From 643328d9ce0928f85a0f451cc039b6baad0d3fa2 Mon Sep 17 00:00:00 2001
From: Simone Carlo Surace <simsurace@gmx.net>
Date: Tue, 5 Mar 2024 13:39:19 +0100
Subject: [PATCH 1/5] Add formatter

---
 .JuliaFormatter              |  1 +
 .github/workflows/Format.yml | 31 +++++++++++++++++++++++++++++++
 2 files changed, 32 insertions(+)
 create mode 100644 .JuliaFormatter
 create mode 100644 .github/workflows/Format.yml

diff --git a/.JuliaFormatter b/.JuliaFormatter
new file mode 100644
index 0000000..c743950
--- /dev/null
+++ b/.JuliaFormatter
@@ -0,0 +1 @@
+style = "blue"
\ No newline at end of file
diff --git a/.github/workflows/Format.yml b/.github/workflows/Format.yml
new file mode 100644
index 0000000..ea33c9f
--- /dev/null
+++ b/.github/workflows/Format.yml
@@ -0,0 +1,31 @@
+name: Format suggestions
+
+on:
+  pull_request:
+  
+concurrency:
+  # Skip intermediate builds: always.
+  # Cancel intermediate builds: only if it is a pull request build.
+  group: ${{ github.workflow }}-${{ github.ref }}
+  cancel-in-progress: ${{ startsWith(github.ref, 'refs/pull/') }}
+
+jobs:
+  format:
+    runs-on: ubuntu-latest
+    permissions:
+      contents: read
+      checks: write
+      issues: write
+      pull-requests: write
+    steps:
+      - uses: actions/checkout@v4
+      - uses: julia-actions/setup-julia@v1
+        with:
+          version: 1
+      - run: |
+          julia  -e 'using Pkg; Pkg.add("JuliaFormatter")'
+          julia  -e 'using JuliaFormatter; format("."; verbose=true)'
+      - uses: reviewdog/action-suggester@v1
+        with:
+          tool_name: JuliaFormatter
+          fail_on_error: true

From c33484f86cfca8b67bb850766b59ca4a4aba28ab Mon Sep 17 00:00:00 2001
From: Simone Carlo Surace <simsurace@gmx.net>
Date: Wed, 6 Mar 2024 16:26:23 +0100
Subject: [PATCH 2/5] Run formatter

---
 examples/0-mauna-loa/script.jl |  4 ++--
 src/EasyGPs.jl                 | 34 +++++++++++++++++-----------------
 test/integration_tests.jl      | 22 +++++++++++-----------
 test/unit_tests.jl             | 10 +++++-----
 4 files changed, 35 insertions(+), 35 deletions(-)

diff --git a/examples/0-mauna-loa/script.jl b/examples/0-mauna-loa/script.jl
index bb99515..f07d7b3 100644
--- a/examples/0-mauna-loa/script.jl
+++ b/examples/0-mauna-loa/script.jl
@@ -43,7 +43,7 @@ plotdata()
 
 k_smooth_trend = exp(8.0) * with_lengthscale(SEKernel(), exp(4.0))#with_lengthscale(SEKernel(), exp(4.0))
 k_seasonality = exp(2.0) * PeriodicKernel(; r=[0.5]) *
-    with_lengthscale(SEKernel(), exp(4.0))
+                with_lengthscale(SEKernel(), exp(4.0))
 k_medium_term_irregularities = 1.0 * with_lengthscale(RationalQuadraticKernel(; α=exp(-1.0)), 1.0)
 k_noise_terms = exp(-4.0) * with_lengthscale(SEKernel(), exp(-2.0)) + exp(-4.0) * WhiteKernel()
 kernel = k_smooth_trend + k_seasonality + k_medium_term_irregularities + k_noise_terms
@@ -85,7 +85,7 @@ plot_gp!(fpost_init; label="posterior f(⋅)")
 
 @time fitted_gp = EasyGPs.fit(
     gp, xtrain, ytrain;
-    optimizer = Optim.LBFGS(;
+    optimizer=Optim.LBFGS(;
         alphaguess=Optim.LineSearches.InitialStatic(; scaled=true),
         linesearch=Optim.LineSearches.BackTracking(),
     )
diff --git a/src/EasyGPs.jl b/src/EasyGPs.jl
index 887d1fd..8c4c105 100644
--- a/src/EasyGPs.jl
+++ b/src/EasyGPs.jl
@@ -63,8 +63,8 @@ two arguments, the first of which is of type `typeof(model(θ0))`.
 """
 function optimize(
     model, θ0, data;
-    iterations = 1000,
-    optimizer = Optim.BFGS(),
+    iterations=1000,
+    optimizer=Optim.BFGS(),
     kwargs...
 )
     par0, unflatten = ParameterHandling.flatten(θ0)
@@ -73,7 +73,7 @@ function optimize(
         Optimization.AutoZygote()
     )
     prob = Optimization.OptimizationProblem(optf, par0, data)
-    sol = Optimization.solve(prob, optimizer; maxiters = iterations)
+    sol = Optimization.solve(prob, optimizer; maxiters=iterations)
     return unflatten(sol.u)
 end
 
@@ -83,7 +83,7 @@ end
 Check whether two things are equal for the purposes of this library. For this to be true,
 roughly speaking the objects must be of the same type and have the same parameters.
 """
-_isequal(::T1, ::T2) where {T1, T2} = false
+_isequal(::T1, ::T2) where {T1,T2} = false
 
 
 
@@ -100,20 +100,20 @@ _isequal(m1::ConstMean, m2::ConstMean) = isapprox(m1.c, m2.c)
 
 # Simple kernels
 KernelsWithoutParameters = Union{
-    SEKernel, Matern32Kernel, Matern52Kernel, WhiteKernel
+    SEKernel,Matern32Kernel,Matern52Kernel,WhiteKernel
 }
 
-extract_parameters(::T) where T <: KernelsWithoutParameters = nothing
-apply_parameters(k::T, θ) where T <: KernelsWithoutParameters = k
-_isequal(k1::T, k2::T) where T <: KernelsWithoutParameters = true
+extract_parameters(::T) where {T<:KernelsWithoutParameters} = nothing
+apply_parameters(k::T, θ) where {T<:KernelsWithoutParameters} = k
+_isequal(k1::T, k2::T) where {T<:KernelsWithoutParameters} = true
 
 extract_parameters(k::PeriodicKernel) = ParameterHandling.positive(only(k.r))
-apply_parameters(::PeriodicKernel, θ) = PeriodicKernel(r = [θ])
-_isequal(k1::T, k2::T) where T <: PeriodicKernel = k1.r ≈ k2.r
+apply_parameters(::PeriodicKernel, θ) = PeriodicKernel(r=[θ])
+_isequal(k1::T, k2::T) where {T<:PeriodicKernel} = k1.r ≈ k2.r
 
 extract_parameters(k::RationalQuadraticKernel) = ParameterHandling.positive(only(k.α))
-apply_parameters(k::RationalQuadraticKernel, θ) = RationalQuadraticKernel(; α = θ, metric = k.metric)
-_isequal(k1::T, k2::T) where T <: RationalQuadraticKernel = true
+apply_parameters(k::RationalQuadraticKernel, θ) = RationalQuadraticKernel(; α=θ, metric=k.metric)
+_isequal(k1::T, k2::T) where {T<:RationalQuadraticKernel} = true
 
 
 
@@ -168,11 +168,11 @@ _isequal(t1::ScaleTransform, t2::ScaleTransform) = isapprox(t1.s, t2.s)
 # Likelihoods
 extract_parameters(::BernoulliLikelihood) = nothing
 apply_parameters(l::BernoulliLikelihood, θ) = l
-_isequal(l1::T, l2::T) where T <: BernoulliLikelihood = true
+_isequal(l1::T, l2::T) where {T<:BernoulliLikelihood} = true
 
 extract_parameters(::PoissonLikelihood) = nothing
 apply_parameters(l::PoissonLikelihood, θ) = l
-_isequal(l1::T, l2::T) where T <: PoissonLikelihood = true
+_isequal(l1::T, l2::T) where {T<:PoissonLikelihood} = true
 
 
 
@@ -202,7 +202,7 @@ function apply_parameters(sva::SVA, θ)
     return SVA(fz, q)
 end
 
-variational_gaussian(n::Int, T = Float64) = MvNormal(zeros(T, n), Matrix{T}(I, n, n))
+variational_gaussian(n::Int, T=Float64) = MvNormal(zeros(T, n), Matrix{T}(I, n, n))
 
 
 
@@ -214,7 +214,7 @@ _isequal(d1::MvNormal, d2::MvNormal) = isapprox(d1.μ, d1.μ) && isapprox(d1.Σ,
 
 
 # Custom wrappers
-struct NoisyGP{T <: GP, Tn <: Real}
+struct NoisyGP{T<:GP,Tn<:Real}
     gp::T
     obs_noise::Tn
 end
@@ -228,7 +228,7 @@ apply_parameters(f::NoisyGP, θ) = NoisyGP(apply_parameters(f.gp, θ[1]), θ[2])
 costfunction(f::NoisyGP, data) = -logpdf(f(data.x), data.y)
 _isequal(f1::NoisyGP, f2::NoisyGP) = _isequal(f1.gp, f2.gp) && isapprox(f1.obs_noise, f2.obs_noise)
 
-struct SVGP{T <: LatentGP, Ts <: SVA}
+struct SVGP{T<:LatentGP,Ts<:SVA}
     lgp::T
     sva::Ts
     fixed_inducing_points::Bool
diff --git a/test/integration_tests.jl b/test/integration_tests.jl
index 9137263..c348f62 100644
--- a/test/integration_tests.jl
+++ b/test/integration_tests.jl
@@ -1,32 +1,32 @@
 @testitem "GP without noise" begin
-    kernel = 2. * with_lengthscale(SEKernel(), 1.) + 3. * Matern32Kernel() * Matern52Kernel()
-    gp = GP(3., kernel)
-    x = 0.01:0.01:1.
+    kernel = 2.0 * with_lengthscale(SEKernel(), 1.0) + 3.0 * Matern32Kernel() * Matern52Kernel()
+    gp = GP(3.0, kernel)
+    x = 0.01:0.01:1.0
     y = rand(gp(x, 0.1))
-    fitted_gp = EasyGPs.fit(gp, x, y; iterations = 1)
+    fitted_gp = EasyGPs.fit(gp, x, y; iterations=1)
     @test fitted_gp isa typeof(gp)
     @test !EasyGPs._isequal(fitted_gp, gp)
 end
 
 @testitem "GP with Gaussian noise" begin
-    kernel = 2. * with_lengthscale(SEKernel(), 1.) + 3. * WhiteKernel()
-    gp = with_gaussian_noise(GP(3., kernel), 0.1)
-    x = 0.01:0.01:1.
+    kernel = 2.0 * with_lengthscale(SEKernel(), 1.0) + 3.0 * WhiteKernel()
+    gp = with_gaussian_noise(GP(3.0, kernel), 0.1)
+    x = 0.01:0.01:1.0
     y = rand(gp.gp(x, 0.1))
-    fitted_gp = EasyGPs.fit(gp, x, y; iterations = 1)
+    fitted_gp = EasyGPs.fit(gp, x, y; iterations=1)
     @test fitted_gp isa typeof(gp)
     @test !EasyGPs._isequal(fitted_gp, gp)
 end
 
 @testitem "Sparse variational 2d GP with Poisson likelihood" begin
-    kernel = 1. * SEKernel()
+    kernel = 1.0 * SEKernel()
     lgp = LatentGP(GP(0.0, kernel), PoissonLikelihood(), 1e-6)
     x = rand(100, 2) |> RowVecs
     y = round.(Int, 10 .* sum.(abs2, x))
     z = x[begin:5:end]
     sva = SVA(lgp(z).fx, variational_gaussian(length(z)))
-    svgp = SVGP(lgp, sva; fixed_inducing_points = true)
-    fitted_svgp = EasyGPs.fit(svgp, x, y; iterations = 1)
+    svgp = SVGP(lgp, sva; fixed_inducing_points=true)
+    fitted_svgp = EasyGPs.fit(svgp, x, y; iterations=1)
     @test fitted_svgp isa typeof(svgp)
     @test !EasyGPs._isequal(fitted_svgp, svgp)
     @test fitted_svgp.sva.fz.x === z
diff --git a/test/unit_tests.jl b/test/unit_tests.jl
index 04ef612..fcd0798 100644
--- a/test/unit_tests.jl
+++ b/test/unit_tests.jl
@@ -13,12 +13,12 @@ end
 @testitem "parameterize" begin
     import ParameterHandling
     for object in (
-        ZeroMean(), ConstMean(1.),
+        ZeroMean(), ConstMean(1.0),
         SEKernel(), Matern32Kernel(), Matern52Kernel(),
-        with_lengthscale(SEKernel(), 2.),
-        2. * SEKernel(), 3. * SEKernel() + 2. * Matern32Kernel(),
-        2. * Matern32Kernel() * SEKernel(),
-        2. * with_lengthscale(SEKernel(), 1.) + 3. * Matern32Kernel() * Matern52Kernel(),
+        with_lengthscale(SEKernel(), 2.0),
+        2.0 * SEKernel(), 3.0 * SEKernel() + 2.0 * Matern32Kernel(),
+        2.0 * Matern32Kernel() * SEKernel(),
+        2.0 * with_lengthscale(SEKernel(), 1.0) + 3.0 * Matern32Kernel() * Matern52Kernel(),
         BernoulliLikelihood(),
         PoissonLikelihood()
     )

From 1f8f15fcc2acedfe3cff82905622128c0f3586a2 Mon Sep 17 00:00:00 2001
From: Simone Carlo Surace <simsurace@gmx.net>
Date: Wed, 6 Mar 2024 16:26:49 +0100
Subject: [PATCH 3/5] Violate code style

---
 src/EasyGPs.jl | 5 +----
 1 file changed, 1 insertion(+), 4 deletions(-)

diff --git a/src/EasyGPs.jl b/src/EasyGPs.jl
index 8c4c105..15b67ea 100644
--- a/src/EasyGPs.jl
+++ b/src/EasyGPs.jl
@@ -68,10 +68,7 @@ function optimize(
     kwargs...
 )
     par0, unflatten = ParameterHandling.flatten(θ0)
-    optf = Optimization.OptimizationFunction(
-        (par, data) -> costfunction(model(unflatten(par)), data),
-        Optimization.AutoZygote()
-    )
+    optf = Optimization.OptimizationFunction((par, data) -> costfunction(model(unflatten(par)), data), Optimization.AutoZygote())
     prob = Optimization.OptimizationProblem(optf, par0, data)
     sol = Optimization.solve(prob, optimizer; maxiters=iterations)
     return unflatten(sol.u)

From a844da214366df459487a3b701033de1fe5aa5bd Mon Sep 17 00:00:00 2001
From: Simone Carlo Surace <simsurace@gmx.net>
Date: Wed, 6 Mar 2024 17:17:10 +0100
Subject: [PATCH 4/5] Run formatter

---
 docs/make.jl                   | 26 ++++++++---------
 examples/0-mauna-loa/script.jl | 36 ++++++++++++-----------
 src/EasyGPs.jl                 | 52 ++++++++++++++++------------------
 test/integration_tests.jl      | 11 +++----
 test/unit_tests.jl             | 12 +++++---
 5 files changed, 70 insertions(+), 67 deletions(-)

diff --git a/docs/make.jl b/docs/make.jl
index 2b3a9a9..4694148 100644
--- a/docs/make.jl
+++ b/docs/make.jl
@@ -1,6 +1,8 @@
 ### Process examples
 using Pkg
-Pkg.add(Pkg.PackageSpec(; url="https://github.com/JuliaGaussianProcesses/JuliaGPsDocs.jl")) # While the package is unregistered, it's a workaround
+Pkg.add(
+    Pkg.PackageSpec(; url = "https://github.com/JuliaGaussianProcesses/JuliaGPsDocs.jl"),
+) # While the package is unregistered, it's a workaround
 
 using JuliaGPsDocs
 
@@ -18,24 +20,20 @@ DocMeta.setdocmeta!(
     quote
         using EasyGPs
     end;  # we have to load all packages used (implicitly) within jldoctest blocks in the API docstrings
-    recursive=true,
+    recursive = true,
 )
 
 makedocs(;
-    sitename="EasyGPs.jl",
-    format=Documenter.HTML(;
-        size_threshold_ignore=[
-            "examples/0-mauna-loa/index.md",
-        ],
-    ),
-    modules=[EasyGPs],
-    pages=[
+    sitename = "EasyGPs.jl",
+    format = Documenter.HTML(; size_threshold_ignore = ["examples/0-mauna-loa/index.md"]),
+    modules = [EasyGPs],
+    pages = [
         "Home" => "index.md",
         "Examples" => JuliaGPsDocs.find_generated_examples(EasyGPs),
     ],
-    warnonly=true,
-    checkdocs=:exports,
-    doctestfilters=JuliaGPsDocs.DOCTEST_FILTERS,
+    warnonly = true,
+    checkdocs = :exports,
+    doctestfilters = JuliaGPsDocs.DOCTEST_FILTERS,
 )
 
-deploydocs(; repo="github.com/JuliaGaussianProcesses/EasyGPs.jl.git", push_preview=true)
+deploydocs(; repo = "github.com/JuliaGaussianProcesses/EasyGPs.jl.git", push_preview = true)
diff --git a/examples/0-mauna-loa/script.jl b/examples/0-mauna-loa/script.jl
index f07d7b3..1869b5f 100644
--- a/examples/0-mauna-loa/script.jl
+++ b/examples/0-mauna-loa/script.jl
@@ -17,7 +17,7 @@ using Plots  # visualisation
 # Let's load and visualize the dataset.
 
 (xtrain, ytrain), (xtest, ytest) = let
-    data = CSV.read(joinpath(@__DIR__, "CO2_data.csv"), Tables.matrix; header=0)
+    data = CSV.read(joinpath(@__DIR__, "CO2_data.csv"), Tables.matrix; header = 0)
     year = data[:, 1]
     co2 = data[:, 2]
 
@@ -29,9 +29,9 @@ using Plots  # visualisation
 end
 
 function plotdata()
-    plot(; xlabel="year", ylabel="CO₂ [ppm]", legend=:bottomright)
-    scatter!(xtrain, ytrain; label="training data", ms=2, markerstrokewidth=0)
-    return scatter!(xtest, ytest; label="test data", ms=2, markerstrokewidth=0)
+    plot(; xlabel = "year", ylabel = "CO₂ [ppm]", legend = :bottomright)
+    scatter!(xtrain, ytrain; label = "training data", ms = 2, markerstrokewidth = 0)
+    return scatter!(xtest, ytest; label = "test data", ms = 2, markerstrokewidth = 0)
 end
 
 plotdata()
@@ -42,10 +42,12 @@ plotdata()
 # original tutorial. 
 
 k_smooth_trend = exp(8.0) * with_lengthscale(SEKernel(), exp(4.0))#with_lengthscale(SEKernel(), exp(4.0))
-k_seasonality = exp(2.0) * PeriodicKernel(; r=[0.5]) *
-                with_lengthscale(SEKernel(), exp(4.0))
-k_medium_term_irregularities = 1.0 * with_lengthscale(RationalQuadraticKernel(; α=exp(-1.0)), 1.0)
-k_noise_terms = exp(-4.0) * with_lengthscale(SEKernel(), exp(-2.0)) + exp(-4.0) * WhiteKernel()
+k_seasonality =
+    exp(2.0) * PeriodicKernel(; r = [0.5]) * with_lengthscale(SEKernel(), exp(4.0))
+k_medium_term_irregularities =
+    1.0 * with_lengthscale(RationalQuadraticKernel(; α = exp(-1.0)), 1.0)
+k_noise_terms =
+    exp(-4.0) * with_lengthscale(SEKernel(), exp(-2.0)) + exp(-4.0) * WhiteKernel()
 kernel = k_smooth_trend + k_seasonality + k_medium_term_irregularities + k_noise_terms
 #md nothing #hide
 
@@ -69,11 +71,11 @@ fpost_init = posterior(gp(xtrain), ytrain)
 # By setting `ribbon_scale=2` we visualize the uncertainty band with ``\pm 2``
 # (instead of the default ``\pm 1``) standard deviations.
 
-plot_gp!(f; label) = plot!(f(1920:0.2:2030); ribbon_scale=2, linewidth=1, label)
+plot_gp!(f; label) = plot!(f(1920:0.2:2030); ribbon_scale = 2, linewidth = 1, label)
 #md nothing #hide
 
 plotdata()
-plot_gp!(fpost_init; label="posterior f(⋅)")
+plot_gp!(fpost_init; label = "posterior f(⋅)")
 
 # A reasonable fit to the data, but poor extrapolation away from the observations!
 
@@ -84,11 +86,13 @@ plot_gp!(fpost_init; label="posterior f(⋅)")
 # We pass an option to choose the exact same optimizer as in the original tutorial.
 
 @time fitted_gp = EasyGPs.fit(
-    gp, xtrain, ytrain;
-    optimizer=Optim.LBFGS(;
-        alphaguess=Optim.LineSearches.InitialStatic(; scaled=true),
-        linesearch=Optim.LineSearches.BackTracking(),
-    )
+    gp,
+    xtrain,
+    ytrain;
+    optimizer = Optim.LBFGS(;
+        alphaguess = Optim.LineSearches.InitialStatic(; scaled = true),
+        linesearch = Optim.LineSearches.BackTracking(),
+    ),
 )
 #md nothing #hide
 
@@ -104,4 +108,4 @@ fpost_opt.prior.kernel
 # And, finally, we can visualize our optimized posterior GP:
 
 plotdata()
-plot_gp!(fpost_opt; label="optimized posterior f(⋅)")
+plot_gp!(fpost_opt; label = "optimized posterior f(⋅)")
diff --git a/src/EasyGPs.jl b/src/EasyGPs.jl
index 15b67ea..f44f0dc 100644
--- a/src/EasyGPs.jl
+++ b/src/EasyGPs.jl
@@ -61,16 +61,14 @@ Takes a callable `model` and returns the optimal parameter, starting with initia
 `θ0`. In order to work, there needs to be an implementation of `EasyGPs.costfunction` taking
 two arguments, the first of which is of type `typeof(model(θ0))`.
 """
-function optimize(
-    model, θ0, data;
-    iterations=1000,
-    optimizer=Optim.BFGS(),
-    kwargs...
-)
+function optimize(model, θ0, data; iterations = 1000, optimizer = Optim.BFGS(), kwargs...)
     par0, unflatten = ParameterHandling.flatten(θ0)
-    optf = Optimization.OptimizationFunction((par, data) -> costfunction(model(unflatten(par)), data), Optimization.AutoZygote())
+    optf = Optimization.OptimizationFunction(
+        (par, data) -> costfunction(model(unflatten(par)), data),
+        Optimization.AutoZygote(),
+    )
     prob = Optimization.OptimizationProblem(optf, par0, data)
-    sol = Optimization.solve(prob, optimizer; maxiters=iterations)
+    sol = Optimization.solve(prob, optimizer; maxiters = iterations)
     return unflatten(sol.u)
 end
 
@@ -96,20 +94,19 @@ _isequal(m1::ConstMean, m2::ConstMean) = isapprox(m1.c, m2.c)
 
 
 # Simple kernels
-KernelsWithoutParameters = Union{
-    SEKernel,Matern32Kernel,Matern52Kernel,WhiteKernel
-}
+KernelsWithoutParameters = Union{SEKernel,Matern32Kernel,Matern52Kernel,WhiteKernel}
 
 extract_parameters(::T) where {T<:KernelsWithoutParameters} = nothing
 apply_parameters(k::T, θ) where {T<:KernelsWithoutParameters} = k
 _isequal(k1::T, k2::T) where {T<:KernelsWithoutParameters} = true
 
 extract_parameters(k::PeriodicKernel) = ParameterHandling.positive(only(k.r))
-apply_parameters(::PeriodicKernel, θ) = PeriodicKernel(r=[θ])
+apply_parameters(::PeriodicKernel, θ) = PeriodicKernel(r = [θ])
 _isequal(k1::T, k2::T) where {T<:PeriodicKernel} = k1.r ≈ k2.r
 
 extract_parameters(k::RationalQuadraticKernel) = ParameterHandling.positive(only(k.α))
-apply_parameters(k::RationalQuadraticKernel, θ) = RationalQuadraticKernel(; α=θ, metric=k.metric)
+apply_parameters(k::RationalQuadraticKernel, θ) =
+    RationalQuadraticKernel(; α = θ, metric = k.metric)
 _isequal(k1::T, k2::T) where {T<:RationalQuadraticKernel} = true
 
 
@@ -121,7 +118,8 @@ _isequal(k1::KernelSum, k2::KernelSum) = mapreduce(_isequal, &, k1.kernels, k2.k
 
 extract_parameters(k::KernelProduct) = map(extract_parameters, k.kernels)
 apply_parameters(k::KernelProduct, θ) = KernelProduct(map(apply_parameters, k.kernels, θ))
-_isequal(k1::KernelProduct, k2::KernelProduct) = mapreduce(_isequal, &, k1.kernels, k2.kernels)
+_isequal(k1::KernelProduct, k2::KernelProduct) =
+    mapreduce(_isequal, &, k1.kernels, k2.kernels)
 
 function extract_parameters(k::TransformedKernel)
     return (extract_parameters(k.kernel), extract_parameters(k.transform))
@@ -130,7 +128,7 @@ end
 function apply_parameters(k::TransformedKernel, θ)
     return TransformedKernel(
         apply_parameters(k.kernel, θ[1]),
-        apply_parameters(k.transform, θ[2])
+        apply_parameters(k.transform, θ[2]),
     )
 end
 
@@ -143,10 +141,7 @@ function extract_parameters(k::ScaledKernel)
 end
 
 function apply_parameters(k::ScaledKernel, θ)
-    return ScaledKernel(
-        apply_parameters(k.kernel, θ[1]),
-        θ[2]
-    )
+    return ScaledKernel(apply_parameters(k.kernel, θ[1]), θ[2])
 end
 
 function _isequal(k1::ScaledKernel, k2::ScaledKernel)
@@ -175,12 +170,14 @@ _isequal(l1::T, l2::T) where {T<:PoissonLikelihood} = true
 
 # GPs
 extract_parameters(f::GP) = (extract_parameters(f.mean), extract_parameters(f.kernel))
-apply_parameters(f::GP, θ) = GP(apply_parameters(f.mean, θ[1]), apply_parameters(f.kernel, θ[2]))
+apply_parameters(f::GP, θ) =
+    GP(apply_parameters(f.mean, θ[1]), apply_parameters(f.kernel, θ[2]))
 costfunction(f::GP, data) = -logpdf(f(data.x), data.y)
 _isequal(f1::GP, f2::GP) = _isequal(f1.mean, f2.mean) && _isequal(f1.kernel, f2.kernel)
 
 extract_parameters(f::LatentGP) = (extract_parameters(f.f), extract_parameters(f.lik))
-apply_parameters(f::LatentGP, θ) = LatentGP(apply_parameters(f.f, θ[1]), apply_parameters(f.lik, θ[2]), f.Σy)
+apply_parameters(f::LatentGP, θ) =
+    LatentGP(apply_parameters(f.f, θ[1]), apply_parameters(f.lik, θ[2]), f.Σy)
 
 
 
@@ -199,7 +196,7 @@ function apply_parameters(sva::SVA, θ)
     return SVA(fz, q)
 end
 
-variational_gaussian(n::Int, T=Float64) = MvNormal(zeros(T, n), Matrix{T}(I, n, n))
+variational_gaussian(n::Int, T = Float64) = MvNormal(zeros(T, n), Matrix{T}(I, n, n))
 
 
 
@@ -220,10 +217,12 @@ end
 
 with_gaussian_noise(gp::GP, obs_noise::Real) = NoisyGP(gp, obs_noise)
 
-extract_parameters(f::NoisyGP) = (extract_parameters(f.gp), ParameterHandling.positive(f.obs_noise, exp, 1e-6))
+extract_parameters(f::NoisyGP) =
+    (extract_parameters(f.gp), ParameterHandling.positive(f.obs_noise, exp, 1e-6))
 apply_parameters(f::NoisyGP, θ) = NoisyGP(apply_parameters(f.gp, θ[1]), θ[2])
 costfunction(f::NoisyGP, data) = -logpdf(f(data.x), data.y)
-_isequal(f1::NoisyGP, f2::NoisyGP) = _isequal(f1.gp, f2.gp) && isapprox(f1.obs_noise, f2.obs_noise)
+_isequal(f1::NoisyGP, f2::NoisyGP) =
+    _isequal(f1.gp, f2.gp) && isapprox(f1.obs_noise, f2.obs_noise)
 
 struct SVGP{T<:LatentGP,Ts<:SVA}
     lgp::T
@@ -234,10 +233,7 @@ end
 SVGP(lgp, sva; fixed_inducing_points) = SVGP(lgp, sva, fixed_inducing_points)
 
 function extract_parameters(f::SVGP)
-    return (
-        extract_parameters(f.lgp),
-        extract_parameters(f.sva, f.fixed_inducing_points),
-    )
+    return (extract_parameters(f.lgp), extract_parameters(f.sva, f.fixed_inducing_points))
 end
 
 function apply_parameters(f::SVGP, θ)
diff --git a/test/integration_tests.jl b/test/integration_tests.jl
index c348f62..b673da6 100644
--- a/test/integration_tests.jl
+++ b/test/integration_tests.jl
@@ -1,9 +1,10 @@
 @testitem "GP without noise" begin
-    kernel = 2.0 * with_lengthscale(SEKernel(), 1.0) + 3.0 * Matern32Kernel() * Matern52Kernel()
+    kernel =
+        2.0 * with_lengthscale(SEKernel(), 1.0) + 3.0 * Matern32Kernel() * Matern52Kernel()
     gp = GP(3.0, kernel)
     x = 0.01:0.01:1.0
     y = rand(gp(x, 0.1))
-    fitted_gp = EasyGPs.fit(gp, x, y; iterations=1)
+    fitted_gp = EasyGPs.fit(gp, x, y; iterations = 1)
     @test fitted_gp isa typeof(gp)
     @test !EasyGPs._isequal(fitted_gp, gp)
 end
@@ -13,7 +14,7 @@ end
     gp = with_gaussian_noise(GP(3.0, kernel), 0.1)
     x = 0.01:0.01:1.0
     y = rand(gp.gp(x, 0.1))
-    fitted_gp = EasyGPs.fit(gp, x, y; iterations=1)
+    fitted_gp = EasyGPs.fit(gp, x, y; iterations = 1)
     @test fitted_gp isa typeof(gp)
     @test !EasyGPs._isequal(fitted_gp, gp)
 end
@@ -25,8 +26,8 @@ end
     y = round.(Int, 10 .* sum.(abs2, x))
     z = x[begin:5:end]
     sva = SVA(lgp(z).fx, variational_gaussian(length(z)))
-    svgp = SVGP(lgp, sva; fixed_inducing_points=true)
-    fitted_svgp = EasyGPs.fit(svgp, x, y; iterations=1)
+    svgp = SVGP(lgp, sva; fixed_inducing_points = true)
+    fitted_svgp = EasyGPs.fit(svgp, x, y; iterations = 1)
     @test fitted_svgp isa typeof(svgp)
     @test !EasyGPs._isequal(fitted_svgp, svgp)
     @test fitted_svgp.sva.fz.x === z
diff --git a/test/unit_tests.jl b/test/unit_tests.jl
index fcd0798..e8a6b1a 100644
--- a/test/unit_tests.jl
+++ b/test/unit_tests.jl
@@ -13,14 +13,18 @@ end
 @testitem "parameterize" begin
     import ParameterHandling
     for object in (
-        ZeroMean(), ConstMean(1.0),
-        SEKernel(), Matern32Kernel(), Matern52Kernel(),
+        ZeroMean(),
+        ConstMean(1.0),
+        SEKernel(),
+        Matern32Kernel(),
+        Matern52Kernel(),
         with_lengthscale(SEKernel(), 2.0),
-        2.0 * SEKernel(), 3.0 * SEKernel() + 2.0 * Matern32Kernel(),
+        2.0 * SEKernel(),
+        3.0 * SEKernel() + 2.0 * Matern32Kernel(),
         2.0 * Matern32Kernel() * SEKernel(),
         2.0 * with_lengthscale(SEKernel(), 1.0) + 3.0 * Matern32Kernel() * Matern52Kernel(),
         BernoulliLikelihood(),
-        PoissonLikelihood()
+        PoissonLikelihood(),
     )
         model, θ = EasyGPs.parameterize(object)
         new_object = @inferred model(θ)

From 2c327fd278c2caaed1467c4ade067f748454c479 Mon Sep 17 00:00:00 2001
From: Simone Carlo Surace <simsurace@gmx.net>
Date: Wed, 6 Mar 2024 17:30:23 +0100
Subject: [PATCH 5/5] Add code style badge

---
 README.md | 1 +
 1 file changed, 1 insertion(+)

diff --git a/README.md b/README.md
index e158836..b595f13 100644
--- a/README.md
+++ b/README.md
@@ -5,6 +5,7 @@
 [![Docs: dev](https://img.shields.io/badge/docs-dev-blue.svg)](https://JuliaGaussianProcesses.github.io/EasyGPs.jl/dev)
 [![CI](https://github.com/JuliaGaussianProcesses/EasyGPs.jl/actions/workflows/CI.yml/badge.svg)](https://github.com/JuliaGaussianProcesses/EasyGPs.jl/actions/workflows/CI.yml)
 [![Codecov](https://codecov.io/gh/JuliaGaussianProcesses/EasyGPs.jl/branch/master/graph/badge.svg)](https://codecov.io/gh/JuliaGaussianProcesses/EasyGPs.jl/tree/master)
+[![Code Style: Blue](https://img.shields.io/badge/code%20style-blue-4495d1.svg)](https://github.com/JuliaDiff/BlueStyle)
 
 EasyGPs.jl is a package that defines a high-level API for the JuliaGaussianProcesses
 ecosystem. It handles model parameterization and training, allowing users to focus on the