Nothing
timse_optim_parallel <- function(x, integration.points,integration.weights=NULL,
intpoints.oldmean=NULL,intpoints.oldsd=NULL,precalc.data,
model, T=NULL, new.noise.var=0,weight=NULL,batchsize,current.timse){
if(!is.null(new.noise.var)){
if(new.noise.var == 0) {
new.noise.var <- NULL
}
}
#x is a vector of size d * batchsize
d <- model@d
n <- model@n
X.new <- matrix(x,nrow=d)
mindist <- Inf
tp1 <- c(as.numeric(t(model@X)),x)
for (i in 1:batchsize){
#distance between the i^th point and all other points (in the DOE or in the batch)
xx <- X.new[,i]
tp2<-matrix(tp1-as.numeric(xx),ncol=d,byrow=TRUE)^2
mysums <- sqrt(rowSums(tp2))
mysums[n+i] <- Inf #because this one is always equal to zero
mindist <- min(mindist,mysums)
}
if (!identical(colnames(integration.points), colnames(model@X))) colnames(integration.points) <- colnames(model@X)
if ((mindist > 1e-5) || (!is.null(new.noise.var))){
X.new <- t(X.new)
krig <- predict_nobias_km(object=model, newdata=as.data.frame(X.new),
type="UK",se.compute=TRUE, cov.compute=TRUE)
mk <- krig$mean ; sk <- krig$sd ; newXvar <- sk*sk
F.newdata <- krig$F.newdata ; c.newdata <- krig$c;Sigma.r <- krig$cov
kn = computeQuickKrigcov(model,integration.points,X.new,precalc.data, F.newdata , c.newdata)
krig2 <- predict_update_km_parallel (newXmean=mk,newXvar=newXvar,newXvalue=mk,
Sigma.r=Sigma.r,newdata.oldmean=intpoints.oldmean,newdata.oldsd=intpoints.oldsd,kn=kn)
if(!is.null(krig2$error)) return(current.timse)
sk.new <- krig2$sd
if(is.null(weight)){
tmse <- sk.new^2
}else{
tmse <- weight * sk.new^2
}
if (is.null(integration.weights)) {crit <- mean(tmse)
}else crit <- sum(tmse*integration.weights)
}else crit <- current.timse * 1.01
return(crit)
}
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