fastfun {GPareto} | R Documentation |
Fast-to-evaluate function wrapper
Description
Modification of an R function to be used with methods predict
and update
(similar to a km
object).
It creates an S4 object which contains the values corresponding to evaluations of other costly observations.
It is useful when an objective can be evaluated fast.
Usage
fastfun(fn, design, response = NULL)
Arguments
fn |
the evaluator function, found by a call to |
design |
a data frame representing the design of experiments. The ith row contains the values of the d input variables corresponding to the ith evaluation. |
response |
optional vector (or 1-column matrix or data frame) containing the values of the 1-dimensional output given by the objective function at the design points. |
Value
An object of class fastfun-class
.
Examples
########################################################
## Example with a fast to evaluate objective
########################################################
## Not run:
set.seed(25468)
library(DiceDesign)
d <- 2
fname <- P1
n.grid <- 21
nappr <- 11
design.grid <- maximinESE_LHS(lhsDesign(nappr, d, seed = 42)$design)$design
response.grid <- t(apply(design.grid, 1, fname))
Front_Pareto <- t(nondominated_points(t(response.grid)))
mf1 <- km(~., design = design.grid, response = response.grid[,1])
mf2 <- km(~., design = design.grid, response = response.grid[,2])
model <- list(mf1, mf2)
nsteps <- 5
lower <- rep(0, d)
upper <- rep(1, d)
# Optimization reference: SMS with discrete search
optimcontrol <- list(method = "pso")
omEGO1 <- GParetoptim(model = model, fn = fname, crit = "SMS", nsteps = nsteps,
lower = lower, upper = upper, optimcontrol = optimcontrol)
print(omEGO1$par)
print(omEGO1$values)
plot(response.grid, xlim = c(0,300), ylim = c(-40,0), pch = 17, col = "blue")
points(omEGO1$values, pch = 20, col ="green")
# Optimization with fastfun: SMS with discrete search
# Separation of the problem P1 in two objectives:
# the first one to be kriged, the second one with fastobj
f1 <- function(x){
if(is.null(dim(x))) x <- matrix(x, nrow = 1)
b1 <- 15*x[,1] - 5
b2 <- 15*x[,2]
return( (b2 - 5.1*(b1/(2*pi))^2 + 5/pi*b1 - 6)^2 +10*((1 - 1/(8*pi))*cos(b1) + 1))
}
f2 <- function(x){
if(is.null(dim(x))) x <- matrix(x, nrow = 1)
b1<-15*x[,1] - 5
b2<-15*x[,2]
return(-sqrt((10.5 - b1)*(b1 + 5.5)*(b2 + 0.5))
- 1/30*(b2 - 5.1*(b1/(2*pi))^2 - 6)^2
- 1/3*((1 - 1/(8*pi))*cos(b1) + 1))
}
optimcontrol <- list(method = "pso")
model2 <- list(mf1)
omEGO2 <- GParetoptim(model = model2, fn = f1, cheapfn = f2, crit = "SMS", nsteps = nsteps,
lower = lower, upper = upper, optimcontrol = optimcontrol)
print(omEGO2$par)
print(omEGO2$values)
points(omEGO2$values, col = "red", pch = 15)
## End(Not run)
[Package GPareto version 1.1.8 Index]