Consider the array a:
> a <- array(c(1:9, 1:9), c(3,3,2))
> a
, , 1
[,1] [,2] [,3]
[1,] 1 4 7
[2,] 2 5 8
[3,] 3 6 9
, , 2
[,1] [,2] [,3]
[1,] 1 4 7
[2,] 2 5 8
[3,] 3 6 9
How do we efficiently compute the row sums of the matrices indexed by the third dimension, such that the result is:
[,1] [,2]
[1,] 12 12
[2,] 15 15
[3,] 18 18
??
The column sums are easy via the 'dims' argument of colSums():
> colSums(a, dims = 1)
but I cannot find a way to use rowSums() on the array to achieve the desired result, as it has a different interpretation of 'dims' to that of colSums().
It is simple to compute the desired row sums using:
> apply(a, 3, rowSums)
[,1] [,2]
[1,] 12 12
[2,] 15 15
[3,] 18 18
but that is just hiding the loop. Are there other efficient, truly vectorised, ways of computing the required row sums?
@Fojtasek's answer mentioned splitting up the array reminded me of the aperm() function which allows one to permute the dimensions of an array. As colSums() works, we can swap the first two dimensions using aperm() and run colSums() on the output.
> colSums(aperm(a, c(2,1,3)))
[,1] [,2]
[1,] 12 12
[2,] 15 15
[3,] 18 18
Some comparison timings of this and the other suggested R-based answers:
> b <- array(c(1:250000, 1:250000),c(5000,5000,2))
> system.time(rs1 <- apply(b, 3, rowSums))
user system elapsed
1.831 0.394 2.232
> system.time(rs2 <- rowSums3d(b))
user system elapsed
1.134 0.183 1.320
> system.time(rs3 <- sapply(1:dim(b)[3], function(i) rowSums(b[,,i])))
user system elapsed
1.556 0.073 1.636
> system.time(rs4 <- colSums(aperm(b, c(2,1,3))))
user system elapsed
0.860 0.103 0.966
So on my system the aperm() solution appears marginally faster:
> sessionInfo()
R version 2.12.1 Patched (2011-02-06 r54249)
Platform: x86_64-unknown-linux-gnu (64-bit)
However, rowSums3d() doesn't give the same answers as the other solutions:
> all.equal(rs1, rs2)
[1] "Mean relative difference: 0.01999992"
> all.equal(rs1, rs3)
[1] TRUE
> all.equal(rs1, rs4)
[1] TRUE
You could chop up the array into two dimensions, compute row sums on that, and then put the output back together the way you want it. Like so:
rowSums3d <- function(a){
m <- matrix(a,ncol=ncol(a))
rs <- rowSums(m)
matrix(rs,ncol=2)
}
> a <- array(c(1:250000, 1:250000),c(5000,5000,2))
> system.time(rowSums3d(a))
user system elapsed
1.73 0.17 1.96
> system.time(apply(a, 3, rowSums))
user system elapsed
3.09 0.46 3.74
I don't know about the most efficient way of doing this, but sapply seems to do well
a <- array(c(1:9, 1:9), c(3,3,2))
x1 <- sapply(1:dim(a)[3], function(i) rowSums(a[,,i]))
x1
[,1] [,2]
[1,] 12 12
[2,] 15 15
[3,] 18 18
x2 <- apply(a, 3, rowSums)
all.equal(x1, x2)
[1] TRUE
Which gives a speed improvement as follows:
> a <- array(c(1:250000, 1:250000),c(5000,5000,2))
> summary(replicate(10, system.time(rowSums3d(a))[3]))
Min. 1st Qu. Median Mean 3rd Qu. Max.
2.784 2.799 2.810 2.814 2.821 2.862
> summary(replicate(10, system.time(apply(a, 3, rowSums))[3]))
Min. 1st Qu. Median Mean 3rd Qu. Max.
2.730 2.755 2.766 2.776 2.788 2.839
> summary(replicate(10, system.time( sapply(1:dim(a)[3], function(i) rowSums(a[,,i])) )[3]))
Min. 1st Qu. Median Mean 3rd Qu. Max.
1.840 1.852 1.867 1.872 1.893 1.914
Timings were done on:
# Ubuntu 10.10
# Kernal Linux 2.6.35-27-generic
> sessionInfo()
R version 2.12.1 (2010-12-16)
Platform: x86_64-pc-linux-gnu (64-bit)
If you have a multi-core system you could write a simple C function and make use of the Open MP parallel threading library. I've done something similar for a problem of mine and I get an 8 fold increase on an 8 core system. The code will still work on a single-processor system and even compile on a system without OpenMP, perhaps with a smattering of #ifdef _OPENMP here and there.
Of course its only worth doing if you know that's what's taking most of the time. Do profile your code before optimising.
来源：https://stackoverflow.com/questions/5135415/efficiently-compute-the-row-sums-of-a-3d-array-in-r