RandomizedQuasiMonteCarlo

This package is now deprecated. It has been "merged" with the QuasiMonteCarlo.jl package with PR57 All the randomization methods are now available there see the doc.

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The purpose of this package is to provide randomization method of low discrepancy sequences.

So far only nested uniform scrambling, Cranley Patterson Rotation (shift mod 1) and Linear Matrix Scrambling.

Compared to over Quasi Monte Carlo package the focus here is not to generate low discrepancy sequences (ξ₁, ..., ξₙ) (Sobol', lattice, ...) but on randomization of these sequences (ξ₁, ..., ξₙ) → (x₁, ..., xₙ). The purpose is to obtain many independent realizations of (x₁, ..., xₙ) by using the functions shift!, scrambling!, etc. The original sequences can be obtained for example via the QuasiMonteCarlo.jl package.

The scrambling codes are inspired from Owen's R implementation that can be found here.

Basic examples

using RandomizedQuasiMonteCarlo, QuasiMonteCarlo
m = 7
N = 2^m # Number of points
d = 2 # dimension
M = 32 # Number of bit to represent a digit
b = 2 # Base
u_uniform = rand(N, d) # i.i.d. uniform
u_sobol = permutedims(QuasiMonteCarlo.sample(N, zeros(d),  ones(d), SobolSample()))  # I should update the convention in my pkg to have dim × n and not n × dim
u_nus = nested_uniform_scramble(u_sobol; M=M)
u_lms = linear_matrix_scramble(u_sobol, b; M=M)
u_digital_shift = digital_shift(u_sobol, b; M=M)
u_shift = shift(u_sobol)

# Plot #
using Plots, LaTeXStrings
# Settings I like for plotting
default(fontfamily="Computer Modern", linewidth=1, label=nothing, grid=true, framestyle=:default)

begin
    d1 = 1
    d2 = 2
    sequences = [u_uniform, u_sobol, u_nus, u_lms, u_shift, u_digital_shift]
    names = ["Uniform", "Sobol (unrandomized)", "Nested Uniform Scrambling", "Linear Matrix Scrambling", "Shift", "Digital Shift"]
    p = [plot(thickness_scaling=2, aspect_ratio=:equal) for i in sequences]
    for (i, x) in enumerate(sequences)
        scatter!(p[i], x[:, d1], x[:, d2], ms=0.9, c=1, grid=false)
        title!(names[i])
        xlims!(p[i], (0, 1))
        ylims!(p[i], (0, 1))
        yticks!(p[i], [0, 1])
        xticks!(p[i], [0, 1])
        hline!(p[i], range(0, 1, step=1 / 4), c=:gray, alpha=0.2)
        vline!(p[i], range(0, 1, step=1 / 4), c=:gray, alpha=0.2)
        hline!(p[i], range(0, 1, step=1 / 2), c=:gray, alpha=0.8)
        vline!(p[i], range(0, 1, step=1 / 2), c=:gray, alpha=0.8)
    end
    plot(p..., size=(1500, 900))
end

Scrambling of Faure sequences in base b

Now let say you want to do scrambling in base b.

using QuasiMonteCarlo
using Random

The InertSampler is used to obtain a randomized Faure sequences (see here in the test file).

struct InertSampler <: Random.AbstractRNG end
InertSampler(args...; kwargs...) = InertSampler()
Random.rand(::InertSampler, ::Type{T}) where {T} = zero(T)
Random.shuffle!(::InertSampler, arg::AbstractArray) = arg

m = 4
d = 3
b = QuasiMonteCarlo.nextprime(d)
N = b^m # Number of points
M = m
rng = InertSampler()

# Unrandomized low discrepency sequence
u_faure = permutedims(QuasiMonteCarlo.sample(N, d, FaureSample(rng)))

# Randomized version
u_nus = nested_uniform_scramble(u_faure, b; M=M)
u_lms = linear_matrix_scramble(u_faure, b; M=M)
u_digital_shift = digital_shift(u_faure, b; M=M)

This plot checks (visually) that you are dealing with $(t,d,m)$ sequence i.e. you must see one point per rectangle.

begin
    d1 = 1 
    d2 = 3
    x = u_lms
    p = [plot(thickness_scaling=2, aspect_ratio=:equal) for i in 0:m]
    for i in 0:m
        j = m - i
        xᵢ = range(0, 1, step=1 / b^(i))
        xⱼ = range(0, 1, step=1 / b^(j))
        scatter!(p[i+1], x[:, d1], x[:, d2], ms=2, c=1, grid=false)
        xlims!(p[i+1], (0, 1.01))
        ylims!(p[i+1], (0, 1.01))
        yticks!(p[i+1], [0, 1])
        xticks!(p[i+1], [0, 1])
        hline!(p[i+1], xᵢ, c=:gray, alpha=0.2)
        vline!(p[i+1], xⱼ, c=:gray, alpha=0.2)
    end
    plot(p..., size=(1500, 900))
end

Multiple randomization

In case you need to repeat randomization several times, I suggest you use in place functions and compute some stuff in advance e.g. bit expansion of your initial set of point to be randomized, the which_permutation function used in Nested Uniform Scrambling.

unrandomized_bits = sobol_pts2bits(m, d, M) 

Here I use directly the bit representation for Sobol sequence. I could have done like in previous example and import the Sobol sequence from QuasiMonteCarlo.jl (which calls Sobol.jl). Note that you can call any sequence of point into its bit representation with points2bits function.

random_bits = similar(unrandomized_bits)
indices = which_permutation(unrandomized_bits) # This function is used in Nested Uniform Scramble. I
nus = NestedUniformScrambler(unrandomized_bits, indices)
lms = LinearMatrixScrambler(unrandomized_bits)

u_sob = dropdims(mapslices(bits2unif, unrandomized_bits, dims=3), dims=3)
u_nus = copy(u_sob)
u_lms = copy(u_sob)
u_shift = copy(u_sob)

NumberOfRand = 100

for j in 1:NumberOfRand
    scramble!(u_nus, random_bits, nus)
    scramble!(u_lms, random_bits, lms)
    shift!(u_shift)
end