Sorry a correction to my last posting. I had accumulate switched
between prod and cumprod and I had also forgotten to included time for
conversion from list back to matrix for cumprod. Now as Chuck stated
the results for Reduce are about the same or worse than a loop.
regards--jeff
Columns 10 100 1000 10000
100000
Rows 1000000 100000 10000 1000 100
cumprod Loop 1.0 1.1 1.3 1.2
3.0
Apply 26.4 3.4 1.8
1.3 1.4
Reduce 1.1 0.9 1.2
1.2 5.3
prod Loop 0.3 0.3 0.4
0.4 0.9
Apply 30.0 2.7 0.7
0.5 0.8
Reduce 0.7 0.6 0.7
0.8 3.1
N=10000000
xmat=matrix(runif(N),ncol=10)
system.time(cumprod.matrix(xmat))
system.time(t(apply(xmat,1,cumprod)))
system.time(do.call("cbind",Reduce("*",as.data.frame(xmat),accumulate=TRUE)))
system.time(prod.matrix(xmat))
system.time(apply(xmat,1,prod))
system.time(Reduce("*",as.data.frame(xmat),accumulate=FALSE))
xmat=matrix(runif(N),ncol=100)
system.time(cumprod.matrix(xmat))
system.time(t(apply(xmat,1,cumprod)))
system.time(do.call("cbind",Reduce("*",as.data.frame(xmat),accumulate=TRUE)))
system.time(prod.matrix(xmat))
system.time(apply(xmat,1,prod))
system.time(Reduce("*",as.data.frame(xmat),accumulate=FALSE))
xmat=matrix(runif(N),ncol=1000)
system.time(cumprod.matrix(xmat))
system.time(t(apply(xmat,1,cumprod)))
system.time(do.call("cbind",Reduce("*",as.data.frame(xmat),accumulate=TRUE)))
system.time(prod.matrix(xmat))
system.time(apply(xmat,1,prod))
system.time(Reduce("*",as.data.frame(xmat),accumulate=FALSE))
xmat=matrix(runif(N),ncol=10000)
system.time(cumprod.matrix(xmat))
system.time(t(apply(xmat,1,cumprod)))
system.time(do.call("cbind",Reduce("*",as.data.frame(xmat),accumulate=TRUE)))
system.time(prod.matrix(xmat))
system.time(apply(xmat,1,prod))
system.time(Reduce("*",as.data.frame(xmat),accumulate=FALSE))
xmat=matrix(runif(N),ncol=100000)
system.time(cumprod.matrix(xmat))
system.time(t(apply(xmat,1,cumprod)))
system.time(do.call("cbind",Reduce("*",as.data.frame(xmat),accumulate=TRUE)))
system.time(prod.matrix(xmat))
system.time(apply(xmat,1,prod))
system.time(Reduce("*",as.data.frame(xmat),accumulate=FALSE))
Jeff Laake wrote:
Thanks for the tips on inline, jit and Reduce. The latter was exactly
what I wanted although the loop
is still the fastest for the simple product (accumulate=TRUE for
reduce). With regards to Moshe's comment,
I was just surprised by the timing difference. I tend to use apply
without giving it much thought. After profiling the code
it became apparent that a loop was better in this case. I was just
surprised that a loop was still as good
when the columns were 10 times the rows.
I'm very intrigued by the inline package but couldn't find any
documentation on the compiler I need with
a Windows machine to make it work. Any hints would be very much
appreciated especially in regards to
FORTRAN which was my first language some 35 years ago. I have MS
FORTRAN 90 although I've not touched
it for over 6 years thanks to the developers of R.
Thanks much for the help. --jeff
Elapsed times from system.time. see code below
Columns 10 100 1000 10000 100000
Rows 1000000 100000 10000 1000 100
cumprod Loop 1.0 1.0 1.3 1.2 3.0
Apply 27.3 3.4 1.8 1.2 1.4
Reduce 0.5 0.7 0.7 0.9 3.2
prod Loop 0.3 0.3 0.4 0.4 0.9
Apply 30.0 2.7 0.7 0.6 0.8
Reduce 0.6 0.6 0.8 1.0 4.7
N=10000000
xmat=matrix(runif(N),ncol=10)
system.time(cumprod.matrix(xmat))
system.time(t(apply(xmat,1,cumprod)))
system.time(Reduce("*",as.data.frame(xmat),accumulate=FALSE))
system.time(prod.matrix(xmat))
system.time(apply(xmat,1,prod))
system.time(Reduce("*",as.data.frame(xmat),accumulate=TRUE))
xmat=matrix(runif(N),ncol=100)
system.time(cumprod.matrix(xmat))
system.time(t(apply(xmat,1,cumprod)))
system.time(Reduce("*",as.data.frame(xmat),accumulate=FALSE))
system.time(prod.matrix(xmat))
system.time(apply(xmat,1,prod))
system.time(Reduce("*",as.data.frame(xmat),accumulate=TRUE))
xmat=matrix(runif(N),ncol=1000)
system.time(cumprod.matrix(xmat))
system.time(t(apply(xmat,1,cumprod)))
system.time(Reduce("*",as.data.frame(xmat),accumulate=FALSE))
system.time(prod.matrix(xmat))
system.time(apply(xmat,1,prod))
system.time(Reduce("*",as.data.frame(xmat),accumulate=TRUE))
xmat=matrix(runif(N),ncol=10000)
system.time(cumprod.matrix(xmat))
system.time(t(apply(xmat,1,cumprod)))
system.time(Reduce("*",as.data.frame(xmat),accumulate=FALSE))
system.time(prod.matrix(xmat))
system.time(apply(xmat,1,prod))
system.time(Reduce("*",as.data.frame(xmat),accumulate=TRUE))
xmat=matrix(runif(N),ncol=100000)
system.time(cumprod.matrix(xmat))
system.time(t(apply(xmat,1,cumprod)))
system.time(Reduce("*",as.data.frame(xmat),accumulate=FALSE))
system.time(prod.matrix(xmat))
system.time(apply(xmat,1,prod))
system.time(Reduce("*",as.data.frame(xmat),accumulate=TRUE))
Charles C. Berry wrote:
On Sun, 17 Aug 2008, Jeff Laake wrote:
I spent a lot of time searching and came up empty handed on the
following query. Is there an equivalent to rowSums that does product
or cumulative product and avoids use of apply or looping? I found a
rowProd in a package but it was a convenience function for apply. As
part of a likelihood calculation called from optim, I’m computing
products and cumulative products of rows of matrices with far more
rows than columns. I started with apply and after some thought
realized that a loop of columns might be faster and it was
substantially faster (see below). Because the likelihood function is
called many times I’d like to speed it up even more if possible.
You might check out the 'inline' or 'jit' packages.
Otherwise, if you can as easily treat xmat as a list (or data.frame),
Reduce( "*", xmat.data.frame, accumulate=want.cumprod )
(where want.cumprod is FALSE for product, TRUE for cumulative
product) will be a bit faster in many circumstances. However, this
advantage is lost if you must retain xmat as a matrix since
converting it to a data.frame seems to require more time than you save.
HTH,
Chuck
Below is an example showing the cumprod.matrix and prod.matrix
looping functions that I wrote and some timing comparisons to the
use of apply for different column and row dimensions. At this point
I’m better off with looping but I’d like to hear of any further
suggestions.
Thanks –jeff
prod.matrix=function(x)
+ {
+ y=x[,1]
+ for(i in 2:dim(x)[2])
+ y=y*x[,i]
+ return(y)
+ }
cumprod.matrix=function(x)
+ {
+ y=matrix(1,nrow=dim(x)[1],ncol=dim(x)[2])
+ y[,1]=x[,1]
+ for (i in 2:dim(x)[2])
+ y[,i]=y[,i-1]*x[,i]
+ return(y)
+ }
N=10000000
xmat=matrix(runif(N),ncol=10)
system.time(cumprod.matrix(xmat))
user system elapsed
1.07 0.09 1.15
system.time(t(apply(xmat,1,cumprod)))
user system elapsed
29.27 0.21 29.50
system.time(prod.matrix(xmat))
user system elapsed
0.29 0.00 0.30
system.time(apply(xmat,1,prod))
user system elapsed
30.69 0.00 30.72
xmat=matrix(runif(N),ncol=100)
system.time(cumprod.matrix(xmat))
user system elapsed
1.05 0.13 1.18
system.time(t(apply(xmat,1,cumprod)))
user system elapsed
3.55 0.14 3.70
system.time(prod.matrix(xmat))
user system elapsed
0.38 0.01 0.39
system.time(apply(xmat,1,prod))
user system elapsed
2.87 0.00 2.89
xmat=matrix(runif(N),ncol=1000)
system.time(cumprod.matrix(xmat))
user system elapsed
1.30 0.18 1.46
system.time(t(apply(xmat,1,cumprod)))
user system elapsed
1.77 0.27 2.05
system.time(prod.matrix(xmat))
user system elapsed
0.46 0.00 0.47
system.time(apply(xmat,1,prod))
user system elapsed
0.7 0.0 0.7
xmat=matrix(runif(N),ncol=10000)
system.time(cumprod.matrix(xmat))
user system elapsed
1.28 0.00 1.29
system.time(t(apply(xmat,1,cumprod)))
user system elapsed
1.19 0.08 1.26
system.time(prod.matrix(xmat))
user system elapsed
0.40 0.00 0.41
system.time(apply(xmat,1,prod))
user system elapsed
0.57 0.00 0.56
xmat=matrix(runif(N),ncol=100000)
system.time(cumprod.matrix(xmat))
user system elapsed
3.18 0.00 3.19
system.time(t(apply(xmat,1,cumprod)))
user system elapsed
1.42 0.21 1.64
system.time(prod.matrix(xmat))
user system elapsed
1.05 0.00 1.05
system.time(apply(xmat,1,prod))
user system elapsed
0.82 0.00 0.81
R.Version()
$platform
[1] "i386-pc-mingw32"
.
.
.
$version.string
[1] "R version 2.7.1 (2008-06-23)"
______________________________________________
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.
Charles C. Berry (858) 534-2098
Dept of Family/Preventive
Medicine
E mailto:[EMAIL PROTECTED] UC San Diego
http://famprevmed.ucsd.edu/faculty/cberry/ La Jolla, San Diego
92093-0901
------------------------------------------------------------------------
______________________________________________
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.
______________________________________________
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.
______________________________________________
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.
