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

# number of hidden units

S=10
Decay=0.05

TrainC<-read.table("trainC.dat")
names(TrainC)<-c("x1","x2","y")

p <- as.matrix(TrainC[, -3])
tp<-TrainC$y
tpi = class.ind(tp)

xp <- seq(min(TrainC$x1), max(TrainC$x1), length = 50); np <- length(xp)
yp <- seq(min(TrainC$x2), max(TrainC$x2), length = 50)
pt <- expand.grid(x1 = xp, x2 = yp)

set.seed(1)
Z.nn <- nnet(p, tpi, skip=T, softmax=T, size=S, decay=Decay, maxit=1000)
Z.nn.t <- predict(Z.nn, pt ) 

zp.nn <- Z.nn.t[,1]-Z.nn.t[,2]
plot(TrainC[, 1], TrainC[, 2], xlab = "x1", ylab = "x2", col=codes(TrainC$y)+1)
title("NN TrainC")
mtext(paste("Size=",S,", Decay=",Decay))
contour(xp, yp, matrix(zp.nn, np), add = T, levels = 0, labex = 0)
   
Z.nn <- nnet(p, tpi, skip=T, softmax=T, size=S, decay=Decay, maxit=1000)
Z.nn.t <- predict(Z.nn, pt ) 
zp.nn <- Z.nn.t[,1]-Z.nn.t[,2]
contour(xp, yp, matrix(zp.nn, np), add = T, levels = 0, labex = 0, col=4)

Z.nn <- nnet(p, tpi, skip=T, softmax=T, size=S, decay=Decay, maxit=1000)
Z.nn.t <- predict(Z.nn, pt ) 
zp.nn <- Z.nn.t[,1]-Z.nn.t[,2]
contour(xp, yp, matrix(zp.nn, np), add = T, levels = 0, labex = 0, col=6)