library(MASS)
library(class)
library(nnet)
library(e1071)

# number of hidden units

G=1
K="radial"
C=10

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

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

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)

Z.svm <- svm(p, tp, kernel=K, gamma=G, cost=C)
Z.svm.t <- class.ind(predict(Z.svm, pt )) 

zp.svm <- Z.svm.t[,1]-Z.svm.t[,2]
plot(TrainC[, 1], TrainC[, 2], xlab = "x1", ylab = "x2", col=codes(TrainC$y)+1)
title("SVM TrainC")
mtext(paste(K,",gamma=",G,"cost=",C))
contour(xp, yp, matrix(zp.svm, np), add = T, levels = 0, labex = 0)