Format .jl files (UB-SSDC-Lab#60)

Co-authored-by: GrantHecht <GrantHecht@users.noreply.github.com>

Author: github-actions[bot]

scripts/de_testing.jl

@@ -55,7 +55,7 @@ mutation_strategy = SelfMutationParameters(
 crossover_strategy = SelfBinomialCrossoverParameters(;
     dist=Uniform(0.0, 1.0),
     #transform = GlobalOptimization.NoTransformation()
-    transform = GlobalOptimization.UncorrelatedCovarianceTransformation(0.5, 0.8, N; ps = 1.0),
+    transform=GlobalOptimization.UncorrelatedCovarianceTransformation(0.5, 0.8, N; ps=1.0),
     #transform = GlobalOptimization.CovarianceTransformation(0.1, 0.5, N)
 )
 

src/DE/crossover.jl

@@ -191,7 +191,7 @@ struct UncorrelatedCovarianceTransformation <: RotationMatrixBasedCrossoverTrans
     UncorrelatedCovarianceTransformation(1.0, 0.5, 0.95, [1.0630691323565e-311 1.0630691151907e-311 … 1.063069230316e-311 1.063069119645e-311; 1.0630691158705e-311 1.063069115333e-311 … 1.063069172704e-311 1.0630692904614e-311; … ; 1.063069115428e-311 1.063069114246e-311 … 1.063069124886e-311 1.0630694190924e-311; 1.0630691153804e-311 1.0630691141986e-311 … 1.0630691348624e-311 1.063069428614e-311], [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0], [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0], UInt16[], UInt16[])
     ```
     """
-    function UncorrelatedCovarianceTransformation(pb, a, num_dims; ps = 1.0)
+    function UncorrelatedCovarianceTransformation(pb, a, num_dims; ps=1.0)
         if ps <= 0.0 || ps > 1.0
             throw(ArgumentError("ps must be in the range (0, 1]."))
         end
@@ -202,13 +202,23 @@ struct UncorrelatedCovarianceTransformation <: RotationMatrixBasedCrossoverTrans
             throw(ArgumentError("a must be in the range (0, 1]."))
         end
         B = Matrix{Float64}(undef, num_dims, num_dims)
-        return new(ps, pb, a, B, zeros(num_dims), zeros(num_dims), Vector{UInt16}(undef, 0), Vector{UInt16}(undef, 0))
+        return new(
+            ps,
+            pb,
+            a,
+            B,
+            zeros(num_dims),
+            zeros(num_dims),
+            Vector{UInt16}(undef, 0),
+            Vector{UInt16}(undef, 0),
+        )
     end
-
 end
 
 initialize!(transformation::NoTransformation, population_size) = nothing
-function initialize!(transformation::RotationMatrixBasedCrossoverTransformation, population_size)
+function initialize!(
+    transformation::RotationMatrixBasedCrossoverTransformation, population_size
+)
     resize!(transformation.idxs, population_size)
     transformation.idxs .= 1:population_size
     return nothing
@@ -241,7 +251,9 @@ function update_transformation!(transformation::CovarianceTransformation, popula
 
     return nothing
 end
-function update_transformation!(transformation::UncorrelatedCovarianceTransformation, population)
+function update_transformation!(
+    transformation::UncorrelatedCovarianceTransformation, population
+)
     # get correlation for each pair of vectors in population
     cor_mat = cor(stack(population.current_generation.candidates))
 
@@ -262,8 +274,8 @@ function update_transformation!(transformation::UncorrelatedCovarianceTransforma
 
         #  find points where two candidates are strongly correlated
 
-        @inbounds for j in axes(cor_mat,2)
-            for i in j+1:size(cor_mat,1)
+        @inbounds for j in axes(cor_mat, 2)
+            for i in (j + 1):size(cor_mat, 1)
                 abs_x = abs(cor_mat[i, j])
                 if abs_x >= transformation.a
                     #Base.push!(transformation.pairs, SVector(i, j))
@@ -274,7 +286,6 @@ function update_transformation!(transformation::UncorrelatedCovarianceTransforma
             end
         end
 
-
         # if we're removing all but one idx, set transformation to identity and return
         if length(transformation.cidxs) >= length(transformation.idxs) - 1
             fill_identity!(transformation.B)
@@ -288,7 +299,11 @@ function update_transformation!(transformation::UncorrelatedCovarianceTransforma
         setdiff!(transformation.idxs, transformation.cidxs)
 
         # get number of candidates to use for covariance based on remaining candidates
-        n = clamp(ceil(Int, transformation.ps * length(transformation.idxs)), 2, length(transformation.idxs))
+        n = clamp(
+            ceil(Int, transformation.ps * length(transformation.idxs)),
+            2,
+            length(transformation.idxs),
+        )
 
         # Get indices of n best remaining candidates
         idxs = view(transformation.idxs, 1:n)
@@ -321,7 +336,9 @@ function to_transformed(transformation::RotationMatrixBasedCrossoverTransformati
 end
 
 from_transformed!(transformation::NoTransformation, mt, m) = nothing
-function from_transformed!(transformation::RotationMatrixBasedCrossoverTransformation, mt, m)
+function from_transformed!(
+    transformation::RotationMatrixBasedCrossoverTransformation, mt, m
+)
     mul!(m, transformation.B, mt)
     return nothing
 end

src/utils.jl

@@ -36,7 +36,7 @@ Fills the `mat` in-place with the identity matrix.
 function fill_identity!(mat)
     @inbounds for j in axes(mat, 2)
         for i in axes(mat, 1)
-            mat[i,j] = ifelse(i == j, 1.0, 0.0)
+            mat[i, j] = ifelse(i == j, 1.0, 0.0)
         end
     end
     return nothing

test/base.jl

@@ -750,12 +750,11 @@ end
 @testset showtiming = true "Utils" begin
     # Test general utilities from utils.jl
 
-    mat = ones(3,3)
+    mat = ones(3, 3)
     @test GlobalOptimization.all_correlated(mat, 0.8) == true
-    mat[3,1] = 0.1
+    mat[3, 1] = 0.1
     @test GlobalOptimization.all_correlated(mat, 0.8) == false
 
     GlobalOptimization.fill_identity!(mat)
-    @test isapprox(mat, [1. 0 0; 0 1. 0; 0 0 1.], atol=1e-8)
-
-end
+    @test isapprox(mat, [1.0 0 0; 0 1.0 0; 0 0 1.0], atol=1e-8)
+end

test/de.jl

@@ -302,9 +302,15 @@ end
     @test newm == collect(orig_c)
 
     # Test UncorrelatedCovarianceTransformation constructors
-    @test_throws ArgumentError GlobalOptimization.UncorrelatedCovarianceTransformation(0.0, 0.5, 2)
-    @test_throws ArgumentError GlobalOptimization.UncorrelatedCovarianceTransformation(0.5, 0.0, 2)
-    @test_throws ArgumentError GlobalOptimization.UncorrelatedCovarianceTransformation(0.5, 0.5, 2; ps=0.0)
+    @test_throws ArgumentError GlobalOptimization.UncorrelatedCovarianceTransformation(
+        0.0, 0.5, 2
+    )
+    @test_throws ArgumentError GlobalOptimization.UncorrelatedCovarianceTransformation(
+        0.5, 0.0, 2
+    )
+    @test_throws ArgumentError GlobalOptimization.UncorrelatedCovarianceTransformation(
+        0.5, 0.5, 2; ps=0.0
+    )
     # Make all candidates identical so covariance is zero matrix
 
     # Test UncorrelatedCovarianceTransformation initialize
@@ -338,11 +344,11 @@ end
     ct5 = GlobalOptimization.UncorrelatedCovarianceTransformation(1.0, 0.8, 3)
     GlobalOptimization.initialize!(ct5, 5)
     pop2 = GlobalOptimization.DEPopulation(5, 3)
-    pop2.current_generation.candidates[1] .= SVector(0.333686,  0.22681,  0.939183)
-    pop2.current_generation.candidates[2] .= SVector(0.877709,  0.0211374,  0.791849)
-    pop2.current_generation.candidates[3] .= SVector(0.745745,  0.846223,  0.937728)
-    pop2.current_generation.candidates[4] .= SVector(0.181283,  0.716657,  0.202931)
-    pop2.current_generation.candidates[5] .= SVector(0.771286,  0.184219,  0.11133)
+    pop2.current_generation.candidates[1] .= SVector(0.333686, 0.22681, 0.939183)
+    pop2.current_generation.candidates[2] .= SVector(0.877709, 0.0211374, 0.791849)
+    pop2.current_generation.candidates[3] .= SVector(0.745745, 0.846223, 0.937728)
+    pop2.current_generation.candidates[4] .= SVector(0.181283, 0.716657, 0.202931)
+    pop2.current_generation.candidates[5] .= SVector(0.771286, 0.184219, 0.11133)
     pop2.current_generation.candidates_fitness .= [1, 1, 2, 3, 4]
     GlobalOptimization.update_transformation!(ct5, pop2)
     @test isapprox(
@@ -352,7 +358,7 @@ end
             0.13752647954958294 0.0030534951859484526 0.99049338392028;
             0.9679481354178778 0.21257893123736155 0.1337408132734163
         ];
-        atol=1.0e-8
+        atol=1.0e-8,
     )
 
     # Reduce threshold for 'correlated' terms so that we are removing all but one candidate, ensure transform is identity
@@ -361,7 +367,6 @@ end
     GlobalOptimization.update_transformation!(ct5, pop2)
     @test isapprox(ct5.B, [1.0 0 0; 0 1.0 0; 0 0 1.0]; atol=1.0e-8)
 
-
     # Test to_transformed always transforms when pb=1.0
     orig_c = pop.current_generation.candidates[1]
     orig_m = pop.mutants.candidates[1]