`MonteCarloBoxMap`

The simplest technique for discretization is a Monte-Carlo approach: choose a random set of points (sampled according to a uniform distribution) within a box and record which boxes are hit by the point map.

GAIO.MonteCarloBoxMap — Function

BoxMap(:montecarlo, map, domain::Box{N}; n_points=16*N) -> SampledBoxMap

Construct a SampledBoxMap that uses n_points Monte-Carlo test points.

source

BoxMap(:montecarlo, :simd, map, domain::Box{N}; n_points=16*N) -> SampledBoxMap

Construct a CPUSampledBoxMap that uses n_points Monte-Carlo test points. The number of points is rounded up to the nearest multiple of the cpu's SIMD capacity.

source

BoxMap(:montecarlo, :gpu, map, domain::Box{N}; n_points=16*N) -> GPUSampledBoxMap

Construct a GPUSampledBoxMap that uses n_points Monte-Carlo test points.

Requires a CUDA-capable gpu.

source

Example

julia> n_points = 128128
julia> F = BoxMap(:montecarlo, f, domain, n_points = n_points)SampledBoxMap with 128 sample points
julia> p = plot!(
           p, F(B),
           color=RGBA(1.,0.,0.,0.5),
           lab="$n_points MonteCarlo test points"
       )Plot{Plots.GRBackend() n=4}

MonteCarlo BoxMap