Hi List,
I'm hoping to get opinions for enhancing the efficiency of the following
code designed to take a vector of probabilities (outcomes) and calculate a
union of the probability space. As part of the union calculation, combn()
must be used, which returns a matrix, and the parallelized version of
lapply() provided in the multicore package requires a list. I've found that
parallelization is very necessary for vectors of outcomes greater in length
than about 10 or 15 elements, which is why I need to make use of multicore
(and, therefore, convert the combn() matrix to a list). It would speed the
process up if there was a more direct way to convert the columns of combn()
to elements of a single list. Any constructive suggestions will be greatly
appreciated. Thanks for your consideration,
C
code:
------------
unionIndependant <- function(outcomes) {
intsctn <- c()
column2list <- function(x){list(x)}
pb <-
ProgressBar(max=length(outcomes),stepLength=1,newlineWhenDone=TRUE)
for (i in 2:length(outcomes)){
increase(pb)
outcomes_ <- apply(combn(outcomes,i),2,column2list)
for (j in 1:length(outcomes_)){outcomes_[[j]] <-
outcomes_[[j]][[1]]}
outcomes_container <- mclapply(outcomes_,prod,mc.cores=3)
intsctn[i] <- sum(unlist(outcomes_container))
}
intsctn <- intsctn[-1]
return(sum(outcomes) - sum(intsctn[which(which((intsctn %in% intsctn))
%% 2 == 1)]) + sum(intsctn[which(which((intsctn %in% intsctn)) %% 2 == 0)])
+ ((-1)^length(intsctn) * prod(outcomes)))
}
------------
PS This code has been tested on vectors of up to length(outcomes) == 25 and
it should be noted that ProgressBar() requires the R.utils package.
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