Hello Gerrit,
Thanks. Your proposal works in general, but I get memory allocation
errors with my actual 57,000 x 14,000 matrix.
The fix which I now use is to scale the data before I build the matrix;
see below.
Cheers,
Dirk
## Code Start -----------------------------
library(parallel)
rowscale <- function(.x) cbind(.x[,1:3], .x[,3] / sum(.x[,3]))
y <- split(x = x, f = x[,1])
localSocketCluster <- parallel::makeCluster(spec = 4, type = "SOCK")
y <- parallel::parLapply(cl = localSocketCluster, X = y, fun = rowscale)
parallel::stopCluster(cl = localSocketCluster)
x <- do.call(what = rbind, args = y)
## build the sparse matrix
M <- Matrix::sparseMatrix(dims = c(N_ROW, N_COL),
i = x[,1], j = x[,2], x = x[,4])
## Code End -----------------------------
On 16-01-13 03:23 AM, Gerrit Eichner wrote:
> Hello, Dirk,
>
> maybe I'm missing something, but to avoid your for-loop-approach doesn't
>
> M <- M/Matrix::rowSums(M)
>
> do what you want?
>
> Hth -- Gerrit
>
> ---------------------------------------------------------------------
> Dr. Gerrit Eichner Mathematical Institute, Room 212
> gerrit.eich...@math.uni-giessen.de Justus-Liebig-University Giessen
> Tel: +49-(0)641-99-32104 Arndtstr. 2, 35392 Giessen, Germany
> Fax: +49-(0)641-99-32109 http://www.uni-giessen.de/eichner
> ---------------------------------------------------------------------
>
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