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#Class implementing a Regression Algorithm
#Implements the M5Rules-R KEEL regression algorithm
M5Rules_R <- function(train, test, pruningFactor=2, heuristic="Coverage"){
alg <- RKEEL::R6_M5Rules_R$new()
alg$setParameters(train, test, pruningFactor, heuristic)
return (alg)
}
R6_M5Rules_R <- R6::R6Class("R6_M5Rules_R",
inherit = RegressionAlgorithm,
public = list(
#Public properties
#Pruning factor
pruningFactor = 2,
#Heuristic
heuristic = "Coverage",
#Public functions
#Initialize function
setParameters = function(train, test, pruningFactor=2, heuristic="Coverage"){
super$setParameters(train, test)
if(tolower(heuristic) == "coverage"){
self$heuristic <- "Coverage"
}
else if((tolower(heuristic) == "rms") || (tolower(heuristic) == "mae") ||
(tolower(heuristic) == "cc")){
self$heuristic <- toupper(heuristic)
}
else{
#Default value of heuristic
self$heuristic <- "Coverage"
}
self$pruningFactor <- pruningFactor
}
),
private = list(
#Private properties
#jar Filename
jarName = "M5Rules.jar",
#algorithm name
algorithmName = "M5Rules-R",
#String with algorithm name
algorithmString = "M5Rules",
#Private functions
#Get the text with the parameters for the config file
getParametersText = function(){
text <- ""
text <- paste0(text, "pruningFactor = ", self$pruningFactor, "\n")
text <- paste0(text, "verbosity = 0", "\n")
text <- paste0(text, "heuristic = ", self$heuristic, "\n")
return(text)
}
)
)
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