On 01/11/2015 7:31 AM, Maram SAlem wrote:
> Hi All,
>
> I'm writing a long code that takes long time to execute. So I used the
> Rprof() function and found out that the function that takes about 80% of
> the time is the incomb () fucntion (below), and this is most probably
> because of the many explicit for() loops I'm using.
>
> n=18;m=4;p=0.3;alpha=0.2;beta=2
> x=c(3,0,0)
> LD<-list()
> for (i in 1:(m-1)) {
> LD[[i]]<-seq(0,x[i],1)
> }
> LD[[m]]<-seq(0,(n-m-sum(x)),1)
> LED<-expand.grid (LD)
> LED<-as.matrix(LED)
> store1<-numeric(nrow(LED))
> h<- numeric(m-1)
> lm<- numeric(m-1)
> for (j in 1:length(store1) )
> {
> incomb<-function(x,alpha,beta) {
>
> g<-((-1)^(sum(LED[j,])))*(gamma((1/beta)+1))*((alpha)^(-(1/beta)))
> for (i in 1:(m-1)) {
> h[i]<- choose(x[i],LED[j,i])
> }
> ik<-prod(h)*choose((n-m-sum(x)),LED[j,m])
> for (i in 1:(m-1)) {
> lm[i]<-(sum(LED[j,1:i])) + i
> }
> plm<-prod(lm)
> gil<-g*ik/(plm)
> hlm<-numeric(sum(LED[j,])+(m-1))
> dsa<-length(hlm)
> for (i in 1:dsa)
> {
> ppp<- sum(LED[j,])+(m-1)
> hlm[i]<-
> (choose(ppp,i))*((-1)^(i))*((i+1)^((-1)*((1/beta)+1)))
> }
> shl<-gil*(sum(hlm)+1)
> return (shl)
> }
> store1[j]<-incomb(x,alpha=0.2,beta=2)
> }
>
>
> I'm trying to use alternatives (for ex. to vectorize things) to the
> explicit for() loops, but things don't work out.
>
> Any suggestions that can help me to speed up the execution of the incomb()
> function are much appreciated.
>
Generally I'd suggest starting with your innermost loops, and vectorize
those. Continue outwards from there if you can. For example, you have
> for (i in 1:(m-1)) {
> h[i]<- choose(x[i],LED[j,i])
> }
which could be vectorized as
i <- 1:(m-1)
h[i] <- choose(x[i], LED[j, i])
and maybe as
h <- choose(x, LED[j, ])
(but I haven't read your code closely enough to know if those are
equivalent).
If m is large, this will make a big difference to that part of the code.
Duncan Murdoch
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