Alexy Khrabrov wrote: > I've read in Phil Spector's new book that it's a good idea to > preallocate a big matrix, like > > u <- matrix(0,nrow,ncol) # (1) > > Now, I read contents of a huge matrix from a Fortran binary dump. > > u <- readBin(con,what="double",n=nrow*ncol) # (2) > > If I do (1) and then (2), u is a vector, obviously it's either > reallocated or its matrix nature is lost -- overridden? overwritten? > > Instead, I do it now as > > u <- > matrix(readBin(con,what="double",n=nrow*ncol),nrow=nrow,ncol=ncol) # (3) > > What's going on with memory management here and what's the right way > to make it efficient -- and how to preallocate? > > After that, I'm saving u as R binary object in an rda file. Does it > make sense to preallocate u before reading it back now from the rda > file? >
This kind of preallocation only makes sense for loops. In your case, it does not yield benefits. Anyway, if you really want to reassign things in u, you could say: u[] <- readBin(con,what="double",n=nrow*ncol) # (2) (empty brackets for indexing all elements) Best wishes, Uwe > Cheers, > Alexy > > ______________________________________________ > 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. ______________________________________________ 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.
