Description Usage Arguments Value Examples
Global optimization using the Universal Prediction distribution
1 2 |
model |
the surrogate model |
fun |
the real function |
nsteps |
the number of points to be generated |
lower |
the lower bound of the design space |
upper |
the upper bound of the design space |
seed |
the random seed (default = 1) |
parinit |
inital points to be used in the optimization (default NULL) |
control |
the optimization control parameters (default NULL) |
EEIcontrol |
the optimization criterion parameters (default NULL) |
list of generated points and their values and the last updated surrogate model
1 2 3 4 5 6 7 8 9 10 | #' library(UP)
d <- 2;
n <- 16
X <- expand.grid(x1=s <- seq(0,1, length=5), x2=s)
Y <- apply(X, 1, branin)
upsm <- UPSM$new(sm= krigingsm$new(), UP=UPClass$new(X,Y,Scale =TRUE) )
upego_res <- upego( upsm,fun = branin, nsteps = 1, lower= c(0,0),upper = c(1,1) )
print( min(upego_res$last$get_DOE()$y ) )
|
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.