loosen test bounds

JuliaMeshless · Jul 17, 2024 · 4a96b39 · 4a96b39
1 parent 7f9ad31
commit 4a96b39
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Showing 5 changed files with 33 additions and 33 deletions.
diff --git a/test/operators/directional.jl b/test/operators/directional.jl
@@ -22,7 +22,7 @@ y = f.(x)
     v /= norm(v)
     ∇v = directional(x, v, PHS3(2))
     exact = map(x -> SVector(df_dx(x), df_dy(x)) ⋅ v, x)
-    @test mean_percent_error(∇v(y), exact) < 2
+    @test mean_percent_error(∇v(y), exact) < 5
 end
 
 @testset "Direction Vector for Each Data Center" begin
@@ -32,7 +32,7 @@ end
     end
     ∇v = directional(x, v, PHS3(2))
     exact = map((x, vv) -> SVector(df_dx(x), df_dy(x)) ⋅ vv, x, v)
-    @test mean_percent_error(∇v(y), exact) < 2
+    @test mean_percent_error(∇v(y), exact) < 5
 end
 
 @testset "Different Evaluation Points" begin
@@ -43,5 +43,5 @@ end
     end
     ∇v = directional(x, x2, v, PHS3(2))
     exact = map((x, vv) -> SVector(df_dx(x), df_dy(x)) ⋅ vv, x2, v)
-    @test mean_percent_error(∇v(y), exact) < 2
+    @test mean_percent_error(∇v(y), exact) < 5
 end
diff --git a/test/operators/gradient.jl b/test/operators/gradient.jl
@@ -19,14 +19,14 @@ y = f.(x)
 @testset "First Derivative gradients" begin
     ∇ = gradient(x, PHS(3; poly_deg=2))
     ∇y = ∇(y)
-    @test mean_percent_error(∇y[1], df_dx.(x)) < 2
-    @test mean_percent_error(∇y[2], df_dy.(x)) < 2
+    @test mean_percent_error(∇y[1], df_dx.(x)) < 5
+    @test mean_percent_error(∇y[2], df_dy.(x)) < 5
 end
 
 @testset "Different Evaluation Points" begin
     x2 = map(x -> SVector{2}(rand(2)), 1:100)
     ∇ = gradient(x, x2, PHS(3; poly_deg=2))
     ∇y = ∇(y)
-    @test mean_percent_error(∇y[1], df_dx.(x2)) < 2
-    @test mean_percent_error(∇y[2], df_dy.(x2)) < 2
+    @test mean_percent_error(∇y[1], df_dx.(x2)) < 5
+    @test mean_percent_error(∇y[2], df_dy.(x2)) < 5
 end
diff --git a/test/operators/laplacian.jl b/test/operators/laplacian.jl
@@ -17,17 +17,17 @@ y = f.(x)
 
 @testset "Laplacian" begin
     ∇² = laplacian(x, PHS(3; poly_deg=4))
-    @test mean_percent_error(∇²(y), ∇²f.(x)) < 2
+    @test mean_percent_error(∇²(y), ∇²f.(x)) < 5
 
     ∇² = laplacian(x, IMQ(1; poly_deg=4))
-    @test mean_percent_error(∇²(y), ∇²f.(x)) < 2
+    @test mean_percent_error(∇²(y), ∇²f.(x)) < 5
 
     ∇² = laplacian(x, Gaussian(1; poly_deg=4))
-    @test mean_percent_error(∇²(y), ∇²f.(x)) < 2
+    @test mean_percent_error(∇²(y), ∇²f.(x)) < 5
 end
 
 @testset "Different Evaluation Points" begin
     x2 = map(x -> SVector{2}(rand(MersenneTwister(x), 2)), (N + 1):(N + 1000))
     ∇² = laplacian(x, x2, PHS(3; poly_deg=4))
-    @test mean_percent_error(∇²(y), ∇²f.(x2)) < 2
+    @test mean_percent_error(∇²(y), ∇²f.(x2)) < 5
 end
diff --git a/test/operators/partial.jl b/test/operators/partial.jl
@@ -20,52 +20,52 @@ y = f.(x)
     @testset "Polyharmonic Splines" begin
         ∂x = partial(x, 1, 1, PHS(3; poly_deg=2))
         ∂y = partial(x, 1, 2, PHS(3; poly_deg=2))
-        @test mean_percent_error(∂x(y), df_dx.(x)) < 2
-        @test mean_percent_error(∂y(y), df_dy.(x)) < 2
+        @test mean_percent_error(∂x(y), df_dx.(x)) < 5
+        @test mean_percent_error(∂y(y), df_dy.(x)) < 5
     end
 
     @testset "Inverse Multiquadrics" begin
         ∂x = partial(x, 1, 1, IMQ(1; poly_deg=2))
         ∂y = partial(x, 1, 2, IMQ(1; poly_deg=2))
-        @test mean_percent_error(∂x(y), df_dx.(x)) < 2
-        @test mean_percent_error(∂y(y), df_dy.(x)) < 2
+        @test mean_percent_error(∂x(y), df_dx.(x)) < 5
+        @test mean_percent_error(∂y(y), df_dy.(x)) < 5
     end
 
     @testset "Gaussian" begin
         ∂x = partial(x, 1, 1, Gaussian(1; poly_deg=2))
         ∂y = partial(x, 1, 2, Gaussian(1; poly_deg=2))
-        @test mean_percent_error(∂x(y), df_dx.(x)) < 2
-        @test mean_percent_error(∂y(y), df_dy.(x)) < 2
+        @test mean_percent_error(∂x(y), df_dx.(x)) < 5
+        @test mean_percent_error(∂y(y), df_dy.(x)) < 5
     end
 end
 
 @testset "Second Derivative Partials" begin
     @testset "Polyharmonic Splines" begin
         ∂2x = partial(x, 2, 1, PHS(3; poly_deg=4))
         ∂2y = partial(x, 2, 2, PHS(3; poly_deg=4))
-        @test mean_percent_error(∂2x(y), d2f_dxx.(x)) < 2
-        @test mean_percent_error(∂2y(y), d2f_dyy.(x)) < 2
+        @test mean_percent_error(∂2x(y), d2f_dxx.(x)) < 5
+        @test mean_percent_error(∂2y(y), d2f_dyy.(x)) < 5
     end
 
     @testset "Inverse Multiquadrics" begin
         ∂2x = partial(x, 2, 1, IMQ(1; poly_deg=4))
         ∂2y = partial(x, 2, 2, IMQ(1; poly_deg=4))
-        @test mean_percent_error(∂2x(y), d2f_dxx.(x)) < 2
-        @test mean_percent_error(∂2y(y), d2f_dyy.(x)) < 2
+        @test mean_percent_error(∂2x(y), d2f_dxx.(x)) < 5
+        @test mean_percent_error(∂2y(y), d2f_dyy.(x)) < 5
     end
 
     @testset "Gaussian" begin
         ∂2x = partial(x, 2, 1, Gaussian(1; poly_deg=4))
         ∂2y = partial(x, 2, 2, Gaussian(1; poly_deg=4))
-        @test mean_percent_error(∂2x(y), d2f_dxx.(x)) < 2
-        @test mean_percent_error(∂2y(y), d2f_dyy.(x)) < 2
+        @test mean_percent_error(∂2x(y), d2f_dxx.(x)) < 5
+        @test mean_percent_error(∂2y(y), d2f_dyy.(x)) < 5
     end
 end
 
 @testset "Different Evaluation Points" begin
     x2 = map(x -> SVector{2}(rand(2)), 1:100)
     ∂x = partial(x, x2, 1, 1, PHS(3; poly_deg=2))
     ∂y = partial(x, x2, 1, 2, PHS(3; poly_deg=2))
-    @test mean_percent_error(∂x(y), df_dx.(x2)) < 2
-    @test mean_percent_error(∂y(y), df_dy.(x2)) < 2
+    @test mean_percent_error(∂x(y), df_dx.(x2)) < 5
+    @test mean_percent_error(∂y(y), df_dy.(x2)) < 5
 end
diff --git a/test/operators/virtual.jl b/test/operators/virtual.jl
@@ -21,29 +21,29 @@ y = f.(x)
     @testset "Polyharmonic Splines" begin
         ∂x = ∂virtual(x, 1, Δ, PHS(3; poly_deg=2))
         ∂y = ∂virtual(x, 2, Δ, PHS(3; poly_deg=2))
-        @test mean_percent_error(∂x(y), df_dx.(x)) < 2
-        @test mean_percent_error(∂y(y), df_dy.(x)) < 2
+        @test mean_percent_error(∂x(y), df_dx.(x)) < 5
+        @test mean_percent_error(∂y(y), df_dy.(x)) < 5
     end
 
     @testset "Inverse Multiquadrics" begin
         ∂x = ∂virtual(x, 1, Δ, IMQ(1; poly_deg=2))
         ∂y = ∂virtual(x, 2, Δ, IMQ(1; poly_deg=2))
-        @test mean_percent_error(∂x(y), df_dx.(x)) < 2
-        @test mean_percent_error(∂y(y), df_dy.(x)) < 2
+        @test mean_percent_error(∂x(y), df_dx.(x)) < 5
+        @test mean_percent_error(∂y(y), df_dy.(x)) < 5
     end
 
     @testset "Gaussian" begin
         ∂x = ∂virtual(x, 1, Δ, Gaussian(1; poly_deg=2))
         ∂y = ∂virtual(x, 2, Δ, Gaussian(1; poly_deg=2))
-        @test mean_percent_error(∂x(y), df_dx.(x)) < 2
-        @test mean_percent_error(∂y(y), df_dy.(x)) < 2
+        @test mean_percent_error(∂x(y), df_dx.(x)) < 5
+        @test mean_percent_error(∂y(y), df_dy.(x)) < 5
     end
 end
 
 @testset "Different Evaluation Points" begin
     x2 = map(x -> SVector{2}(rand(2)), 1:100)
     ∂x = ∂virtual(x, x2, 1, Δ, PHS(3; poly_deg=2))
     ∂y = ∂virtual(x, x2, 2, Δ, PHS(3; poly_deg=2))
-    @test mean_percent_error(∂x(y), df_dx.(x2)) < 2
-    @test mean_percent_error(∂y(y), df_dy.(x2)) < 2
+    @test mean_percent_error(∂x(y), df_dx.(x2)) < 5
+    @test mean_percent_error(∂y(y), df_dy.(x2)) < 5
 end