Nothing
cv10mlp <-
function (data, units, decay = 0, maxwts = 1000, maxit = 100,
repet)
{
#require(nnet)
n <- dim(data)[1]
p <- dim(data)[2]
ecv <- rep(0, repet)
for (kk in 1:repet) {
azar <- data[rank(runif(n)), ]
azar[, p] <- as.factor(azar[, p])
parti <- floor(n/10)
salida <- rep(0, 10)
for (j in 1:10) {
cc <- ((j - 1) * parti + 1):(j * parti)
if (j == 10) {
cc <- ((j - 1) * parti + 1):n
}
datap <- azar[cc, ]
datat <- azar[-cc, ]
clasest = nnet::class.ind(azar[-cc, p])
tempo <- nnet::nnet(as.matrix(datat[, 1:(p - 1)]), clasest,
entropy = TRUE, size = units, decay = decay,
MaxNWts = maxwts, maxit = maxit)
tempo1 = predict(tempo, datap)
pd = max.col(tempo1)
salida[j] <- sum(pd != datap[, p])
}
ecv[kk] <- sum(salida)/n
}
cat("The misclassification errors of each repetition are:",
"\n")
print(ecv)
cat("The mean misclassifcation error is", "\n")
ECV1 = mean(ecv)
ECV1
}
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