Looks like the rank function in R takes up most of the time.
Replacing it with a sum reduces the time of the rollapply solution to
one sixth of the its original time:
> system.time(rollapply(z,len, function(x) rank(x)[len]))
user system elapsed
17.00 0.27 17.27
> system.time(rollapply(z,len, function(x) sum(x <= x[len])))
user system elapsed
2.72 0.28 3.01
On Sun, Apr 18, 2010 at 11:51 AM, zerdna <az...@yahoo.com> wrote:
>
> Gabor, Charles, Whit -- i've been walking the woods of R alone so far, and i
> got to say that your replies to that trivial question are eye-opening
> experience for me. Gentlemen, what i am trying to say in a roundabout way is
> that i am extremely grateful and that you guys are frigging awesome.
>
> Let me outline the times i am getting for different proposed solutions on
> the same machine, same data, same version of R
>
> x<-rnorm(50000); len<-100
>
> 1. my naive roll.rank
>
> system.time(x.rank.1<-roll.rank(x,len))
> user system elapsed
> 6.405 0.488 6.94
>
> 2. Gabor's zoo
>
> z<-zoo(x)
> system.time(rollapply(z,len, function(x) rank(x)[len]))
> user system elapsed
> 6.195 0.361 6.554
>
> 3. Charles embed
>
> system.time(x.rank <- rowSums(x[ -(1:(len-1)) ] >= embed(x,len) ))
> user system elapsed
> 0.181 0.055 0.236
>
>
> 4. Whit's fts
> dat<-fts(x)
> system.time(x.rank<-moving.rank(dat, len))
> user system elapsed
> 0.036 0 0.036
>
> 5. Charles suggestion with inline, my crude implementation
>
> sig<-signature(x="numeric", rank="integer", n="integer", len="integer")
> code<-"int k=0; for(int i=*len-1; i< *n; i++) {int r=1; for(int j=i-1; j>
> i-len;j--) r+=(x[i]>x[j] ?1:0); rank[k++]<-r;}"
> fns<-cfunction(sig,code, convention=".C")
>
> system.time( x.rank<-fns(x, numeric(length(x)-len), length(x), len))
>
> user system elapsed
> 0.011 0 0.011
>
>
> I guess i could speed it up from time being proportional to length(x)*len
> to time proportional to length(x)*log(len) if i use slightly more
> intelligent algo, but this works fine for my requirements. Only thing i
> really wonder about is why exactly R takes 640 times more than this C code.
> It would be immensely enlightening if someone could point to an explanation
> of how execution in R works and where and when it slows down like this.
> --
> View this message in context:
> http://n4.nabble.com/efficient-rolling-rank-tp2013535p2014922.html
> Sent from the R help mailing list archive at Nabble.com.
>
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>
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