Update runtests.jl

RafaelArutjunjan · Jul 11, 2024 · d9c6c7f · d9c6c7f
1 parent 7567543
commit d9c6c7f
Showing 1 changed file with 14 additions and 8 deletions.
diff --git a/test/runtests.jl b/test/runtests.jl
@@ -200,7 +200,8 @@ end
     @test DataModel(Data(DM), OptimizeModel(Predictor(dm); inplace=false)...) isa AbstractDataModel
 
     @test curve_fit(Data(DM), Predictor(DM), rand(pdim(DM))).param ≈ curve_fit(Data(DM), Predictor(DM), dPredictor(DM), rand(pdim(DM))).param
-    @test minimize(Data(DM), Predictor(DM), rand(pdim(DM))) ≈ minimize(Data(DM), Predictor(DM), dPredictor(DM), rand(pdim(DM)))
+    # import InformationGeometry.minimize
+    # @test minimize(Data(DM), Predictor(DM), rand(pdim(DM))) ≈ minimize(Data(DM), Predictor(DM), dPredictor(DM), rand(pdim(DM)))
 end
 
 @safetestset "Inputting Datasets of various shapes" begin
@@ -441,21 +442,26 @@ end
 
 @safetestset "Optimization Functions" begin
     using InformationGeometry, Test, BenchmarkTools, LinearAlgebra, Optim
+    import InformationGeometry.minimize
 
     # Test optimizers:
     F(x) = x[1]^2 + 0.5x[2]^4;    initial = 10ones(2) + rand(2);    Cube = HyperCube(-2ones(2), 18ones(2))
 
-    @test norm(InformationGeometry.minimize(F, initial, Cube; tol=1e-5, meth=NelderMead())) < 5e-1
-    @test norm(InformationGeometry.minimize(F, initial, Cube; tol=1e-5, meth=LBFGS())) < 5e-2
-    @test norm(InformationGeometry.minimize(F, initial, Cube; tol=1e-5, meth=Newton())) < 5e-2
+    @test norm(minimize(F, initial, Cube; tol=1e-5, meth=NelderMead())) < 5e-1
+    @test norm(minimize(F, initial, Cube; tol=1e-5, meth=LBFGS())) < 5e-2
+    @test norm(minimize(F, initial, Cube; tol=1e-5, meth=Newton())) < 5e-2
 
-    @test norm(InformationGeometry.minimize(F, initial; tol=1e-5, meth=NelderMead())) < 5e-1
-    @test norm(InformationGeometry.minimize(F, initial; tol=1e-5, meth=LBFGS())) < 5e-2
-    @test norm(InformationGeometry.minimize(F, initial; tol=1e-5, meth=Newton())) < 5e-2
+    @test norm(minimize(F, initial; tol=1e-5, meth=NelderMead())) < 5e-1
+    @test norm(minimize(F, initial; tol=1e-5, meth=LBFGS())) < 5e-2
+    @test norm(minimize(F, initial; tol=1e-5, meth=Newton())) < 5e-2
+
+    # Check optimization with non-linear constraints and box constraints
+
+    # Check in-place and out-of-place optimization
 
     # Check type stability of optimization
     using ComponentArrays
-    @test InformationGeometry.minimize(X->X.A[1]^2 + 0.5X.B[1]^4, ComponentVector(A=[initial[1]], B=[initial[1]]); tol=1e-5, meth=Newton()) isa ComponentVector
+    @test minimize(X->X.A[1]^2 + 0.5X.B[1]^4, ComponentVector(A=[initial[1]], B=[initial[1]]); tol=1e-5, meth=Newton()) isa ComponentVector
 end