hypervolume (version 1.1.1)

hypervolume_set: Set operations (intersection / union / unique components)

Description

Computes the intersection, union, and unique components of two hypervolumes.

Usage

hypervolume_set(hv1, hv2, npoints_max = NULL, verbose = TRUE, check_memory = TRUE)

Arguments

hv1

A n-dimensional hypervolume

hv2

A n-dimensional hypervolume

npoints_max

Maximum number of random points to use for set operations. If NULL defaults to 10*10^sqrt(n) where n is the dimensionality of the input hypervolumes.

verbose

Logical value; print diagnostic output if true.

check_memory

Logical value; returns information about expected memory usage if true.

Value

• If {check_memory} is false, returns a HypervolumeList object, with four items in its HVList slot:
• Intersection
  The intersection of hv1 and hv2
• Union
  The union of hv1 and hv2
• Unique_1
  The unique component of hv1 relative to hv2
• Unique_2
  The unique component of hv2 relative to hv1
• Note that the output hypervolumes will have lower random point densities than the input hypervolumes. You may find it useful to define a Jaccard-type fractional overlap between hv1 and hv2 as {hv_set@HVList$Intersection@Volume / hv_set@HVList$Union@Volume}. If {check_memory} is true, instead returns a scalar with the expected number of pairwise comparisons.

Details

Uses the inclusion test approach to identify points in the first hypervolume that are or are not within the second hypervolume and vice-versa. The intersection is the points in both hypervolumes, the union those in either hypervolume, and the unique components the points in one hypervolume but not the other.

By default, the function uses {check_memory=TRUE} which will provide an estimate of the computational cost of the set operations. The function should then be re-run with {check_memory=FALSE} if the cost is acceptable. This algorithm's memory and time cost scale quadratically with the number of input points, so large datasets can have disproportionately high costs. This error-checking is intended to prevent the user from large accidental memory allocation.

The computation is actually performed on a random sample from both input hypervolumes, constraining each to have the same point density given by the minimum of the point density of each input hypervolume, and the point density calculated using the volumes of each input hypervolume divided by npoints_max.

Examples

data(iris)
hv1 = hypervolume(subset(iris, Species=="setosa")[,1:4],reps=1000,bandwidth=0.2,warn=FALSE)
hv2 = hypervolume(subset(iris, Species=="virginica")[,1:4],reps=1000,bandwidth=0.2,warn=FALSE)

hv_set = hypervolume_set(hv1, hv2, check_memory=FALSE)

# no overlap found
get_volume(hv_set)