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#Class implementing a Regression Algorithm
#Implements the MLP-BP-R KEEL regression algorithm
MLP_BP_R <- function(train, test, hidden_layers=2, hidden_nodes=15, transfer="Htan", eta=0.15, alpha=0.1, lambda=0.0, test_data=TRUE, validation_data=FALSE, cross_validation=FALSE, cycles=10000, improve=0.01, tipify_inputs=TRUE, save_all=FALSE, seed=-1){
alg <- RKEEL::R6_MLP_BP_R$new()
alg$setParameters(train, test, hidden_layers, hidden_nodes, transfer, eta, alpha, lambda, test_data, validation_data, cross_validation, cycles, improve, tipify_inputs, save_all, seed)
return (alg)
}
R6_MLP_BP_R <- R6::R6Class("R6_MLP_BP_R",
inherit = RegressionAlgorithm,
public = list(
#Public properties
#Hidden layers
hidden_layers = 2,
#Hidden nodes
hidden_nodes = 15,
#Transfer function
transfer = "Htan",
#Eta
eta = 0.15,
#Alpha
alpha = 0.1,
#Lambda
lambda = 0.0,
#Test data?
test_data = TRUE,
#Validation data?
validation_data = FALSE,
#Cross-validation?
cross_validation = FALSE,
#Cycles
cycles = 10000,
#Improve
improve = 0.01,
#Tipify inputs?
tipify_inputs = TRUE,
#Save all?
save_all = FALSE,
#seed
seed = -1,
#Public functions
#Initialize function
setParameters = function(train, test,
hidden_layers=2, hidden_nodes=15, transfer="Htan",
eta=0.15, alpha=0.1, lambda=0.0, test_data=TRUE,
validation_data=FALSE, cross_validation=FALSE,
cycles=10000, improve=0.01, tipify_inputs=TRUE,
save_all=FALSE, seed=-1){
super$setParameters(train, test)
self$hidden_layers <- hidden_layers
self$hidden_nodes <- hidden_nodes
if(tolower(transfer) == "htan"){
self$transfer = "Htan"
}
else if(tolower(transfer) == "log"){
self$transfer = "Log"
}
else if(tolower(transfer) == "lin"){
self$transfer = "Lin"
}
else{
#Default value for transfer function
self$transfer <- "Htan"
}
self$eta <- eta
self$alpha <- alpha
self$lambda <- lambda
self$test_data <- test_data
self$validation_data <- validation_data
self$cross_validation <- cross_validation
self$cycles <- cycles
self$improve <- improve
self$tipify_inputs <- tipify_inputs
self$save_all <- save_all
if(seed == -1) {
self$seed <- sample(1:1000000, 1)
}
else {
self$seed <- seed
}
}
),
private = list(
#Private properties
#jar Filename
jarName = "Regr-MLP-BackProp.jar",
#algorithm name
algorithmName = "MLP-BP-R",
#String with algorithm name
algorithmString = "MLP Neural Network",
#Private functions
#Get the text with the parameters for the config file
getParametersText = function(){
text <- ""
text <- paste0(text, "seed = ", self$seed, "\n")
text <- paste0(text, "Hidden_layers = ", self$hidden_layers, "\n")
text <- paste0(text, "Hidden_nodes = ", self$hidden_nodes, "\n")
text <- paste0(text, "Transfer = ", self$transfer, "\n")
text <- paste0(text, "Eta = ", self$eta, "\n")
text <- paste0(text, "Alpha = ", self$alpha, "\n")
text <- paste0(text, "Lambda = ", self$lambda, "\n")
text <- paste0(text, "Test_data = ", self$test_data, "\n")
text <- paste0(text, "Validation_data = ", self$validation_data, "\n")
text <- paste0(text, "Cross_validation = ", self$cross_validation, "\n")
text <- paste0(text, "Cycles = ", self$cycles, "\n")
text <- paste0(text, "Improve = ", self$improve, "\n")
text <- paste0(text, "Problem = Regression", "\n")
text <- paste0(text, "Tipify_inputs = ", self$tipify_inputs, "\n")
text <- paste0(text, "Verbose = FALSE", "\n")
text <- paste0(text, "SaveAll = ", self$save_all, "\n")
return(text)
}
)
)
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