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#Class implementing a Classification Algorithm
#Implements the NNEP-C of KEEL
NNEP_C <- function(train, test, hidden_nodes=4, transfer="Product_Unit", generations=200, seed=-1){
alg <- RKEEL::R6_NNEP_C$new()
alg$setParameters(train, test, hidden_nodes, transfer, generations, seed)
return (alg)
}
R6_NNEP_C <- R6::R6Class("R6_NNEP_C",
inherit = ClassificationAlgorithm,
public = list(
#Public properties
#hidden_nodes: num of hidden nodes in the neural network
hidden_nodes = 4,
#transfer: type of nodes transfer functions
transfer = "Product_Unit",
#generations: max number of generations
generations = 200,
#seed
seed = -1,
#Public functions
#Initialize function
setParameters = function(train, test, hidden_nodes=4, transfer="Product_Unit",
generations=200, seed=-1){
super$setParameters(train, test)
self$hidden_nodes <- hidden_nodes
if((tolower(transfer) == "product_unit") || (tolower(transfer) == "sigmoidal")){
self$transfer = transfer
}
else{
#Default value for transfer function
self$transfer <- "Product_Unit"
}
self$generations <- generations
if(seed == -1) {
self$seed <- sample(1:1000000, 1)
}
else {
self$seed <- seed
}
}
),
private = list(
#Private properties
#jar Filename
jarName = "NNEP_Clas.jar",
#algorithm name
algorithmName = "NNEP-C",
#String with algorithm name
algorithmString = "Clas-NNEP",
#Private functions
#Get the text with the parameters for the config file
getParametersText = function(){
text <- ""
text <- paste0(text, "seed = ", self$seed, "\n")
text <- paste0(text, "Hidden_nodes = ", self$hidden_nodes, "\n")
text <- paste0(text, "Transfer = ", self$transfer, "\n")
text <- paste0(text, "Generations = ", self$generations, "\n")
return(text)
}
)
)
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