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# control functions for subsemble
# These are modified versions of the control.R functions from SuperLearner
# Original version of function created by Eric Polley on 2011-01-03.
.cv_control <- function(V = 10L, stratifyCV = TRUE, shuffle = TRUE){
# Parameters that control the CV process
# Output used in SuperLearner::CVFolds
# Make sure V is an integer
V <- as.integer(V)
if(!is.logical(stratifyCV)) {
stop("'stratifyCV' must be logical")
}
if(!is.logical(shuffle)) {
stop("'shuffle' must be logical")
}
return(list(V = V, stratifyCV = stratifyCV, shuffle = shuffle))
}
.sub_control <- function(J = 3L, stratifyCV = TRUE, shuffle = TRUE, supervision = NULL){
# Parameters that control the data partitioning process
# Output used in SuperLearner::CVFolds
# J is the number of unique data partitions/subsets
ctrl <- .cv_control(V=J, stratifyCV=stratifyCV, shuffle=shuffle)
if (!is.null(supervision)){
stop("Supervised Subsemble is not yet implemented. Check back in a future release.")
}
ctrl[["supervision"]] <- supervision
return(ctrl)
}
.learn_control <- function(multiType = "crossprod"){
# Parameters that control the learning process
# If there are multiple learners, "crossprod" will create
# an ensemble of K models, where K = J x length(learner)
if (!(multiType %in% c("crossprod","divisor"))){
stop("'multiType' must be equal to 'crossprod' or 'divisor'")
}
return(list(multiType = multiType))
}
.gen_control <- function(saveFits = TRUE){
# General control parameters
return(list(saveFits = saveFits))
}
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