Dear R-programmer,
I wrote an adapted implementation of the Kennard-Stone algorithm for
sample selection of multivariate data (R 2.7.1 under MacBook Pro,
Processor 2.2 GHz Intel Core 2 Duo, Memory 2 GB 667 MHZ DDR2 SDRAM).
I used for the heart of the script three embedded loops. This makes it
especially for huge datasets very slow. For a datamatrix of 1853*1853
and the selection of 556 samples needed computation time of more than
24 hours.
I did some research on vecotrization, but I could not figure out how
to do it better/faster. Which ways are there to replace the time
consuming loops?
Here are some information:
# val.n<-24;
# start.b<-matrix(nrow=1812, ncol=20);
# val is a vector of the rownames of 22 in an earlier step chosen
extrem samples;
# euc<-<-matrix(nrow=1853, ncol=1853); [contains the Euclidean
distance calculations]
The following calculation of the system.time was for the selection of
two samples:
system.time(KEN.STO(val.n,start.b,val.start,euc))
user system elapsed
25.294 13.262 38.927
The function:
KEN.STO<-function(val.n,start.b,val,euc){
for(k in 1:val.n){
sum.dist<-c();
for(i in 1:length(start.b[,1])){
sum<-c();
for(j in 1:length(val)){
sum[j]<-euc[rownames(start.b)[i],val[j]]
}
sum.dist[i]<-min(sum);
}
bla<-rownames(start.b)[which(sum.dist==max(sum.dist))]
val<-c(val,bla[1]);
start.b<-start.b[-(which(match(rownames(start.b),val[length(val)])!
="NA")),];
if(length(val)>=val.n)break;
}
return(val);
}
Regards,
Thomas
Dr. Thomas Terhoeven-Urselmans
Post-Doc Fellow
Soil infrared spectroscopy
World Agroforestry Center (ICRAF)
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