## "INFOF422 Statistical foundations of machine learning" course
## R package gbcode
## Author: G. Bontempi
rm(list=ls())
par(ask=TRUE)
n<-10
N<-100
X<-array(rnorm(N*n),c(N,n))
Y<-cbind(X[,1]+X[,n]+rnorm(N,sd=0.1),X[,2],-5*X[,3]+rnorm(N,sd=1))
m<-NCOL(Y)
rls<-function(x,y,t,P,mu=1){
x=rbind(x)
P.new <-(P-(P%*%t(x)%*%x%*%P)/as.numeric(1+x%*%P%*%t(x)))/mu
ga <- P.new%*%t(x)
epsi <- y-x%*%t
t.new<-t+ga%*%as.numeric(epsi)
list(t.new,P.new,mean(epsi^2))
}
N<-NROW(X)
t<-array(0,c(n+1,m))
P<-500*diag(n+1)
mu<-0.95
E<-NULL
for (i in 1:N){
rls.step<-rls(c(1, X[i,]),Y[i,],t,P,mu)
t<-rls.step[[1]]
P<-rls.step[[2]]
E<-c(E,rls.step[[3]])
}
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