library(rpart)

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

par(mfcol=c(2,1))

Z.rp <- rpart(y ~ x1 + x2, data=TrainC)
Z.rp.t <- predict(Z.rp, pt ) 

zp.rp <- Z.rp.t[,1]-Z.rp.t[,2]
plot(TrainC[, 1], TrainC[, 2], xlab = "x1", ylab = "x2", col=codes(TrainC$y)+1)
title("RP TrainC")
contour(xp, yp, matrix(zp.rp, np), add = T, levels = 0, labex = 0)
   
TestC <- read.table("ExamC.dat")
names(TestC)<-c("x1","x2","y")
N <- length(TestC[,1])

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

yhat <- predict(Z.rp, TestC[,-3] ) 

yThat <- rep('d', length(yhat[,1]))
for(i in 1:length(yhat[,1])) { if( yhat[i,1] > yhat[i,2] ) yThat[i]="a" else yThat[i]="b" }

plot(TestC[, 1], TestC[, 2], xlab = "x1", ylab = "x2", col=codes(TestC$y)+1)
title("RP ExamC")

T.rp.t <- predict(Z.rp, pt ) 
zp.rp <- T.rp.t[,1]-T.rp.t[,2]
contour(xp, yp, matrix(zp.rp, np), add = T, levels = 0, labex = 0)