This Package implements a variation of the Voronoi Graph Traversal algorithm by Polianskii and Pokorny [1]. It constructs a Voronoi Diagram from a set of points by performing a random walk on the graph of the vertices of the diagram. Unlike many other Voronoi implementations this algorithm is not limited to 2 or 3 dimensions and promises good performance even in higher dimensions.
We can compute the Voronoi diagram with a simple call of voronoi
julia> data = rand(4, 100) # 100 points in 4D space
julia> v, P = voronoi(data)which returns the vertices v::Dict. keys(v) returns the simplicial complex of the diagram,
wheras v[xs] returns the coordinates of the vertex inbetween the generators data[xs].
Additionally P contains the data in a vector-of-vectors format, used for further computations.
It also exports the random walk variant (returning only a subset of vertices):
julia> v, P = voronoi_random(data, 1000) # perform 1000 iterations of the random walkjulia> A,V = volumes(v, P)computes the (deterministic) areas of the boundaries of neighbouring cells (as sparse array A)
as well as the volume of the cells themselves (vector V) by falling back onto the Polyhedra.jl volume computation.
Combining the raycasting approach with Monte Carlo estimates we can approximate the areas and volumes effectively:
julia> A, V = mc_volumes(P, 1000) # cast 1000 Monte Carlo rays per cellIf the simplicial complex of vertices is already known we can speed up the process:
julia> A, V = mc_volumes(v, P, 1000) # use the neighbourhood infromation contained in vWe furthermore can integrate any function f over a cell i and its boundaries:
julia> y, δy, V, A = mc_integrate(x->x^2, 1, P, 100, 10) # integrate cell 1 with 100 boundary and 100*10 volume samplesHere y and the vector δy contain the integrals over the cell and its boundaries. V and A get computed as a byproduct.
[1] V. Polianskii, F. T. Pokorny - Voronoi Graph Traversal in High Dimensions with Applications to Topological Data Analysis and Piecewise Linear Interpolation (2020, Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining)
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