On Jun 9, 2012, at 11:08 , wl2776 wrote: > Hi all. > I'm developing a function, which must return a square matrix. > > Here is the code: > http://pastebin.com/THzEW9N7 > > These functions implement an analog of two embedded for cycles. > > The first variant creates the resulting matrix by columns, cbind()-ing them > one by one. > The second variant creates the matrix with two columns, which rows contain > all possible > variants of i and j and calls apply on them. > > The test input (data frame cp.table) can be produced with the following > commands: >> n<-132 >> cpt<-data.frame(x=runif(n, min=0, max=100), y=runif(n, min=0, max=100), >> la=runif(n, min=0, max=360), phi=runif(n, min=-90, max=90)) > Any random data will do. > > The second variant seems to me much more readable and beauteful. > However, the first ugly variant runs much faster. > Why?? > Here are the profiles: >
Nope, they weren't... Anyways, you're effectively looping over N^2 (i,j) combinations, with complex indexing all the way, without making proper use of vectorization. As far as I can tell, what you're doing is effectively with(cp.table, sqrt(outer(x, x, "-")^2 + outer(y, y, "-")^2) ) or even dist(cptable[1:2]) both of which should be a good deal faster. -- Peter Dalgaard, Professor, Center for Statistics, Copenhagen Business School Solbjerg Plads 3, 2000 Frederiksberg, Denmark Phone: (+45)38153501 Email: pd....@cbs.dk Priv: pda...@gmail.com ______________________________________________ R-help@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.
