library(RSNNS)
library(data.table)

set.seed(1)

#CARGA DE DATOS
trainSet <- data.table(read.csv("DatosTrain.csv",dec=",",sep=";",header = T))
testSet  <- data.table(read.csv("DatosTest.csv",dec=",",sep=";",header = T))

#ELECCION DEL OBJETIVO
target <- "salida"

#SELECCION DE LOS PARAMETROS
topologia        <- c(20)
razonAprendizaje <- 0.01
ciclosMaximos    <- 200

#EJECUCION DEL APRENDIZAJE Y GENERACION DEL MODELO
model <-  rbf(x= trainSet[,-target,with=F],
             y= trainSet[,target,with=F],
             size= topologia,
             maxit=ciclosMaximos,
             learnFuncParams=c(razonAprendizaje),
             shufflePatterns = F
)

#GRAFICO DE LA EVOLUCION DEL ERROR
plotIterativeError(model)


RMSE <- function(pred,obs) (sum((pred - obs)^2) / length(obs)) ^ (1/2)

#VECTOR DE LOS ERRORES
errors <- c(TrainRMSE= RMSE(pred= predict(model,trainSet[,-target,with=F]),obs= trainSet[,target,with=F]),
            TestRMSE=  RMSE(pred= predict(model,testSet[,-target,with=F]) ,obs= testSet[,target,with=F]))

#TABLA CON LOS ERRORES POR CICLO
iterativeErrors <- data.table(RMSETrain= (model$IterativeFitError/nrow(trainSet)) ^(1/2))

#SALIDAS DE LA RED
outputs <- data.table(train= predict(model,trainSet[,-target,with=F]),
                      test=  c(predict(model,testSet[,-target,with=F] ),rep(NA,nrow(trainSet)-nrow(testSet))))
colnames(outputs) <- c("train","test")

#GUARDADO DE RESULTADOS
saveRDS(model,"nnet.rds")
write.csv2(errors,"finalErrors.csv")
write.csv2(iterativeErrors,"iterativeErrors.csv")
write.csv2(outputs,"netOutputs.csv")