#-*- R -*-

## Script from Fourth Edition of `Modern Applied Statistics with S'

# Chapter 12   Classification

library(MASS)
library(class)
library(nnet)


# 12.4  Neural networks

pltnn <- function(main, ...) {
   plot(Cushings[,1], Cushings[,2], type="n",
   xlab="Tetrahydrocortisone", ylab = "Pregnanetriol", main=main, ...)
   for(il in 1:4) {
       set <- Cushings$Type==levels(Cushings$Type)[il]
       text(Cushings[set, 1], Cushings[set, 2],
          as.character(Cushings$Type[set]), col = 2 + il) }
}

plt.bndry <- function(size=0, decay=0, ...)
{
   cush.nn <- nnet(cush, tpi, skip=T, softmax=T, size=size,
      decay=decay, maxit=1000)
   invisible(b1(predict(cush.nn, cushT), ...))
}

b1 <- function(Z, ...)
{
   zp <- Z[,3] - pmax(Z[,2], Z[,1])
   contour(xp, yp, matrix(zp, np),
      add=T, levels=0, labex=0, ...)
   zp <- Z[,1] - pmax(Z[,3], Z[,2])
   contour(xp, yp, matrix(zp, np),
      add=T, levels=0, labex=0, ...)
}

data(Cushings)
cush <- as.matrix(Cushings[, -3])
tp <- Cushings$Type[1:21, drop = T]
xp <- seq(0.6, 4.0, length = 100)
np <- length(xp)
yp <- seq(-3.25, 2.45, length = 100)

cushT <- expand.grid(Tetrahydrocortisone = xp, Pregnanetriol = yp)
cush <- cush[1:21,]
tpi <- class.ind(tp)

# functions pltnn and plt.bndry given in the scripts

pltnn("Size = 2")
set.seed(1) 
plt.bndry(size = 2, col = 2)