CPFs {eaf} | R Documentation |
Conditional Pareto fronts obtained from Gaussian processes simulations.
Description
The data has the only goal of providing an example of use of vorobT()
and
vorobDev()
. It has been obtained by fitting two Gaussian processes on 20
observations of a bi-objective problem, before generating conditional
simulation of both GPs at different locations and extracting non-dominated
values of coupled simulations.
Usage
CPFs
Format
A data frame with 2967 observations on the following 3 variables.
f1
first objective values.
f2
second objective values.
set
indices of corresponding conditional Pareto fronts.
Source
M Binois, D Ginsbourger, O Roustant (2015). “Quantifying uncertainty on Pareto fronts with Gaussian process conditional simulations.” European Journal of Operational Research, 243(2), 386–394. doi: 10.1016/j.ejor.2014.07.032.
Examples
data(CPFs)
res <- vorobT(CPFs, reference = c(2, 200))
eafplot(CPFs[,1:2], sets = CPFs[,3], percentiles = c(0, 20, 40, 60, 80, 100),
col = gray(seq(0.8, 0.1, length.out = 6)^2), type = "area",
legend.pos = "bottomleft", extra.points = res$VE, extra.col = "cyan")
[Package eaf version 2.5 Index]