## "INFOF422 Statistical foundations of machine learning" course
## R package gbcode
## Author: G. Bontempi
rm(list=ls())
source("dopler.R")
library(lazy)
N<-2000
D<-dataset.dopler(2000)
plot(D$x,D$y,type="l")
d<-data.frame(D$y,D$x)
names(d)<-c("Y","X")
MSE=NULL
Nn=seq(3,50,by=1)
for (number.neighbors in Nn){
mod.lazy<-lazy(Y~.,d,
control=lazy.control(conIdPar=NULL,
linIdPar=c(number.neighbors,number.neighbors), quaIdPar=NULL))
d.ts<-data.frame(D$y.ts,D$x.ts)
names(d.ts)<-c("Y","X")
p<-predict(mod.lazy,d.ts)
MSE=c(MSE,mean((p$h-D$y.ts)^2))
plot(D$x.ts,D$y.ts,type="l", main=paste("Number neighbors=",number.neighbors))
lines(D$x.ts,c(p$h),col="red")
Sys.sleep(.1)
}
plot(Nn,MSE,type="l",ylab="MISE",xlab="Number of neighbors")
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.