hypervolume (version 1.4.1)

hypervolume_inclusion_test: Inclusion test

Description

Determines if a set of points are within a hypervolume.

Usage

hypervolume_inclusion_test(hv, points, reduction_factor = 1, 
  verbose = T, distance_factor = 1)

Arguments

hv

n-dimensional hypervolume to compare against

points

Candidate points. A m x n matrix or dataframe, where m is the number of candidate points and n is the number of dimensions.

reduction_factor

A number in (0,1] that represents the fraction of random points sampled from the hypervolume for the stochastic inclusion test. Larger values are more accurate but computationally slower.

verbose

Logical value; print diagnostic output if true.

distance_factor

Numeric value; multiplicative factor applied to the critical distance for all inclusion tests (see below). Recommended to not change this parameter.

Value

A m x 1 logical vector indicating whether each candidate point is in the hypervolume.

Details

Tests if a point is in a hypervolume by determining if it is within a characteristic distance of at least one point from a uniformly random sample of the hypervolume. Because this algorithm is based on distances calculated between the distributions of random points, the critical distance (point density ^ (-1/n)) can be scaled by a user-specified factor to provide more or less liberal estimates (distance_factor greater than or less than 1).

Examples

# NOT RUN {
# construct a hypervolume of points in the unit square [0,1] x [0,1]
data = data.frame(x=runif(100,min=0,max=1), y=runif(100,min=0,max=1))
hv = hypervolume(data, reps=1000, bandwidth=0.1)

# test if (0.5,0.5) and (-1,1) are in - should return TRUE FALSE
hypervolume_inclusion_test(hv, points=data.frame(x=c(0.5,-1),y=c(0.5,-1)))
# }