I have this R code:
> coef
[1] 1.5 2.4 3.9 4.4
> y
[,1] [,2] [,3] [,4]
[1,] 1 2 12 45
[2,] 5 6 7 8
[3,] 9 10 2 12
Two more possibilities: sweep
and scale
(the latter only operates columnwise, and seems to me to be a bit of hack).
coef <- c(1.5,2.4,3.9,4.4)
y <- matrix(c(seq(1,17,by=4),
seq(2,18,by=4),
c(12,7,2,15,39,
45,8,12,45,7)),
ncol=4)
t(t(y)*coef)
t(apply(y,1,"*",coef))
sweep(y,2,coef,"*")
scale(y,center=FALSE,scale=1/coef)
library(rbenchmark)
benchmark(t(t(y)*coef),
y %*% diag(coef),
t(apply(y,1,"*",coef)),
sweep(y,2,coef,"*"),
scale(y,center=FALSE,scale=1/coef),
replications=1e4)
test replications elapsed relative
5 scale(y, center = FALSE, scale = 1/coef) 10000 0.990 4.342105
4 sweep(y, 2, coef, "*") 10000 0.846 3.710526
3 t(apply(y, 1, "*", coef)) 10000 1.537 6.741228
1 t(t(y) * coef) 10000 0.228 1.000000
2 y %*% diag(coef) 10000 0.365 1.600877
edit: added y %*% diag(coef)
from @baptiste [not fastest, although it might be so for a big problem with a sufficiently optimized BLAS package ...] [and it was fastest in another trial, so I may just not have had a stable estimate]
edit: fixed typo in t(t(y)*coef)
[thanks to Timur Shtatland] (but did not update timings, so they might be slightly off ...)
I also tried library(Matrix); y %*% Diagonal(x=coef)
, which is very slow for this example but might be fast for a large matrix (??). (I also tried constructing the diagonal matrix just once, but even multiplication by a predefined matrix was slow in this example (25x slower than the best, vs. 47x slower when defining the matrix on the fly.)
I have a mild preference for sweep
as I think it expresses most clearly the operation being done ("multiply the columns by the elements of coef
")