[Rd] Speed of for loops

[Byron Ellis]

Actually, better yet:

gen.iter = function(y=NA) {
  function(x) {
    y <<- if(is.na(y)) x else x+y
  }
}
sapply(x,gen.iter())


On 1/30/07, Byron Ellis <byron.ellis at gmail.com> wrote:
> Actually, why not use a closure to store previous value(s)?
>
> In the simple case, which depends on x_i and y_{i-1}
>
> gen.iter = function(x) {
>     y = NA
>     function(i) {
>        y <<- if(is.na(y)) x[i] else y+x[i]
>     }
> }
>
> y = sapply(1:10,gen.iter(x))
>
> Obviously you can modify the function for the bookkeeping required to
> manage whatever lag you need. I use this sometimes when I'm
> implementing MCMC samplers of various kinds.
>
>
> On 1/30/07, Herve Pages <hpages at fhcrc.org> wrote:
> > Tom McCallum wrote:
> > > Hi Everyone,
> > >
> > > I have a question about for loops.  If you have something like:
> > >
> > > f <- function(x) {
> > >       y <- rep(NA,10);
> > >       for( i in 1:10 ) {
> > >               if ( i > 3 ) {
> > >                       if ( is.na(y[i-3]) == FALSE ) {
> > >                               # some calculation F which depends on one or more of the previously
> > > generated values in the series
> > >                               y[i] = y[i-1]+x[i];
> > >                       } else {
> > >                               y[i] <- x[i];
> > >                       }
> > >               }
> > >       }
> > >       y
> > > }
> > >
> > > e.g.
> > >
> > >> f(c(1,2,3,4,5,6,7,8,9,10,11,12));
> > >   [1] NA NA NA  4  5  6 13 21 30 40
> > >
> > > is there a faster way to process this than with a 'for' loop?  I have
> > > looked at lapply as well but I have read that lapply is no faster than a
> > > for loop and for my particular application it is easier to use a for loop.
> > > Also I have seen 'rle' which I think may help me but am not sure as I have
> > > only just come across it, any ideas?
> >
> > Hi Tom,
> >
> > In the general case, you need a loop in order to propagate calculations
> > and their results across a vector.
> >
> > In _your_ particular case however, it seems that all you are doing is a
> > cumulative sum on x (at least this is what's happening for i >= 6).
> > So you could do:
> >
> > f2 <- function(x)
> > {
> >     offset <- 3
> >     start_propagate_at <- 6
> >     y_length <- 10
> >     init_range <- (offset+1):start_propagate_at
> >     y <- rep(NA, offset)
> >     y[init_range] <- x[init_range]
> >     y[start_propagate_at:y_length] <- cumsum(x[start_propagate_at:y_length])
> >     y
> > }
> >
> > and it will return the same thing as your function 'f' (at least when 'x' doesn't
> > contain NAs) but it's not faster :-/
> >
> > IMO, using sapply for propagating calculations across a vector is not appropriate
> > because:
> >
> >   (1) It requires special care. For example, this:
> >
> >         > x <- 1:10
> >         > sapply(2:length(x), function(i) {x[i] <- x[i-1]+x[i]})
> >
> >       doesn't work because the 'x' symbol on the left side of the <- in the
> >       anonymous function doesn't refer to the 'x' symbol defined in the global
> >       environment. So you need to use tricks like this:
> >
> >         > sapply(2:length(x),
> >                  function(i) {x[i] <- x[i-1]+x[i]; assign("x", x, envir=.GlobalEnv); x[i]})
> >
> >   (2) Because of this kind of tricks, then it is _very_ slow (about 10 times
> >       slower or more than a 'for' loop).
> >
> > Cheers,
> > H.
> >
> >
> > >
> > > Many thanks
> > >
> > > Tom
> > >
> > >
> > >
> >
> > ______________________________________________
> > R-devel at r-project.org mailing list
> > https://stat.ethz.ch/mailman/listinfo/r-devel
> >
>
>
> --
> Byron Ellis (byron.ellis at gmail.com)
> "Oook" -- The Librarian
>


-- 
Byron Ellis (byron.ellis at gmail.com)
"Oook" -- The Librarian