Last chance! 50% off unlimited learning
Sale ends in
This function fits a Gaussian process (GP) model to a set of possible designs and their corresponding design criterion evaluations. This GP will then be used to optimize the design criterion using an expected improvement criterion.
gp_fit(
design,
response,
options = list(formula = ~1, kernel = "matern5_2", optimizer = "gen", nuggetUse =
TRUE)
)
A matrix of n
rows and d
columns.
A column vector of length n
.
A list specifying the type of GP model to fit (see km
).
A list of properties from class km
(see km-class
).
# NOT RUN {
x <- matrix(seq(-1,1,0.3),ncol=1)
y <- x^2
gp <- gp_fit(x,
y,
options=list(formula=~1,
kernel = "matern5_2",
optimizer = "gen",
nuggetUse = FALSE))
gp_plot(gp$km.model,seq(-1.5,1.5,0.1))
# }
Run the code above in your browser using DataLab