Nothing
vorobVol_optim_parallel <- function(x, integration.points,integration.weights=NULL,
intpoints.oldmean,intpoints.oldsd,precalc.data,
model, T, new.noise.var=NULL,batchsize,alpha,current.crit,typeEx=">"){
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 usually 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.crit)
sk.new <- krig2$sd
a <- (intpoints.oldmean-T) / sk.new
a[a==Inf]<- 1000 ;a[a== -Inf] <- -1000;a[is.nan(a)] <- 1000
c <- (intpoints.oldsd*intpoints.oldsd)/(sk.new*sk.new)
c[c==Inf]<- 1000; c[is.nan(c)] <- 1000
arg=0
if(typeEx==">"){
arg <- as.numeric((qnorm(alpha) - a)/sqrt(c-1))
}else{
arg <- as.numeric((qnorm(alpha) + a)/sqrt(c-1))
}
result <- pnorm(-arg)
if (is.null(integration.weights)) {crit <- mean(result)
}else crit <- sum(result*integration.weights)
}else crit <- current.crit + 0.01
return(crit)
}
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