Optimised subset of LHC Sampling Plan

Generate an optimised subset of an existing plan. The optimisation of the subset is based on a genetic algorithm.

Functions

LatinHypercubeSampling.subLHCoptim — Method

function subLHCoptim(X,n::Int,gens; rng::U=Random.GLOBAL_RNG,
                                    popsize::Int=100,
                                    ntour::Int=2,
                                    ptour::Float64=0.8,
                                    periodic_ae::Bool=false,
                                    ae_power::Union{Int,Float64}=2) where U <: AbstractRNG

Produce an optimized Latin Hyper Cube with n sample points from a subset of points in X. Optimization is run for gens generations. Returns a tuple of the sample plan and the optimization fitness history.

source

LatinHypercubeSampling.subLHCindex — Method

function subLHCindex(X,Xsub)

Index in the large LHC to get the subLHC.

source

Example

Create an optimised subset LHC plan from an existing plan with 120 points in 2 dimensions.

julia> subLHCoptim(X,Xsub)

The indices of the subset in the larger plan can be extracted with

julia> subLHCindex(X,Xsub)