Hello,
If your matrix is in the order of 300x300, the problem of extracting all
possible submatrices and applying a function will allways be a large
one, but the use of ?combn may reduce it a bit if the order of
rows/columns in the submatrices doesn't matter. It can reduce it from
300^4 = 8.1e+09 to 2.0e+09 submatrices, a factor of 4.
See the example below.
# Make up some data.
nr <- nc <- 10
x <- matrix(rnorm(nr*nc), nrow = nr)
cr <- combn(nr, 2)
cc <- combn(nc, 2)
fun <- function(rr, cc, xx, FUN) FUN(xx[rr, cc])
apply(cr, 2, function(i)
apply(cc, 2, function(j)
fun(cr[, i], cc[, j], x, sum)
)
)
Note that this hides away the quartic nature of the algorithm.
Hope this helps,
Rui Barradas
Em 22-10-2012 18:10, CMB123 escreveu:
Hi all,
I'm working with a large data set (on the order of 300X300) and trying to
apply a function which compares the elements of all possible 2x2
submatrices. There are rc(r-1)(c-1) such submatrices, so obviously the naive
method of looping through the rows and columns is computationally unfeasible
for my data set:
for(i in 1:(nrow(data)-1)) {
for(j in (i+1):nrow(data)) {
for (m in 1:(ncol(data)-1)) {
for (n in (m+1):ncol(data)) {
I have tried using the outer and apply functions to vectorize the task, but
I can't seem to figure out the best method (or any method, for that matter)
to help with what I'm trying to do.
Any and all help is much appreciated! Thanks in advance,
C
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