Want to share your content on R-bloggers? click here if you have a blog, or here if you don't.

For a recent project I needed to calculate the pairwise distances of a set of observations to a set of cluster centers. In MATLAB you can use the pdist function for this. As far as I know, there is no equivalent in the R standard packages. So I looked into writing a fast implementation for R. Turns out that vectorizing makes it about 40x faster. Using Rcpp is another 5-6x faster, ending up with a 225x speed-up over the naive implementation.

At the start I wrote a naive (and very slow) implementation that look liked this:

```naive_pdist <- function(A,B) {
# A: matrix with obersvation vectors
#         (nrow = number of observations)
#
# B: matrix with another set of vectors
#          (e.g. cluster centers)
result = matrix(ncol=nrow(B), nrow=nrow(A))
for (i in 1:nrow(A))
for (j in 1:nrow(B))
result[i,j] = sqrt(sum( (A[i,] - B[j,])^2 ))

result
}
```

When I realized that this is too slow, I started looking for an implementation and I found the pdist CRAN package, which is way faster:

The speed up made me curious about how pdist was implemented in this package. To my disappointment it is the same naive method only written in C (and using float, not double precision) — no vectorization and no tricks involved. So I was pretty sure there was room for improvement.

In search for tricks on computing the pairwise distance a blog post from Alex Smola turned up. He suggest to “use the second binomial formula to decompose the distance into norms of vectors in A and B and an inner product between them”. Translated into R code this solution looks like this:

```vectorized_pdist <- function(A,B)
an = apply(A, 1, function(rvec) crossprod(rvec,rvec))
bn = apply(B, 1, function(rvec) crossprod(rvec,rvec))

m = nrow(A)
n = nrow(B)

tmp = matrix(rep(an, n), nrow=m)
tmp = tmp +  matrix(rep(bn, m), nrow=m, byrow=TRUE)
sqrt( tmp - 2 * tcrossprod(A,B) )
}
```

Now that I knew how to implement pdist with a couple of simple operations, I wanted to know how much faster a C (or C++) implementation would be. Thanks to the excellent Rcpp and RcppArmadillo package, it is easy to translate the above R code into C++:

```#include

using namespace Rcpp;

// [[Rcpp::export]]
NumericMatrix fastPdist2(NumericMatrix Ar, NumericMatrix Br) {
int m = Ar.nrow(),
n = Br.nrow(),
k = Ar.ncol();
arma::mat A = arma::mat(Ar.begin(), m, k, false);
arma::mat B = arma::mat(Br.begin(), n, k, false);

arma::colvec An =  sum(square(A),1);
arma::colvec Bn =  sum(square(B),1);

arma::mat C = -2 * (A * B.t());
C.each_col() += An;
C.each_row() += Bn.t();

return wrap(sqrt(C));
}
```

This C++ implementation turns out to be another 6x faster than the vectorized R implementation:

## All implementations compared

The time measurements for all implementations:

```Unit: milliseconds
expr         min          lq      median          uq        max  neval
vectorized_pdist(A, B)   26.667005   30.299216   32.945532   34.548596   134.8368    100
fastPdist(A, B)    5.357734    5.581193    5.693534    5.798465   109.9736    100
naive_pdist(A, B) 1259.290444 1280.897937 1290.150653 1320.467180  1425.3864    100
pdist::pdist(A, B)   98.825835  101.955146  103.719962  105.843313   205.7123    100
```

and the speed up among all implementations:

vectorized C++ vectorized R naive C naive R
vectorized C++ 1.00 5.79 18.23 226.74
vectorized R 0.17 1. 00 3.15 39.15
naive C 0.05 0.32 1.00 12.44
naive R 0.00 0.03 0.08 1.00

## Conclusion

In my example the (naive) C implementation only acheived a 12x speed up, while the improved R implementation was about 40x faster. These findings agree with what is preached in various blog posts and guides about R: first try to vectorize code, then try to find a faster method (algorithm), and only as last step consider using a faster language.