Merge branch 'master' into NewBranin

.github/dependabot.yml

@@ -5,3 +5,6 @@ updates:
     directory: "/" # Location of package manifests
     schedule:
       interval: "weekly"
+    ignore:
+      - dependency-name: "crate-ci/typos"
+        update-types: ["version-update:semver-patch"]

.github/workflows/CI.yml

@@ -15,7 +15,6 @@ jobs:
           - Core
         version:
           - '1'
-          - '1.6'
     steps:
       - uses: actions/checkout@v4
       - uses: julia-actions/setup-julia@v1

.github/workflows/Documentation.yml

@@ -16,7 +16,15 @@ jobs:
         with:
           version: '1'
       - name: Install dependencies
-        run: julia --project=docs/ -e 'using Pkg; Pkg.develop(PackageSpec(path=pwd())); Pkg.instantiate()'
+        run: julia --project=docs/ -e 'using Pkg; 
+                                      Pkg.develop(PackageSpec(path=pwd()));
+                                      Pkg.develop(PackageSpec(path=joinpath(pwd(), "lib", "SurrogatesAbstractGPs")));
+                                      Pkg.develop(PackageSpec(path=joinpath(pwd(), "lib", "SurrogatesFlux")));
+                                      Pkg.develop(PackageSpec(path=joinpath(pwd(), "lib", "SurrogatesMOE")));
+                                      Pkg.develop(PackageSpec(path=joinpath(pwd(), "lib", "SurrogatesPolyChaos")));
+                                      Pkg.develop(PackageSpec(path=joinpath(pwd(), "lib", "SurrogatesRandomForest")));
+                                      Pkg.develop(PackageSpec(path=joinpath(pwd(), "lib", "SurrogatesSVM")));
+                                      Pkg.instantiate()'
       - name: Build and deploy
         env:
           GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }} # For authentication with GitHub Actions token

.github/workflows/SpellCheck.yml

@@ -0,0 +1,13 @@
+name: Spell Check
+
+on: [pull_request]
+
+jobs:
+  typos-check:
+    name: Spell Check with Typos
+    runs-on: ubuntu-latest
+    steps:
+      - name: Checkout Actions Repository
+        uses: actions/checkout@v4
+      - name: Check spelling
+        uses: crate-ci/[email protected] 

.typos.toml

@@ -0,0 +1,2 @@
+[default.extend-words]
+ND = "ND"

Project.toml

@@ -1,7 +1,7 @@
 name = "Surrogates"
 uuid = "6fc51010-71bc-11e9-0e15-a3fcc6593c49"
 authors = ["SciML"]
-version = "6.6.0"
+version = "6.8.0"
 
 [deps]
 Distributions = "31c24e10-a181-5473-b8eb-7969acd0382f"
@@ -21,10 +21,10 @@ Flux = "0.12, 0.13"
 GLM = "1.3"
 IterativeSolvers = "0.9"
 PolyChaos = "0.2"
-QuasiMonteCarlo = "=0.2.16"
+QuasiMonteCarlo = "0.3"
 Statistics = "1"
 Zygote = "0.4, 0.5, 0.6"
-julia = "1.6"
+julia = "1.9"
 
 [extras]
 Cubature = "667455a9-e2ce-5579-9412-b964f529a492"

docs/Project.toml

@@ -15,7 +15,7 @@ Zygote = "e88e6eb3-aa80-5325-afca-941959d7151f"
 
 [compat]
 AbstractGPs = "0.5.13"
-Documenter = "0.27"
+Documenter = "1"
 Flux = "0.13.7, 0.14"
 Plots = "1.36.2"
 QuadGK = "2.6.0"

docs/make.jl

@@ -10,17 +10,11 @@ using Plots
 include("pages.jl")
 
 makedocs(sitename = "Surrogates.jl",
-         strict = [
-             :doctest,
-             :linkcheck,
-             :parse_error,
-             :example_block,
-             # Other available options are
-             # :autodocs_block, :cross_references, :docs_block, :eval_block, :example_block, :footnote, :meta_block, :missing_docs, :setup_block
-         ],
-         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")

docs/pages.jl

@@ -1,40 +1,40 @@
 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",
-             "Improved Branin function" => "ImprovedBraninFunction.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",
+    "Improved Branin function" => "ImprovedBraninFunction.md",
+    "Ackley function" => "ackley.md",
+    "Gramacy & Lee Function" => "gramacylee.md",
+    "Salustowicz Benchmark" => "Salustowicz.md",
+    "Multi objective optimization" => "multi_objective_opt.md",
+]]

docs/src/InverseDistance.md

@@ -15,15 +15,15 @@ default()
 
 ### Sampling
 
-We choose to sample f in 25 points between 0 and 10 using the `sample` function. The sampling points are chosen using a Low Discrepancy, this can be done by passing `LowDiscrepancySample()` to the `sample` function.
+We choose to sample f in 25 points between 0 and 10 using the `sample` function. The sampling points are chosen using a Low Discrepancy, this can be done by passing `HaltonSample()` to the `sample` function.
 
 ```@example Inverse_Distance1D
 f(x) = sin(x) + sin(x)^2 + sin(x)^3
 
 n_samples = 25
 lower_bound = 0.0
 upper_bound = 10.0
-x = sample(n_samples, lower_bound, upper_bound, LowDiscrepancySample(;base=2))
+x = sample(n_samples, lower_bound, upper_bound, HaltonSample())
 y = f.(x)
 
 scatter(x, y, label="Sampled points", xlims=(lower_bound, upper_bound), legend=:top)

docs/src/ackley.md

@@ -58,7 +58,7 @@ The fit looks good. Let's now see if we are able to find the minimum value using
 optimization methods:
 
 ```@example ackley
-surrogate_optimize(ackley,DYCORS(),lb,ub,my_rad,UniformSample())
+surrogate_optimize(ackley,DYCORS(),lb,ub,my_rad,RandomSample())
 scatter(x, y, label="Sampled points", xlims=(lb, ub), ylims=(0, 30), legend=:top)
 plot!(xs, ackley.(xs), label="True function", legend=:top)
 plot!(xs, my_rad.(xs), label="Radial basis optimized", legend=:top)

docs/src/gekpls.md

@@ -80,7 +80,7 @@ This next example demonstrates how this can be accomplished.
     y = sphere_function.(x)
     g = GEKPLS(x, y, grads, n_comp, delta_x, lb, ub, extra_points, initial_theta)
     x_point, minima = surrogate_optimize(sphere_function, SRBF(), lb, ub, g,
-                                         UniformSample(); maxiters = 20,
+                                         RandomSample(); maxiters = 20,
                                          num_new_samples = 20, needs_gradient = true)
     println(minima)
 

docs/src/index.md

@@ -107,62 +107,65 @@ my_lobachevsky = LobachevskySurrogate(x,y,lb,ub,alpha=alpha,n=n)
 value = my_lobachevsky(5.0)
 
 #Adding more data points
-surrogate_optimize(f,SRBF(),lb,ub,my_lobachevsky,UniformSample())
+surrogate_optimize(f,SRBF(),lb,ub,my_lobachevsky,RandomSample())
 
 #New approximation
 value = my_lobachevsky(5.0)
 ```
 ## Reproducibility
+
 ```@raw html
 <details><summary>The documentation of this SciML package was built using these direct dependencies,</summary>
 ```
+
 ```@example
 using Pkg # hide
 Pkg.status() # hide
 ```
+
 ```@raw html
 </details>
 ```
+
 ```@raw html
 <details><summary>and using this machine and Julia version.</summary>
 ```
+
 ```@example
 using InteractiveUtils # hide
 versioninfo() # hide
 ```
+
 ```@raw html
 </details>
 ```
+
 ```@raw html
 <details><summary>A more complete overview of all dependencies and their versions is also provided.</summary>
 ```
+
 ```@example
 using Pkg # hide
-Pkg.status(;mode = PKGMODE_MANIFEST) # hide
+Pkg.status(; mode = PKGMODE_MANIFEST) # hide
 ```
+
 ```@raw html
 </details>
 ```
-```@raw html
-You can also download the 
-<a href="
-```
-```@eval
-using TOML
-version = TOML.parse(read("../../Project.toml",String))["version"]
-name = TOML.parse(read("../../Project.toml",String))["name"]
-link = "https://github.com/SciML/"*name*".jl/tree/gh-pages/v"*version*"/assets/Manifest.toml"
-```
-```@raw html
-">manifest</a> file and the
-<a href="
-```
+
 ```@eval
 using TOML
-version = TOML.parse(read("../../Project.toml",String))["version"]
-name = TOML.parse(read("../../Project.toml",String))["name"]
-link = "https://github.com/SciML/"*name*".jl/tree/gh-pages/v"*version*"/assets/Project.toml"
-```
-```@raw html
-">project</a> file.
+using Markdown
+version = TOML.parse(read("../../Project.toml", String))["version"]
+name = TOML.parse(read("../../Project.toml", String))["name"]
+link_manifest = "https://github.com/SciML/" * name * ".jl/tree/gh-pages/v" * version *
+                "/assets/Manifest.toml"
+link_project = "https://github.com/SciML/" * name * ".jl/tree/gh-pages/v" * version *
+               "/assets/Project.toml"
+Markdown.parse("""You can also download the
+[manifest]($link_manifest)
+file and the
+[project]($link_project)
+file.
+""")
 ```

docs/src/moe.md

@@ -92,7 +92,7 @@ end
 lb = [-1.0, -1.0]
 ub = [1.0, 1.0]
 n = 150
-x = sample(n, lb, ub, SobolSample())
+x = sample(n, lb, ub, RandomSample())
 y = discont_NDIM.(x)
 x_test = sample(10, lb, ub, GoldenSample())
 
@@ -110,7 +110,6 @@ rbf = RadialBasis(x, y, lb, ub)
 rbf_pred_vals = rbf.(x_test)
 rbf_rmse = rmse(true_vals, rbf_pred_vals)
 println(rbf_rmse > moe_rmse)
-
 ```
 
 ### Usage Notes - Example With Other Surrogates

docs/src/optimizations.md

@@ -28,5 +28,5 @@ surrogate_optimize(obj::Function,sop1::SOP,lb::Number,ub::Number,surrSOP::Abstra
 To add another optimization method, you just need to define a new
 SurrogateOptimizationAlgorithm and write its corresponding algorithm, overloading the following:
 ```
-surrogate_optimize(obj::Function,::NewOptimizatonType,lb,ub,surr::AbstractSurrogate,sample_type::SamplingAlgorithm;maxiters=100,num_new_samples=100)
+surrogate_optimize(obj::Function,::NewOptimizationType,lb,ub,surr::AbstractSurrogate,sample_type::SamplingAlgorithm;maxiters=100,num_new_samples=100)
 ```

docs/src/parallel.md

@@ -17,24 +17,24 @@ To ensure that points of interest returned by `potential_optimal_points` are suf
 
 The following strategies are available for virtual point selection for all optimization algorithms:
 
-- "Minimum Constant Liar (CLmin)":
+- "Minimum Constant Liar (MinimumConstantLiar)":
   - The virtual point is assigned using the lowest known value of the merit function across all evaluated points.
-- "Mean Constant Liar (CLmean)":
+- "Mean Constant Liar (MeanConstantLiar)":
   - The virtual point is assigned using the mean of the merit function across all evaluated points.
-- "Maximum Constant Liar (CLmax)":
+- "Maximum Constant Liar (MaximumConstantLiar)":
   - The virtual point is assigned using the great known value of the merit function across all evaluated points.
 
 For Kriging surrogates, specifically, the above and follow strategies are available:  
 
-- "Kriging Believer (KB)":
+- "Kriging Believer (KrigingBeliever):
   - The virtual point is assigned using the mean of the Kriging surrogate at the virtual point.
-- "Kriging Believer Upper Bound (KBUB)":
+- "Kriging Believer Upper Bound (KrigingBelieverUpperBound)":
   - The virtual point is assigned using 3$\sigma$ above the mean of the Kriging surrogate at the virtual point.
-- "Kriging Believer Lower Bound (KBLB)":
+- "Kriging Believer Lower Bound (KrigingBelieverLowerBound)":
   - The virtual point is assigned using 3$\sigma$ below the mean of the Kriging surrogate at the virtual point.
 
 
-In general, CLmin and KBLB tend to favor exploitation while CLmax and KBUB tend to favor exploration. CLmean and KB tend to be a compromise between the two.
+In general, MinimumConstantLiar and KrigingBelieverLowerBound tend to favor exploitation while MaximumConstantLiar and KrigingBelieverUpperBound tend to favor exploration. MeanConstantLiar and KrigingBeliever tend to be a compromise between the two.
 
 ## Examples
 
@@ -50,7 +50,7 @@ y = f.(x)
 my_k = Kriging(x, y, lb, ub)
 
 for _ in 1:10
-    new_x, eis = potential_optimal_points(EI(), lb, ub, my_k, SobolSample(), 3, CLmean!)
+    new_x, eis = potential_optimal_points(EI(), MeanConstantLiar(), lb, ub, my_k, SobolSample(), 3)
     add_point!(my_k, new_x, f.(new_x))
 end
 ```

docs/src/polychaos.md

@@ -9,7 +9,7 @@ we are trying to fit. Under the hood, PolyChaos.jl has been used.
 It is possible to specify a type of polynomial for each dimension of the problem.
 ### Sampling
 
-We choose to sample f in 25 points between 0 and 10 using the `sample` function. The sampling points are chosen using a Low Discrepancy, this can be done by passing `LowDiscrepancySample()` to the `sample` function.
+We choose to sample f in 25 points between 0 and 10 using the `sample` function. The sampling points are chosen using a Low Discrepancy, this can be done by passing `HaltonSample()` to the `sample` function.
 
 ```@example polychaos
 using Surrogates
@@ -20,7 +20,7 @@ default()
 n = 20
 lower_bound = 1.0
 upper_bound = 6.0
-x = sample(n,lower_bound,upper_bound,LowDiscrepancySample(2))
+x = sample(n,lower_bound,upper_bound,HaltonSample())
 f = x -> log(x)*x + sin(x)
 y = f.(x)
 scatter(x, y, label="Sampled points", xlims=(lower_bound, upper_bound), legend=:top)