#
library(MASS)    
library(stats)        
library(boost)        # for adaboost
#
# First I'll generate the training data.
greenx1a <- runif(25, 0, 1)
greenx1b <- runif(50, 1, 2)
greenx2a <- runif(25, 0, 1)
greenx2b <- runif(50, 0, 2)
redx1a <- runif(50, 0, 1)
redx1b <- runif(25, 1, 2)
redx2a <- runif(25, 1, 3)
redx2b <- runif(25, 2, 3)
greena <- cbind(greenx1a, greenx2a)
greenb <- cbind(greenx1b, greenx2b)
reda <- cbind(redx1a, redx2a)
redb <- cbind(redx1b, redx2b)
trainx <- rbind(greena, greenb, reda, redb)
greeny <- rep(0, 75)
redy <- rep(1, 75)
trainy <- c(greeny, redy)
plot(trainx[,1], trainx[,2], col=3-trainy)
#
# Now I'll generate some generalization data
# the same way.
reenx1a <- runif(25, 0, 1)
greenx1b <- runif50, 1, 2)
greenx2a <- runif(25, 0, 1)
greenx2b <- runif(50, 0, 2)
redx1a <- runif(50, 0, 1)
redx1b <- runif(25, 1, 2)
redx2a <- runif(25, 1, 3)
redx2b <- runif(25, 2, 3)
greena <- cbind(greenx1a, greenx2a)
greenb <- cbind(greenx1b, greenx2b)
reda <- cbind(redx1a, redx2a)
redb <- cbind(redx1b, redx2b)
genx <- rbind(greena, greenb, reda, redb)
greeny <- rep(0, 75)
redy <- rep(1, 75)
geny <- c(greeny, redy)
#
boostpred <- adaboost(trainx, trainy, genx)
summarize(boostpred, geny)