Learn R Programming

DiceOptim (version 2.0)

fastfun: Fastfun function

Description

Modification of an R function to be used as 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 match.fun,
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

Run this code
########################################################
## 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)

Run the code above in your browser using DataLab