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Fixing qEI.grad.R to work with 1d data #2

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Fixing qEI.grad.R to work with 1d data #2

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23a2265
Fixing qEI.grad.R to work with 1d data
nrontsis Oct 11, 2017
10fe2da
Fix for dim=1, batch_size=2
nrontsis Oct 11, 2017
6bca38e
Fix call to EI.grad (fixes the case of batch_size=1)
nrontsis Oct 11, 2017
62e72c7
Fixing 1d case for EI.grad
nrontsis Oct 11, 2017
643c7f6
Fix for dimensions > 1
nrontsis Oct 23, 2017
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4 changes: 2 additions & 2 deletions R/EI.grad.R
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Original file line number Diff line number Diff line change
Expand Up @@ -158,7 +158,7 @@ EI.grad <- function(x, model, plugin=NULL, type="UK", minimization = TRUE, envir
} else
{ # Compute derivatives of the covariance and trend functions
dc <- covVector.dx(x=newdata.num, X=X, object=covStruct, c=c)
f.deltax <- trend.deltax(x=newdata.num, model=model)
f.deltax <- matrix(trend.deltax(x=newdata.num, model=model), ncol=d)

# Compute gradients of the kriging mean and variance
W <- backsolve(t(T), dc, upper.tri=FALSE)
Expand All @@ -179,4 +179,4 @@ EI.grad <- function(x, model, plugin=NULL, type="UK", minimization = TRUE, envir
}
########################################################################################
return(ei.grad)
}
}
6 changes: 3 additions & 3 deletions R/qEI.grad.R
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Expand Up @@ -119,11 +119,11 @@ krigingDeriv <- function(x, model, type="UK", envir=NULL){
f.deltax <- trend.deltax(x=newdata.num[k,], model=model)
# Compute gradients of the kriging mean at point k
W <- backsolve(t(T), dc, upper.tri=FALSE)
kriging.mean.jacob[k,,k] <- t(z)%*%W + model@trend.coef%*%f.deltax
kriging.mean.jacob[k,,k] <- t(z)%*%W + model@trend.coef%*%matrix(f.deltax, ncol=d)
# Compute gradients of the kriging covariance between point k and point l
for (l in 1:q) {
# Compute gradients of the kernel between point k and point l
ker.grad <- covVector.dx(x=newdata.num[k,], X=newdata[l,],
ker.grad <- covVector.dx(x=newdata.num[k,], X=matrix(newdata[l,], ncol=d),
object=covStruct, c=covM[k,l])
kriging.cov.jacob[k,,k,l] <- ker.grad - t(v[,l])%*%W
if (type=="UK") {
Expand Down Expand Up @@ -296,7 +296,7 @@ qEI.grad <- function(x, model, plugin=NULL, type="UK", minimization = TRUE, fast
if (!minimization) {
stop("qEI.grad doesn't work in \'minimization = FALSE\' when dim = 1 (in progress).")
}
return(EI.grad(x,model,plugin,type,envir))
return(EI.grad(x,model,plugin,type,minimization,envir))
}

if(!is.null(envir)) {
Expand Down