Fix mse for least squares PCE (#128)

Author: andreaperin

Project.toml

@@ -1,7 +1,7 @@
 name = "UncertaintyQuantification"
 uuid = "7183a548-a887-11e9-15ce-a56ab60bad7a"
 authors = ["Jasper Behrensdorf <behrensdorf@irz.uni-hannover.de>", "Ander Gray <ander.gray@liverpool.ac.uk>"]
-version = "0.7.1"
+version = "0.7.2"
 
 [deps]
 Bootstrap = "e28b5b4c-05e8-5b66-bc03-6f0c0a0a06e0"

src/models/pce/polynomialchaosexpansion.jl

@@ -31,7 +31,7 @@ function polynomialchaos(
     y = A \ samples[:, output]
 
     ϵ = samples[:, output] - A * y
-    mse = dot(ϵ, ϵ)
+    mse = mean(ϵ .^ 2)
 
     to_physical_space!(random_inputs, samples)
 

test/models/pce/polynomialchaosexpansion.jl

@@ -16,10 +16,15 @@
 
     @testset "LeastSquares" begin
         ls = LeastSquares(SobolSampling(1000))
-        pce, _, _ = polynomialchaos(x, model, Ψ, :y, ls)
+        pce, samples, mse = polynomialchaos(x, model, Ψ, :y, ls)
+
+        new_samples = samples[:, Not(:y1, :y)]
+        evaluate!(pce, new_samples)
+        ϵ = mean((new_samples.y .- samples.y) .^ 2)
 
         @test mean(pce) ≈ -1.5 rtol = 1e-10
         @test var(pce) ≈ 0.5 rtol = 1e-10
+        @test mse ≈ ϵ atol = eps()
     end
 
     @testset "GaussQuadrature" begin