chore: format the repo

SciML · Dec 13, 2023 · 9224fd8 · 9224fd8
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commit 9224fd8
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diff --git a/docs/make.jl b/docs/make.jl
@@ -10,11 +10,11 @@ using Plots
 include("pages.jl")
 
 makedocs(sitename = "Surrogates.jl",
-         linkcheck = true,
-         warnonly = [:missing_docs],
-         format = Documenter.HTML(analytics = "UA-90474609-3",
-                                  assets = ["assets/favicon.ico"],
-                                  canonical = "https://docs.sciml.ai/Surrogates/stable/"),
-         pages = pages)
+    linkcheck = true,
+    warnonly = [:missing_docs],
+    format = Documenter.HTML(analytics = "UA-90474609-3",
+        assets = ["assets/favicon.ico"],
+        canonical = "https://docs.sciml.ai/Surrogates/stable/"),
+    pages = pages)
 
 deploydocs(repo = "github.com/SciML/Surrogates.jl.git")
diff --git a/docs/pages.jl b/docs/pages.jl
@@ -1,39 +1,39 @@
 pages = ["index.md"
-         "Tutorials" => [
-             "Basics" => "tutorials.md",
-             "Radials" => "radials.md",
-             "Kriging" => "kriging.md",
-             "Gaussian Process" => "abstractgps.md",
-             "Lobachevsky" => "lobachevsky.md",
-             "Linear" => "LinearSurrogate.md",
-             "InverseDistance" => "InverseDistance.md",
-             "RandomForest" => "randomforest.md",
-             "SecondOrderPolynomial" => "secondorderpoly.md",
-             "NeuralSurrogate" => "neural.md",
-             "Wendland" => "wendland.md",
-             "Polynomial Chaos" => "polychaos.md",
-             "Variable Fidelity" => "variablefidelity.md",
-             "Gradient Enhanced Kriging" => "gek.md",
-             "GEKPLS" => "gekpls.md",
-             "MOE" => "moe.md",
-             "Parallel Optimization" => "parallel.md"
-         ]
-         "User guide" => [
-             "Samples" => "samples.md",
-             "Surrogates" => "surrogate.md",
-             "Optimization" => "optimizations.md",
-         ]
-         "Benchmarks" => [
-             "Sphere function" => "sphere_function.md",
-             "Lp norm" => "lp.md",
-             "Rosenbrock" => "rosenbrock.md",
-             "Tensor product" => "tensor_prod.md",
-             "Cantilever beam" => "cantilever.md",
-             "Water Flow function" => "water_flow.md",
-             "Welded beam function" => "welded_beam.md",
-             "Branin function" => "BraninFunction.md",
-             "Ackley function" => "ackley.md",
-             "Gramacy & Lee Function" => "gramacylee.md",
-             "Salustowicz Benchmark" => "Salustowicz.md",
-             "Multi objective optimization" => "multi_objective_opt.md",
-         ]]
+    "Tutorials" => [
+    "Basics" => "tutorials.md",
+    "Radials" => "radials.md",
+    "Kriging" => "kriging.md",
+    "Gaussian Process" => "abstractgps.md",
+    "Lobachevsky" => "lobachevsky.md",
+    "Linear" => "LinearSurrogate.md",
+    "InverseDistance" => "InverseDistance.md",
+    "RandomForest" => "randomforest.md",
+    "SecondOrderPolynomial" => "secondorderpoly.md",
+    "NeuralSurrogate" => "neural.md",
+    "Wendland" => "wendland.md",
+    "Polynomial Chaos" => "polychaos.md",
+    "Variable Fidelity" => "variablefidelity.md",
+    "Gradient Enhanced Kriging" => "gek.md",
+    "GEKPLS" => "gekpls.md",
+    "MOE" => "moe.md",
+    "Parallel Optimization" => "parallel.md",
+]
+    "User guide" => [
+    "Samples" => "samples.md",
+    "Surrogates" => "surrogate.md",
+    "Optimization" => "optimizations.md",
+]
+    "Benchmarks" => [
+    "Sphere function" => "sphere_function.md",
+    "Lp norm" => "lp.md",
+    "Rosenbrock" => "rosenbrock.md",
+    "Tensor product" => "tensor_prod.md",
+    "Cantilever beam" => "cantilever.md",
+    "Water Flow function" => "water_flow.md",
+    "Welded beam function" => "welded_beam.md",
+    "Branin function" => "BraninFunction.md",
+    "Ackley function" => "ackley.md",
+    "Gramacy & Lee Function" => "gramacylee.md",
+    "Salustowicz Benchmark" => "Salustowicz.md",
+    "Multi objective optimization" => "multi_objective_opt.md",
+]]
diff --git a/lib/SurrogatesAbstractGPs/test/runtests.jl b/lib/SurrogatesAbstractGPs/test/runtests.jl
@@ -27,7 +27,7 @@ using Surrogates: sample, SobolSample
         x_points = sample(5, lb, ub, SobolSample())
         y_points = f.(x_points)
         agp1D = AbstractGPSurrogate([x_points[1]], [y_points[1]],
-                                    gp = GP(SqExponentialKernel()), Σy = 0.05)
+            gp = GP(SqExponentialKernel()), Σy = 0.05)
         x_new = 2.5
         y_actual = f.(x_new)
         for i in 2:length(x_points)
@@ -88,7 +88,7 @@ using Surrogates: sample, SobolSample
         b = 6
         my_k_EI1 = AbstractGPSurrogate(x, y)
         surrogate_optimize(objective_function, EI(), a, b, my_k_EI1, RandomSample(),
-                           maxiters = 200, num_new_samples = 155)
+            maxiters = 200, num_new_samples = 155)
     end
 
     @testset "Optimization ND" begin

diff --git a/lib/SurrogatesFlux/src/SurrogatesFlux.jl b/lib/SurrogatesFlux/src/SurrogatesFlux.jl
@@ -26,8 +26,8 @@ NeuralSurrogate(x,y,lb,ub,model,loss,opt,n_echos)
 
 """
 function NeuralSurrogate(x, y, lb, ub; model = Chain(Dense(length(x[1]), 1), first),
-                         loss = (x, y) -> Flux.mse(model(x), y), opt = Descent(0.01),
-                         n_echos::Int = 1)
+        loss = (x, y) -> Flux.mse(model(x), y), opt = Descent(0.01),
+        n_echos::Int = 1)
     X = vec.(collect.(x))
     data = zip(X, y)
     ps = Flux.params(model)
@@ -59,7 +59,7 @@ function add_point!(my_n::NeuralSurrogate, x_new, y_new)
     end
     X = vec.(collect.(my_n.x))
     data = zip(X, my_n.y)
-    for epoch in 1:my_n.n_echos
+    for epoch in 1:(my_n.n_echos)
         Flux.train!(my_n.loss, my_n.ps, data, my_n.opt)
     end
     nothing

diff --git a/lib/SurrogatesFlux/test/runtests.jl b/lib/SurrogatesFlux/test/runtests.jl
@@ -19,7 +19,7 @@ using SafeTestsets
     my_opt = Descent(0.01)
     n_echos = 1
     my_neural = NeuralSurrogate(x, y, a, b, model = my_model, loss = my_loss, opt = my_opt,
-                                n_echos = 1)
+        n_echos = 1)
     my_neural_kwargs = NeuralSurrogate(x, y, a, b)
     add_point!(my_neural, 8.5, 20.0)
     add_point!(my_neural, [3.2, 3.5], [7.4, 8.0])
@@ -37,7 +37,7 @@ using SafeTestsets
     my_opt = Descent(0.01)
     n_echos = 1
     my_neural = NeuralSurrogate(x, y, lb, ub, model = my_model, loss = my_loss,
-                                opt = my_opt, n_echos = 1)
+        opt = my_opt, n_echos = 1)
     my_neural_kwargs = NeuralSurrogate(x, y, lb, ub)
     my_neural((3.5, 1.49))
     my_neural([3.4, 1.4])
@@ -54,7 +54,7 @@ using SafeTestsets
     my_model = Chain(Dense(1, 2))
     my_loss(x, y) = Flux.mse(my_model(x), y)
     surrogate = NeuralSurrogate(x, y, lb, ub, model = my_model, loss = my_loss,
-                                opt = my_opt, n_echos = 1)
+        opt = my_opt, n_echos = 1)
     surr_kwargs = NeuralSurrogate(x, y, lb, ub)
 
     f = x -> [x[1], x[2]^2]
@@ -66,7 +66,7 @@ using SafeTestsets
     my_model = Chain(Dense(2, 2))
     my_loss(x, y) = Flux.mse(my_model(x), y)
     surrogate = NeuralSurrogate(x, y, lb, ub, model = my_model, loss = my_loss,
-                                opt = my_opt, n_echos = 1)
+        opt = my_opt, n_echos = 1)
     surrogate_kwargs = NeuralSurrogate(x, y, lb, ub)
     surrogate((1.0, 2.0))
     x_new = (2.0, 2.0)
@@ -85,7 +85,7 @@ using SafeTestsets
     n_echos = 1
     my_neural_ND_neural = NeuralSurrogate(x, y, lb, ub)
     surrogate_optimize(objective_function_ND, SRBF(), lb, ub, my_neural_ND_neural,
-                       SobolSample(), maxiters = 15)
+        SobolSample(), maxiters = 15)
 
     # AD Compatibility
     lb = 0.0
@@ -101,7 +101,7 @@ using SafeTestsets
         my_opt = Descent(0.01)
         n_echos = 1
         my_neural = NeuralSurrogate(x, y, lb, ub, model = my_model, loss = my_loss,
-                                    opt = my_opt, n_echos = 1)
+            opt = my_opt, n_echos = 1)
         g = x -> my_neural'(x)
         g(3.4)
     end
@@ -120,7 +120,7 @@ using SafeTestsets
         my_opt = Descent(0.01)
         n_echos = 1
         my_neural = NeuralSurrogate(x, y, lb, ub, model = my_model, loss = my_loss,
-                                    opt = my_opt, n_echos = 1)
+            opt = my_opt, n_echos = 1)
         g = x -> Zygote.gradient(my_neural, x)
         g((2.0, 5.0))
     end
@@ -141,7 +141,7 @@ using SafeTestsets
         my_opt = Descent(0.01)
         n_echos = 1
         my_neural = NeuralSurrogate(x, y, lb, ub, model = my_model, loss = my_loss,
-                                    opt = my_opt, n_echos = 1)
+            opt = my_opt, n_echos = 1)
         Zygote.gradient(x -> sum(my_neural(x)), (2.0, 5.0))
 
         my_rad = RadialBasis(x, y, lb, ub, rad = linearRadial())

diff --git a/lib/SurrogatesMOE/X-QMC2.csv b/lib/SurrogatesMOE/X-QMC2.csv
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