hypervolume_thin: Reduces the number of random points in a hypervolume

Description Usage Arguments Details Value Examples

View source: R/hypervolume_thin.R

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

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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

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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

hypervolume documentation built on Nov. 17, 2017, 6:21 a.m.