In MickyDowns/mine_algorithms: What the package does (short line)

AdaBoost

setwd("./data")
train<-read.csv("sonar_train.csv",header=FALSE)
test<-read.csv("sonar_test.csv",header=FALSE)
setwd("../")

y<-train[,61]
x<-train[,1:60]
y_test<-test[,61]
x_test<-test[,1:60]

train_error<-rep(0,500) # Keep track of errors
test_error<-rep(0,500)
f<-rep(0,130) # 130 pts in training data
f_test<-rep(0,78) # 78 pts in test data
i<-1
library(rpart)

while(i<=2){
w<-exp(-y*f) # This is a shortcut to compute w
print(head(w))
w<-w/sum(w)
fit<-rpart(y~.,x,w,method="class")
g<--1+2*(predict(fit,x)[,2]>.5) # make -1 or 1
g_test<--1+2*(predict(fit,x_test)[,2]>.5)
e<-sum(log(w*(y*g<0))

alpha<-.5*log ( (1-e) / e )
f<-f+alpha*g
f_test<-f_test+alpha*g_test
train_error[i]<-sum(1*f*y<0)/130
test_error[i]<-sum(1*f_test*y_test<0)/78
i<-i+1
}

plot(seq(1,500),test_error,type="l",
ylim=c(0,.5),
ylab="log(w)",xlab="Iterations",lwd=2)
lines(train_error,lwd=2,col="purple")
legend(4,.5,c("Training Exponential Loss","Test Exponential Loss"),
col=c("purple","black"),lwd=2)