[Rd] Speed of for loops
Tom McCallum
tom.mccallum at levelelimited.com
Wed Jan 31 13:35:15 CET 2007
Thank you all for your advice and tips. In the end, I think the for loop
is the easiest way forward due to other requirements but its good to know
that I haven't missed anything too obvious.
Tom
On Tue, 30 Jan 2007 23:42:27 -0000, Oleg Sklyar <osklyar at ebi.ac.uk> wrote:
> It is surely an elegant way of doing things (although far from being
> easy to parse visually) but is it really faster than a loop?
>
> After all, the indexing problem is the same and sapply simply does the
> same job as for in this case, plus "<<-" will _search_ through the
> environment on every single step. Where is the gain?
>
> Oleg
>
> --
> Dr Oleg Sklyar | EBI-EMBL, Cambridge CB10 1SD, UK | +44-1223-494466
>
>
> Byron Ellis 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
>>>
>>
>>
>
> ______________________________________________
> R-devel at r-project.org mailing list
> https://stat.ethz.ch/mailman/listinfo/r-devel
--
Dr. Thomas McCallum
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