upsmart: UP-SMART

In malekbs/UP: What the package does (short line)

Description

Enhance the overall prediction capabilities of a surrogate model by the Universal Prediction distribution based Surrogate Modeling Adapative Refinement Strategy UP-SMART

Usage

upsmart(model, fun, nsteps, lower, upper, seed = 1, parinit = NULL,
  control = NULL, RefControl = NULL)

Arguments

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)
RefControl	the refienement criterion parameters (default NULL)

Value

list of generated points and their values and the last updated surrogate model

Examples

#' library(UP)
d            <- 2;
n            <- 16
X            <- expand.grid(x1=s <- seq(0,1, length=5), x2=s)
Xtest        <- expand.grid(x1=seq(0,1,length=5), x2=seq(0,1,length=4))
Y            <- apply(X, 1, branin)
sm           <- krigingsm$new()
sm$setDOE(X,Y)
sm$train() 
upsmart_res  <- upsmart(sm,fun = branin,nsteps = 5, lower= c(0,0),upper = c(1,1))

print(upsmart_res$last$get_DOE())

malekbs/UP documentation built on May 14, 2019, 8:05 a.m.