You can always just pull the last one off the list. When running things in
parallel, what does the "last one" mean? Do you want the last from each of
the parallel threads, or just the last one on the list? You might want to
put some flag on the data being returned so you can determine which one you
want to process.
Jim Holtman
Data Munger Guru
What is the problem that you are trying to solve?
Tell me what you want to do, not how you want to do it.
On Sat, Aug 1, 2015 at 3:19 PM, Martin Spindler <martin.spind...@gmx.de>
wrote:
> Dear Jim,
>
> Thank you very much for your response. It seems to work now, but the
> return value is not the required matrix but a list of matrices (one for
> each repition j).
> Any idea how it is possible to return only the last matrix and not all?
>
> Thanks and best,
>
> Martin
>
>
>
> Gesendet: Freitag, 31. Juli 2015 um 18:22 Uhr
> Von: "jim holtman" <jholt...@gmail.com>
> An: "Martin Spindler" <martin.spind...@gmx.de>
> Cc: "r-help@r-project.org" <r-help@r-project.org>
> Betreff: Re: [R] R parallel / foreach - aggregation of results
>
> Try this chance to actually return values:
>
>
> library(doParallel)
> Simpar3 <- function(n1) {
> L2distance <- matrix(NA, ncol=n1, nrow=n1)
> data <- rnorm(n1)
> diag(L2distance)=0
> cl <- makeCluster(4)
> registerDoParallel(cl)
> x <- foreach(j=1:n1) %dopar% {
> library(np)
> datj <- data[j]
> for(k in j:n1) {
> L2distance[j,k] <- k*datj
> }
> L2distance # return the value
> }
> stopCluster(cl)
> return(x)
> }
> Res <- Simpar3(100)
>
>
> Jim Holtman
> Data Munger Guru
>
> What is the problem that you are trying to solve?
> Tell me what you want to do, not how you want to do it.
> On Fri, Jul 31, 2015 at 8:39 AM, Martin Spindler <martin.spind...@gmx.de>
> wrote:Dear all,
>
> when I am running the code attached below, it seems that no results are
> returned, only the predefined NAs. What mistake do I make?
> Any comments and help is highly appreciated.
>
> Thanks and best,
>
> Martin
>
>
> Simpar3 <- function(n1) {
> L2distance <- matrix(NA, ncol=n1, nrow=n1)
> data <- rnorm(n1)
> diag(L2distance)=0
> cl <- makeCluster(4)
> registerDoParallel(cl)
> foreach(j=1:n1) %dopar% {
> library(np)
> datj <- data[j]
> for(k in j:n1) {
> L2distance[j,k] <- k*datj
> }
> }
> stopCluster(cl)
> return(L2distance)
> }
>
> Res <- Simpar3(100)
>
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>
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>
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