Efficient way to Calculate the squared distances for a set ofvectors to a fixed vector

You could do something like this:

# data
nrows <- 20000L
ncols <- 5L
myVec <- array(rnorm(nrows * ncols), dim = c(nrows, ncols))
y <- rnorm(ncols)

temp <- t(myVec) - y
result <- colSums(temp * temp)

# check
all.equal(as.numeric(crossprod(myVec[1L, ] - y)), result[1L])
#...

(And don't use a data.frame, but a matrix.)

regards,
Enrico

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Von: r-help-bounces at r-project.org
[mailto:r-help-bounces at r-project.org] Im Auftrag von Wei Wu
Gesendet: Mittwoch, 24. August 2011 07:18
An: r-help at r-project.org
Betreff: [R] Efficient way to Calculate the squared distances
for a set ofvectors to a fixed vector

I am pretty new to R. So this may be an easy question for most of you.

I would like to calculate the squared distances of a large
set (let's say 20000) of vectors (let's say dimension of 5)
to a fixed vector.

Say I have a data frame MY_VECTORS with 20000 rows and 5
columns, and one 5x1 vector y. I would like to efficiently
calculate the squared distances?between each of the 20000
vectors in MY_VECTORS and y.

The squared distance between two vectors x and y can be calculated:
distance <- crossprod(x-y)

Without looping, what is?the efficient code to achieve this?

Thanks.