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#Class implementing a Classification Algorithm
#Implements the PUBLIC-C KEEL classification algorithm
PUBLIC_C <- function(train, test, nodesBetweenPrune=25, estimateToPrune="PUBLIC(1)"){
alg <- RKEEL::R6_PUBLIC_C$new()
alg$setParameters(train, test, nodesBetweenPrune, estimateToPrune)
return (alg)
}
R6_PUBLIC_C <- R6::R6Class("R6_PUBLIC_C",
inherit = ClassificationAlgorithm,
public = list(
#Public properties
#nodes between prune
nodesBetweenPrune = 25,
#estimate to prune
estimateToPrune = "PUBLIC(1)",
#Public functions
#Initialize function
setParameters = function(train, test, nodesBetweenPrune=25,
estimateToPrune="PUBLIC(1)"){
super$setParameters(train, test)
self$nodesBetweenPrune <- nodesBetweenPrune
if((tolower(estimateToPrune) == "public(1)") || (tolower(estimateToPrune) == "public(s)") ||
(tolower(estimateToPrune) == "public(v)")) {
self$estimateToPrune <- toupper(estimateToPrune)
}
else {
#default value of estimate to prune
self$estimateToPrune <- "PUBLIC(1)"
}
}
),
private = list(
#Private properties
#jar Filename
jarName = "PUBLIC.jar",
#algorithm name
algorithmName = "PUBLIC-C",
#String with algorithm name
algorithmString = "PUBLIC",
#Private functions
#Get the text with the parameters for the config file
getParametersText = function(){
text <- ""
text <- paste0(text, "nodesBetweenPrune = ", self$nodesBetweenPrune, "\n")
text <- paste0(text, "estimateToPrune = ", self$estimateToPrune, "\n")
return(text)
}
)
)
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