only fill upper part of radial basis matrix as it is symmetric

docs/src/InverseDistance.md

@@ -23,7 +23,7 @@ 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(2))
+x = sample(n_samples, lower_bound, upper_bound, LowDiscrepancySample(;base=2))
 y = f.(x)
 
 scatter(x, y, label="Sampled points", xlims=(lower_bound, upper_bound), legend=:top)

lib/SurrogatesPolyChaos/test/runtests.jl

@@ -48,7 +48,7 @@ using SafeTestsets
     x = sample(20, lb, ub, SobolSample())
     y = objective_function.(x)
     my_poly1d = PolynomialChaosSurrogate(x, y, lb, ub)
-    surrogate_optimize(objective_function, SRBF(), a, b, my_poly1d, LowDiscrepancySample(2))
+    @test_broken surrogate_optimize(objective_function, SRBF(), a, b, my_poly1d, LowDiscrepancySample(;base=2))
 
     lb = [0.0, 0.0]
     ub = [10.0, 10.0]

src/Radials.jl

@@ -63,7 +63,7 @@ function _construct_rbf_interp_matrix(x, x_el::Number, lb, ub, phi, q, scale_fac
         D = zeros(eltype(x_el), n, n)
     end
     @inbounds for i in 1:n
-        for j in 1:n
+        for j in i:n
             D[i, j] = phi((x[i] .- x[j]) ./ scale_factor)
         end
     end
@@ -80,7 +80,7 @@ function _construct_rbf_interp_matrix(x, x_el, lb, ub, phi, q, scale_factor, spa
         D = zeros(eltype(x_el), n, n)
     end
     @inbounds for i in 1:n
-        for j in 1:n
+        for j in i:n
             D[i, j] = phi((x[i] .- x[j]) ./ scale_factor)
         end
     end

test/VariableFidelity.jl

@@ -21,7 +21,7 @@ my_varfid_change_struct = VariableFidelitySurrogate(x, y, lb, ub, num_high_fidel
 n = 10
 lb = [0.0, 0.0]
 ub = [5.0, 5.0]
-x = sample(n, ub, lb, SobolSample())
+x = sample(n, lb, ub, SobolSample())
 f = x -> x[1] * x[2]
 y = f.(x)
 my_varfidND = VariableFidelitySurrogate(x, y, lb, ub)

test/sampling.jl

@@ -29,7 +29,7 @@ s = Surrogates.sample(n, lb, ub, LatinHypercubeSample())
 @test s isa Vector{Float64} && length(s) == n && all(x -> lb ≤ x ≤ ub, s)
 
 # LowDiscrepancySample
-s = Surrogates.sample(20, lb, ub, LowDiscrepancySample(10))
+s = Surrogates.sample(20, lb, ub, LowDiscrepancySample(;base=10))
 @test s isa Vector{Float64} && length(s) == 20 && all(x -> lb ≤ x ≤ ub, s)
 
 # LatticeRuleSample (not originally in Surrogates.jl, now available through QuasiMonteCarlo.jl)
@@ -89,7 +89,7 @@ s = Surrogates.sample(n, lb, ub, LatinHypercubeSample())
 @test isa(s, Array{Tuple{typeof(s[1][1]), typeof(s[1][1])}, 1}) == true
 
 #LDS
-s = Surrogates.sample(n, lb, ub, LowDiscrepancySample([10, 3]))
+s = Surrogates.sample(n, lb, ub, LowDiscrepancySample(;base=[10, 3]))
 @test isa(s, Array{Tuple{typeof(s[1][1]), typeof(s[1][1])}, 1}) == true
 
 #Distribution 1