R : Copyright 2003, The R Development Core Team
Version 1.6.2  (2003-01-10)

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> invisible(options(echo = TRUE))
> library(class)
> 
> train <- read.table("loans.dat",header=F,col.names=c("x1","x2","y"))
> 
> X <- cbind(train$x1,train$x2)
> ty <- train$y
> y <- as.factor(train$y)
> 
> valid <- read.table("eval.dat",header=F,col.names=c("x1","x2","y"))
> 
> vX <- cbind(valid$x1,valid$x2)
> vy <- valid$y
> 
> idx <- seq(3,15,2)
> n <- length(idx)
> 
> te <- mat.or.vec(n,1)
> ve <- mat.or.vec(n,1)
> K <- mat.or.vec(n,1)
> 
> j = 0
> for (i in idx) {
+ 
+   j <- j + 1
+   K[j] = -i
+   print(i)
+ 
+   dflt <- knn(train=X,test=X,cl=y,k=i)
+ 
+   dflt <- as.numeric((dflt==1))
+ 
+   err <- abs(ty-dflt)
+   te[j] <- mean(err)
+   print(mean(err))
+ 
+   dflt <- knn(train=X,test=vX,cl=y,k=i)
+ 
+   dflt <- as.numeric((dflt==1))
+ 
+   err <- abs(vy-dflt)
+   ve[j] <- mean(err)
+   print(mean(err))
+ 
+ }
[1] 3
[1] 0.044
[1] 0.08465
[1] 5
[1] 0.068
[1] 0.07943
[1] 7
[1] 0.064
[1] 0.07572
[1] 9
[1] 0.072
[1] 0.07521
[1] 11
[1] 0.076
[1] 0.07557
[1] 13
[1] 0.06
[1] 0.07635
[1] 15
[1] 0.056
[1] 0.07699
> 
> source("psopts.r")
> postscript(file="nearloss.eps")
> 
> plot(x=c(K,K),y=c(te,ve),type='n',xlab="Complexity",ylab="Loss")
>   lines(x=K,y=te,lty="solid",col="red")
>   lines(x=K,y=ve,lty="solid",col="green")
> dev.off()
null device 
          1 
> 
> proc.time()
[1] 8.51 0.29 8.81 0.00 0.00
>