Sundar,
I'm not sure how much faster (or slower) this might be (perhaps Professor
Ripley will help on this one), but one can "trick" a function from the class
package called knn1 into doing this for you, I think. From your example:
> set.seed(1)
> A <- matrix(runif(12), 6, 2) # 6 x 2
> B <- matrix(runif(6), 3, 2) # 3 x 2
> m <- vector("numeric", nrow(B))
> for(j in 1:nrow(B)) {
+ d <- (A[, 1] - B[j, 1])^2 + (A[, 2] - B[j, 2])^2
+ m[j] <- which.min(d)
+ }
> m
[1] 3 2 3
Now using knn1:
> knn1(A,B,seq(1,nrow(A),1))
[1] 3 2 3
Levels: 1 2 3 4 5 6
Sean
----- Original Message -----
From: "Sundar Dorai-Raj" <[EMAIL PROTECTED]>
To: "R-help" <[EMAIL PROTECTED]>
Sent: Tuesday, January 27, 2004 3:00 PM
Subject: [R] distance between two matrices
> Hi all,
> Say I have a matrix A with dimension m x 2 and matrix B with
> dimension n x 2. I would like to find the row in A that is closest to
> the each row in B. Here's an example (using a loop):
>
> set.seed(1)
> A <- matrix(runif(12), 6, 2) # 6 x 2
> B <- matrix(runif(6), 3, 2) # 3 x 2
> m <- vector("numeric", nrow(B))
> for(j in 1:nrow(B)) {
> d <- (A[, 1] - B[j, 1])^2 + (A[, 2] - B[j, 2])^2
> m[j] <- which.min(d)
> }
>
> All I need is m[]. I would like to accomplish this without using the
> loop if possible, since for my real data n > 140K and m > 1K. I hope
> this makes sense.
>
> Thanks,
> Sundar
>
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