Merge pull request #60 from Gopal1259/master

Author: FriesischScott

src/UncertaintyQuantification.jl

@@ -50,6 +50,7 @@ export GaussianCopula
 export HaltonSampling
 export JointDistribution
 export LatinHypercubeSampling
+export LatticeRuleSampling
 export LineSampling
 export Model
 export MonteCarlo

src/simulations/montecarlo.jl

@@ -18,6 +18,11 @@ struct LatinHypercubeSampling <: AbstractQuasiMonteCarlo
     LatinHypercubeSampling(n) = n > 0 ? new(n) : error("n must be greater than zero")
 end
 
+struct LatticeRuleSampling <: AbstractQuasiMonteCarlo
+    n::Integer
+    LatticeRuleSampling(n) = n > 0 ? new(n) : error("n must be greater than zero")
+end
+
 function sample(inputs::Array{<:UQInput}, sim::MonteCarlo)
     return sample(inputs, sim.n)
 end
@@ -58,3 +63,9 @@ function qmc_samples(sim::LatinHypercubeSampling, rvs::Integer)
         QuasiMonteCarlo.sample(sim.n, zeros(rvs), ones(rvs), LatinHypercubeSample())
     )
 end
+
+function qmc_samples(sim::LatticeRuleSampling, rvs::Integer)
+    return transpose(
+        QuasiMonteCarlo.sample(sim.n, zeros(rvs), ones(rvs), LatticeRuleSample())
+    )
+end

test/Project.toml

@@ -2,5 +2,6 @@
 DataFrames = "a93c6f00-e57d-5684-b7b6-d8193f3e46c0"
 Formatting = "59287772-0a20-5a39-b81b-1366585eb4c0"
 InteractiveUtils = "b77e0a4c-d291-57a0-90e8-8db25a27a240"
+QuasiMonteCarlo = "8a4e6c94-4038-4cdc-81c3-7e6ffdb2a71b"
 Random = "9a3f8284-a2c9-5f02-9a11-845980a1fd5c"
 Test = "8dfed614-e22c-5e08-85e1-65c5234f0b40"

test/runtests.jl

@@ -1,6 +1,7 @@
 using DataFrames
 using Formatting
 using InteractiveUtils
+using QuasiMonteCarlo
 using Random
 using Test
 using UncertaintyQuantification

test/simulations/montecarlo.jl

@@ -46,3 +46,22 @@ end
         end
     end
 end
+
+@testset "LatticeRuleSampling" begin
+    lattice = LatticeRuleSampling(1000)
+    @test isa(lattice, LatticeRuleSampling)
+    @test lattice.n == 1000
+    @testset "sample" begin
+        inputs = [RandomVariable.(Uniform(), [:a, :b]); Parameter(1, :c)]
+
+        Random.seed!(1)
+        s = QuasiMonteCarlo.sample(4, [0, 0], [1, 1], LatticeRuleSample())
+        Random.seed!(1)
+        samples = sample(inputs, LatticeRuleSampling(4))
+        Random.seed!()
+
+        @test isapprox(samples.a, s[1, :])
+        @test isapprox(samples.b, s[2, :])
+        @test samples.c == [1.0, 1.0, 1.0, 1.0]
+    end
+end