I corrected your code a bit and put it into a function, f0, to
make testing easier. I also made a small dataset to make
testing easier. Then I made a new function f1 which does
what f0 does in a vectorized manner:
x <- array(rnorm(50 * 50 * 50 * 91, 0, 2), dim=c(50, 50, 50, 91))
xsmall <- array(log(seq_len(2 * 2 * 2 * 91)), dim=c(2, 2, 2, 91))
f0 <- function(x) {
data_reduced <- array(0, dim=c(dim(x)[1:3], trunc(dim(x)[4]/3)))
reduce <- seq(1, dim(x)[4]-1, by=3)
for( i in 1:length(reduce) ) {
data_reduced[ , , , i] <- apply(x[ , , , reduce[i] : (reduce[i]+2) ],
1:3, mean)
}
data_reduced
}
f1 <- function(x) {
reduce <- seq(1, dim(x)[4]-1, by=3)
data_reduced <- (x[, , , reduce] + x[, , , reduce+1] + x[, , , reduce+2])
/ 3
data_reduced
}
The results were:
> system.time(v1 <- f1(x))
user system elapsed
0.280 0.040 0.323
> system.time(v0 <- f0(x))
user system elapsed
73.760 0.060 73.867
> all.equal(v0, v1)
[1] TRUE
>> "I thought apply would already vectorize, rather than loop over every
>> coordinate."
No, you have that backwards. Use *apply functions when you cannot figure
out how to vectorize.
Bill Dunlap
Spotfire, TIBCO Software
wdunlap tibco.com
> -----Original Message-----
> From: r-help-boun...@r-project.org [mailto:r-help-boun...@r-project.org] On
> Behalf Of Martin Batholdy
> Sent: Wednesday, October 05, 2011 10:40 AM
> To: R Help
> Subject: [R] speed up this algorithm (apply-fuction / 4D array)
>
> Hi,
>
>
> I have this sample-code (see above) and I was wondering wether it is possible
> to speed things up.
>
>
>
> What this code does is the following:
>
> x is 4D array (you can imagine it as x, y, z-coordinates and a
> time-coordinate).
>
> So x contains 50x50x50 data-arrays for 91 time-points.
>
> Now I want to reduce the 91 time-points.
> I want to merge three consecutive time points to one time-points by
> calculating the mean of this three
> time-points for every x,y,z coordinate.
>
> The reduce-sequence defines which time-points should get merged.
> And the apply-function in the for-loop calculates the mean of the three
> 3D-Arrays and puts them into a
> new 4D array (data_reduced).
>
>
>
> The problem is that even in this example it takes really long.
> I thought apply would already vectorize, rather than loop over every
> coordinate.
>
> But for my actual data-set it takes a really long time ... So I would be
> really grateful for any
> suggestions how to speed this up.
>
>
>
>
> x <- array(rnorm(50 * 50 * 50 * 90, 0, 2), dim=c(50, 50, 50, 91))
>
>
>
> data_reduced <- array(0, dim=c(50, 50, 50, 90/3))
>
> reduce <- seq(1,90, 3)
>
>
>
> for( i in 1:length(reduce) ) {
>
> data_reduced[ , , , i] <- apply(x[ , , , reduce[i] :
> (reduce[i]+3) ], 1:3, mean)
> }
>
> ______________________________________________
> R-help@r-project.org mailing list
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> PLEASE do read the posting guide http://www.R-project.org/posting-guide.html
> and provide commented, minimal, self-contained, reproducible code.
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