On Nov 20, 2009, at 5:05 PM, Andrew Barr wrote:
Hi R folks,
I have a massive array (object name "points") in the following form
[,1] [,2]
[1,] 1369 22
[2,] 1370 22
[3,] 1368 23
[4,] 1369 23
[5,] 1370 23
[6,] 1371 23
(10080 rows truncated)
mtx <- matrix(scan(textConnection("1369 22
1370 22
1368 23
1369 23
1370 23
1371 23")), ncol=2, byrow=TRUE)
> dist(mtx)
1 2 3 4 5
2 1.000000
3 1.414214 2.236068
4 1.000000 1.414214 1.000000
5 1.414214 1.000000 2.000000 1.000000
6 2.236068 1.414214 3.000000 2.000000 1.000000
These represent pixel coordinates of interest in a jpeg image. I need
to find the distance from each point to all other points of interest.
The only way I can see to do this is by pythagoras and nested for
loops.
distance<-function(x1,y1,x2,y2){sqrt((x2-x1)^2 + (y2-y1)^2)}
#pythagoras
for(i in 1:nrow(points)){
for(j in 1:nrow(points)){
dist<-c(dist,distance(points[i,1],point.array.indices[i,2],points[j,
1],points[j,2]))
}
}
This is obviously prohibitively slow with >10000 rows in the array.
I'm not sure that your request to make this fast is reasonable. The
size of the resulting matrix will be more than most Windows machines
will support.
Any thoughts on how to do this without for loops?
I can do that operation in 8 characters ... but ...
I apologize in
advance if there is an obvious way around this.
I think you should be thinking about apologizing for expecting a
relaxation of the laws of combinatorics and geometry.
Thanks!
Andrew Barr
University of Texas at Austin
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David Winsemius, MD
Heritage Laboratories
West Hartford, CT
______________________________________________
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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.
