here's another one - which is easier to generalize:
x <- array(rnorm(50 * 50 * 50 * 91, 0, 2), dim=c(50, 50, 50, 91))
y <- x [,,,1:90] # decide yourself what to do with slice 91, but
# 91 is not divisible by 3
system.time ({
dim (y) <- c (50, 50, 50, 3, 90 %/% 3)
y <- aperm (y, c (4, 1:3, 5))
v2 <- colMeans (y)
})
User System verstrichen
0.32 0.08 0.40
(my computer is a bit slower than Bill's:)
> system.time (v1 <- f1 (x))
User System verstrichen
0.360 0.030 0.396
Claudia
Am 05.10.2011 20:24, schrieb William Dunlap:
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)
}
______________________________________________
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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.
______________________________________________
R-help@r-project.org mailing list
https://stat.ethz.ch/mailman/listinfo/r-help
PLEASE do read the posting guide http://www.R-project.org/posting-guide.html
and provide commented, minimal, self-contained, reproducible code.
--
Claudia Beleites
Spectroscopy/Imaging
Institute of Photonic Technology
Albert-Einstein-Str. 9
07745 Jena
Germany
email: claudia.belei...@ipht-jena.de
phone: +49 3641 206-133
fax: +49 2641 206-399
______________________________________________
R-help@r-project.org mailing list
https://stat.ethz.ch/mailman/listinfo/r-help
PLEASE do read the posting guide http://www.R-project.org/posting-guide.html
and provide commented, minimal, self-contained, reproducible code.
