gen<-function(n){
    x<-matrix(runif(5*n),n,5)
    TT<-10*sin(pi*x[,1]*x[,2])+20*(x[,3]-0.5)^2+10*x[,4]+5*x[,5]
     y<-TT+rnorm(n)
     list(x,y,TT)
}

library(randomForest)
hw6<-function(times){
n=200
ptm <- proc.time()
result<-rep(0,times)
resulttest<-rep(0,times)
for(i in 1:times){
#calculate loss
#set.seed()
da<-gen(n)    #generate data
tr<-da[[3]]  #true value
y<-da[[2]]   #response y
xx<-da[[1]]    #independent variables X
rffit<-randomForest(xx,y)  #fit a randomforest
yhat<-rffit$predicted      #OOB yhat
lossrf<-sum((tr-yhat)^2)/n
result[i]<-lossrf
#calculate prediction error
tda<-gen(n)  #generate testing data
ytest<-tda[[2]]  #testing data response y
xtest<-tda[[1]]  #testing data independent variables X
yhattest<-predict(rffit,xtest)  #predicted value of the testing data using the fit from training data
testrf<-sum((ytest-yhattest)^2)/n
resulttest[i]<-testrf
}
print(result)
print(resulttest)
loss<-mean(result)
pe<-mean(resulttest)
print(loss)
print(pe)
}