R/distEuclidean.R

In rintakumpu/custom-kmeans: K-means Clustering

Defines functions distEuclidean

Documented in distEuclidean

distEuclidean <-
function(x,y) {
  z <- matrix(0, nrow = nrow(x), ncol = nrow(y))
  for (k in 1:nrow(y)) {
    z[, k] <- sqrt(colSums((t(x) - y[k, ])^2))
  }
  return(z)
}