(problem, β; queryvars=[1,4])
     logp, config = most_probable_config(model)
     @test config == [0, 0]
+
+    β = 1.0
+    problem = SpinGlass(g, -ones(Int, ne(g)), zeros(Int, nv(g)))
+    model = TensorNetworkModel(problem, β; mars=[[2, 3]])
+    samples = sample(model, 100)
+    @test sum(energy.(Ref(problem), samples))/100 <= -14
 end