Hello,
I believe that your bottleneck lies at this piece of code:
sum<-c();
for(j in 1:length(val)){
sum[j]<-euc[rownames(start.b)[i],val[j]]
}
In order to speed up your code, there are two alternatives:
1) Try to reorder the euc matrix so that the sum vector corresponds to
(part of) a row or column of euc.
2) For each i value, create a matrix with the coordinates corresponding
to ( rownames(start.b)[i], val[j] ) and index the matrix by this matrix
in order to create sum. This will be easiest if you can reorder euc in a
way that accessing its elements will be easy (and then you would be back
into (1)).
Creating a variable sum as c() and increasing its size in a loop is one
of the easiest ways to uselessly burn your CPU.
Best regards,
Carlos J. Gil Bellosta
http://www.datanalytics.com
On Wed, 2009-01-14 at 10:32 +0300, Thomas Terhoeven-Urselmans wrote:
> 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)
> [[alternative HTML version deleted]]
>
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> and provide commented, minimal, self-contained, reproducible code.
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