R/8_precalculated_Xfgpm_objects.R

In funGp: Gaussian Process Models for Scalar and Functional Inputs

#' @name precalculated_Xfgpm_objects
#' @aliases xm xm25 xmc xmh xms
#'
#' @keywords data
#' @docType data
#' @title Precalculated Xfgpm objects
#'
#' @description A dataset containing the results of the application of
#'     \code{fgpm_factory} to \code{fgp_BB7} analytic black-box
#'     function. See \bold{Examples} for details.
#'
#' @format Five objects of class \code{"Xfgpm"}:
#' \describe{
#'   \item{xm}{With 32 training points and default parameters.}
#'   \item{xm25}{With 32 training points and 25 iterations of the algorithm.}
#'   \item{xmc}{With 32 training points and customized solution space.}
#'   \item{xmh}{With 32 training points and customized heuristic parameters.}
#'   \item{xms}{With 32 training points and a time budget constraint and large number of iterations.}
#' }
#'
#' @examples
#'
#'\dontrun{
#'
#' ##################################################################
#' ## Construction of xm object with default parameters (~12 seconds)
#' ##################################################################
#' set.seed(100)
#' n.tr <- 32
#' x1 <- x2 <- x3 <- x4 <- x5 <- seq(0,1,length = n.tr^(1/5))
#' sIn <- expand.grid(x1 = x1, x2 = x2, x3 = x3, x4 = x4, x5 = x5)
#' fIn <- list(f1 = matrix(runif(n.tr * 10), ncol = 10),
#'             f2 = matrix(runif(n.tr * 22), ncol = 22))
#' sOut <- fgp_BB7(sIn, fIn, n.tr)
## Not run:
# optimizing the model structure with fgpm_factory (~12 seconds)
#' xm <- fgpm_factory(sIn = sIn, fIn = fIn, sOut = sOut)
#'
#' ##################################################################
#' ## Construction of xm25 object with 25 iterations (~20 seconds)
#' ##################################################################
#' xm25 <- fgpm_factory(sIn = sIn, fIn = fIn, sOut = sOut,
#'                      setup = list(n.iter = 25))
#'
#' ##################################################################
#' ## Construction of xmc object with customized solution space (~12 seconds)
#' ##################################################################
#' myctr <- list(s_keepOn = c(1,2), # keep both scalar inputs always on
#' f_keepOn = c(2), # keep f2 always active
#' f_disTypes = list("2" = c("L2_byindex")), # only use L2_byindex distance for f2
#' f_fixDims = matrix(c(2,4), ncol = 1), # f2 projected in dimension 4
#' f_maxDims = matrix(c(1,5), ncol = 1), # f1 projected in dimension max 5
#' f_basTypes = list("1" = c("B-splines")), # only use B-splines projection for f1
#' kerTypes = c("matern5_2", "gauss")) # test only Matern 5/2 and Gaussian kernels
#' xmc <- fgpm_factory(sIn = sIn, fIn = fIn, sOut = sOut, ctraints = myctr)
#'
#' ##################################################################
#' ## Construction of xmc object with customized heuristic parameters (~15 seconds)
#' ##################################################################
#' mysup <- list(n.iter = 30, n.pop = 12, tao0 = .15, dop.s = 1.2,
#'               dop.f = 1.3, delta.f = 4, dispr.f = 1.1, q0 = .85,
#'               rho.l = .2, u.gbest = TRUE, n.ibest = 2, rho.g = .08)
#' xmh <- fgpm_factory(sIn = sIn, fIn = fIn, sOut = sOut, setup = mysup)
#'
#' ##################################################################
#' ## Construction of xmc object with time budget constraint (~60 seconds)
#' ##################################################################
#' mysup <- list(n.iter = 2000)
#' mytlim <- 60
#' xms <- fgpm_factory(sIn = sIn, fIn = fIn, sOut = sOut,
#'                     setup = mysup, time.lim = mytlim)
#'
#' }
#'
NULL
# "precomp_Xfgpm"