hypervolume (version 2.0.12)

hypervolume_thin: Reduces the number of random points in a hypervolume

Description

Many hypervolume algorithms have computational complexities that scale with the number of random points used to characterize a hypervolume (@RandomPoints). This value can be reduced to improve runtimes at the cost of lower resolution.

Usage

hypervolume_thin(hv, factor = NULL, num.points = NULL)

Arguments

hv

An object of class Hypervolume

factor

A number in (0,1) describing the fraction of random points to keep.

num.points

A number describing the number random points to keep.

Value

A Hypervolume object

Details

Either factor or npoints (but not both) must be specified.

Examples

# NOT RUN {
data(iris)
hv1 = hypervolume_gaussian(subset(iris, Species=="setosa")[,1:3])

# downsample to 1000 random points
hv1_thinned = hypervolume_thin(hv1, num.points=1000)
hv1_thinned
# }