hypervolume_thin: Reduces the number of random points in a hypervolume

In dc165/Hypervolume-Dev: High Dimensional Geometry and Set Operations Using Kernel Density Estimation, Support Vector Machines, and Convex Hulls

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.

Details

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

Value

A Hypervolume object

Examples

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

dc165/Hypervolume-Dev documentation built on Dec. 13, 2020, 6:02 p.m.