TrainA1<-read.table("TrainA1.dat")
names(TrainA1)<-c("x1","x2","y")

g1 <- lm( y ~ x1 + x2, data=TrainA1)
y1 <- predict( g1, TrainA1[,-3])

yhat <- rep( 0,100)
for( i in 1:100 ) { if(y1[i] > 0.5) yhat[i] <- 1 else yhat[i] <- 0}

CErr = 0
for( i in 1:50 ) { if( yhat[i] == 0 ) CErr = CErr + 1 }
for( i in 51:100 ) { if( yhat[i] == 1 ) CErr = CErr + 1 }

par(mfcol=c(2,2))

plot(TrainA1[,-3],col=TrainA1$y+1)
title("Training Set A1")
mtext(paste("Plug-in Error = ", 100*CErr/100, "%")) 

p <- as.matrix(TrainA1[, -3])
xp <- seq(min(TrainA1$x1), max(TrainA1$x1), length = 50); np <- length(xp)
yp <- seq(min(TrainA1$x2), max(TrainA1$x2), length = 50); 
pT <- expand.grid(x1 = xp, x2 = yp)
gp <- predict(g1,pT)
contour(xp, yp, matrix(gp, np), add = T, levels = 0.5, labex = 0)

TrainA2 <- read.table("TrainA2.dat")
names(TrainA2)<-c("x1","x2","y")

y2 <- predict( g1, TrainA2[,-3])

yhat2 <- rep( 0,100)
for( i in 1:100 ) { if(y2[i] > 0.5) yhat2[i] <- 1 else yhat2[i] <- 0}

CErr2 = 0
for( i in 1:50 ) { if( yhat2[i] == 0 ) CErr2 = CErr2 + 1 }
for( i in 51:100 ) { if( yhat2[i] == 1 ) CErr2 = CErr2 + 1 }

plot(TrainA2[,-3],col=TrainA2$y+1)
title("Classification of Training Set A2")
mtext(paste("Test Error = ", 100*CErr2/100, "%")) 

p <- as.matrix(TrainA1[, -3])
xp <- seq(min(TrainA2$x1), max(TrainA2$x1), length = 50); np <- length(xp)
yp <- seq(min(TrainA2$x2), max(TrainA2$x2), length = 50); 
pT <- expand.grid(x1 = xp, x2 = yp)
gp <- predict(g1,pT)
contour(xp, yp, matrix(gp, np), add = T, levels = 0.5, labex = 0)

TrainA12<-rbind(TrainA1,TrainA2)

g12 <- lm( y ~ x1 + x2, data=TrainA12)
y12 <- predict( g12, TrainA12[,-3])
yhat12<-rep(0,200)
for( i in 1:200 ) { if(y12[i] > 0.5) yhat12[i] <- 1 else yhat12[i] <- 0}
CErr12 = 0
for( i in 1:50 ) { if( yhat12[i] == 0 ) CErr12 = CErr12 + 1 }
for( i in 51:100 ) { if( yhat12[i] == 1 ) CErr12 = CErr12 + 1 }
for( i in 100:150 ) { if( yhat12[i] == 0 ) CErr12 = CErr12 + 1 }
for( i in 151:200 ) { if( yhat12[i] == 1 ) CErr12 = CErr12 + 1 }

plot(TrainA12[,-3],col=TrainA12$y+1)
title("Training Sets A1 and A2")
mtext(paste("Plug-in Error = ", 100*CErr12/200, "%")) 

p <- as.matrix(TrainA12[, -3])
xp <- seq(min(TrainA12$x1), max(TrainA12$x1), length = 50); np <- length(xp)
yp <- seq(min(TrainA12$x2), max(TrainA12$x2), length = 50); 
pT <- expand.grid(x1 = xp, x2 = yp)
gp <- predict(g12,pT)
contour(xp, yp, matrix(gp, np), add = T, levels = 0.5, labex = 0)

TestA<-read.table("TestA.dat")
names(TestA)<-c("x1","x2")
gTest <- predict(g12,TestA)

y<-rep(0,400)
for( i in 1:400 ) { if(gTest[i] > 0.5) y[i] <- 1 else y[i] <- 0}

plot(TestA,col=y+1)
title("Test Set A")

text.x1=paste("x1=", format(g12$coefficients["x1"], digits=3))
text.x2=paste("x2=", format(g12$coefficients["x2"], digits=3))
text.i=paste("Int=", format(g12$coefficients["(Intercept)"], digits=3))

mtext(paste(text.i, text.x1, text.x2))

contour(xp, yp, matrix(gp, np), add = T, levels = 0.5, labex = 0)
write.table(data.frame(TestA,y), "TestA-C-L.dat", quote=F, col.name=F, row.name=F)