kriging.quantile: Kriging quantile

In DiceOptim: Kriging-Based Optimization for Computer Experiments

Description

Evaluation of a kriging quantile a a new point. To be used in an optimization loop.

Usage

kriging.quantile(x, model, beta = 0.1, type = "UK", envir = NULL)

Arguments

x	the input vector at which one wants to evaluate the criterion
model	a Kriging model of "km" class
beta	Quantile level (default value is 0.1)
type	Kriging type: "SK" or "UK"
envir	an optional environment specifying where to assign intermediate values for future gradient calculations. Default is NULL.

Value

Kriging quantile

Author(s)

Victor Picheny

David Ginsbourger

Examples

##########################################################################
###    KRIGING QUANTILE SURFACE                                       ####
### OF THE BRANIN FUNCTION KNOWN AT A 12-POINT LATIN HYPERCUBE DESIGN ####
##########################################################################

set.seed(421)

# Set test problem parameters
doe.size <- 12
dim <- 2
test.function <- get("branin2")
lower <- rep(0,1,dim)
upper <- rep(1,1,dim)
noise.var <- 0.2

# Generate DOE and response
doe <- as.data.frame(matrix(runif(doe.size*dim),doe.size))
y.tilde <- rep(0, 1, doe.size)
for (i in 1:doe.size)  {
y.tilde[i] <- test.function(doe[i,]) + sqrt(noise.var)*rnorm(n=1)
}
y.tilde <- as.numeric(y.tilde)

# Create kriging model
model <- km(y~1, design=doe, response=data.frame(y=y.tilde),
            covtype="gauss", noise.var=rep(noise.var,1,doe.size), 
	    lower=rep(.1,dim), upper=rep(1,dim), control=list(trace=FALSE))

# Compute actual function and criterion on a grid
n.grid <- 12 # Change to 21 for a nicer picture
x.grid <- y.grid <- seq(0,1,length=n.grid)
design.grid <- expand.grid(x.grid, y.grid)
nt <- nrow(design.grid)

crit.grid <- apply(design.grid, 1, kriging.quantile, model=model, beta=.1)
func.grid <- apply(design.grid, 1, test.function)

# Compute kriging mean and variance on a grid
names(design.grid) <- c("V1","V2")
pred <- predict(model, newdata=design.grid, type="UK", checkNames = FALSE)
mk.grid <- pred$m
sk.grid <- pred$sd

# Plot actual function
z.grid <- matrix(func.grid, n.grid, n.grid)
filled.contour(x.grid,y.grid, z.grid, nlevels=50, color = rainbow,
plot.axes = {title("Actual function");
points(model@X[,1],model@X[,2],pch=17,col="blue"); 
axis(1); axis(2)})

# Plot Kriging mean
z.grid <- matrix(mk.grid, n.grid, n.grid)
filled.contour(x.grid,y.grid, z.grid, nlevels=50, color = rainbow,
plot.axes = {title("Kriging mean");
points(model@X[,1],model@X[,2],pch=17,col="blue"); 
axis(1); axis(2)})

# Plot Kriging variance
z.grid <- matrix(sk.grid^2, n.grid, n.grid)
filled.contour(x.grid,y.grid, z.grid, nlevels=50, color = rainbow,
plot.axes = {title("Kriging variance");
points(model@X[,1],model@X[,2],pch=17,col="blue"); 
axis(1); axis(2)})

# Plot kriging.quantile criterion
z.grid <- matrix(crit.grid, n.grid, n.grid)
filled.contour(x.grid,y.grid, z.grid, nlevels=50, color = rainbow,
plot.axes = {title("kriging.quantile");
points(model@X[,1],model@X[,2],pch=17,col="blue"); 
axis(1); axis(2)})

DiceOptim documentation built on Feb. 2, 2021, 1:06 a.m.