`GridBoxMap`

Another naive but still useful approach is to place the test points on an equidistant grid within a box. This approach removes an element of randomness from the Monte-Carlo approach.

GAIO.GridBoxMap — Function

BoxMap(:grid, map, domain::Box{N}; n_points::NTuple{N} = ntuple(_->16, N)) -> SampledBoxMap

Construct a SampledBoxMap that uses a grid of test points. The size of the grid is defined by n_points, which is a tuple of length equal to the dimension of the domain.

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BoxMap(:grid, :simd, map, domain::Box{N}; n_points::NTuple{N} = ntuple(_->16, N)) -> CPUSampledBoxMap

Construct a CPUSampledBoxMap that uses a grid of test points. The size of the grid is defined by n_points, which is a tuple of length equal to the dimension of the domain. The number of points is rounded up to the nearest mutiple of the cpu's SIMD capacity.

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BoxMap(:grid, :gpu, map, domain::Box{N}; n_points::NTuple{N} = ntuple(_->16, N)) -> GPUSampledBoxMap

Construct a GPUSampledBoxMap that uses a grid of test points. The size of the grid is defined by n_points, which is a tuple of length equal to the dimension of the domain.

Requires a CUDA-capable gpu.

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Example

julia> n_points = (8, 8)(8, 8)
julia> F = BoxMap(:grid, f, domain, n_points = n_points)SampledBoxMap with 64 sample points
julia> p = plot!(
           p, F(B),
           color=RGBA(0.,1.,0.,0.5),
           lab="$(join(n_points, "x")) grid of test points"
       )Plot{Plots.GRBackend() n=4}

Grid BoxMap