#tsee_optim
set.seed(9)
N <- 20 #number of observations
T <- 80 #threshold
testfun <- branin
#a 20 points initial design
design <- data.frame( matrix(runif(2*N),ncol=2) )
response <- testfun(design)
#km object with matern3_2 covariance
#params estimated by ML from the observations
model <- km(formula=~., design = design,
response = response,covtype="matern3_2")
x <- c(0.5,0.4)#one evaluation of the tsee criterion
tsee_optim(x=x,T=T,model=model)
n.grid <- 20 #you can run it with 100
x.grid <- y.grid <- seq(0,1,length=n.grid)
x <- expand.grid(x.grid, y.grid)
tsee.grid <- tsee_optim(x=x,T=T,model=model)
z.grid <- matrix(tsee.grid, n.grid, n.grid)
#plots: contour of the criterion, doe points and new point
image(x=x.grid,y=y.grid,z=z.grid,col=grey.colors(10))
contour(x=x.grid,y=y.grid,z=z.grid,25,add=TRUE)
points(design, col="black", pch=17, lwd=4,cex=2)
i.best <- which.max(tsee.grid)
points(x[i.best,], col="blue", pch=17, lwd=4,cex=3)
#plots the real (unknown in practice) curve f(x)=T
testfun.grid <- apply(x,1,testfun)
z.grid.2 <- matrix(testfun.grid, n.grid, n.grid)
contour(x.grid,y.grid,z.grid.2,levels=T,col="blue",add=TRUE,lwd=5)
title("Contour lines of tsee criterion (black) and of f(x)=T (blue)")
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