R/trend_base.R

In CollinErickson/GauPro: Gaussian Process Fitting

# Trend functions should implement:
# mu prediction for new matrix
# update_params
# get_optim_functions: return optim.func, optim.grad, optim.fngr
# param_optim_lower - lower bound of params
# param_optim_upper - upper
# param_optim_start - current param values
# param_optim_start0 - some central param values that can be used for optimization restarts
# param_optim_jitter - how to jitter params in optimization




#' Trend R6 class
#'
#' @docType class
#' @importFrom R6 R6Class
# @export
#' @useDynLib GauPro, .registration = TRUE
#' @importFrom Rcpp evalCpp
#' @importFrom stats optim
# @keywords data, kriging, Gaussian process, regression
#' @return Object of \code{\link{R6Class}} with methods for fitting GP model.
#' @format \code{\link{R6Class}} object.
#' @field D Number of input dimensions of data
#' @examples
#' #k <- GauPro_trend$new()
GauPro_trend <- R6::R6Class(
  classname = "GauPro_trend",
  public = list(
    # Gradient is -2 * t(yminusmu) %*% Siginv %*% du/db
    D = NULL
  ),
  private = list(

  )
)