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 > -----Ursprüngliche Nachricht----- > Von: r-help-boun...@r-project.org > [mailto:r-help-boun...@r-project.org] Im Auftrag von Wei Wu > Gesendet: Mittwoch, 24. August 2011 07:18 > An: r-help@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. > > ______________________________________________ > 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.
