Function to calculate Euclidean distance in R -

i trying implement knn classifier in r scratch on iris data set , part of have written function calculate euclidean distance. here code.

known_data <- iris[1:15,c("sepal.length", "petal.length", "class")] unknown_data <- iris[16,c("sepal.length", "petal.length")]  # euclidean distance  euclidean_dist <- function(k,unk) {  distance <- 0  for(i in 1:nrow(k))  distance[i] <- sqrt((k[,1][i] - unk[,1][i])^2 + (k[,2][i] - unk[,2][i])^2)  return(distance) }   euclidean_dist(known_data, unknown_data) 

however, when call function it's returning first value correctly , rest na. show have gone wrong code? in advance.

the aim calculate distance between ith row of known_data, , single unknown_data point.

how fix code

when calculate distance[i], you're trying access ith row of unknown data point, doesn't exits, , hence na. believe code should run fine if make following edits:

known_data <- iris[1:15,c("sepal.length", "petal.length", "class")]  unknown_data <- iris[16,c("sepal.length", "petal.length")]  # euclidean distance euclidean_dist <- function(k,unk) {   # make distance vector [although not technically required]   distance <- rep(0, nrow(k))    for(i in 1:nrow(k))     # change unk[,1][i] unk[1,1] , unk[,2][i]     distance[i] <- sqrt((k[,1][i] - unk[1,1])^2 + (k[,2][i] - unk[1,2])^2)    return(distance) }   euclidean_dist(known_data, unknown_data) 

one final note - in version of r i'm using, known dataset uses species opposed class column

an alternative method

as suggested @roman luštrik, entire aim of getting euclidean distances can achieved simple one-liner:

sqrt((known_data[, 1] - unknown_data[, 1])^2 + (known_data[, 2] - unknown_data[, 2])^2) 

this similar function wrote, in vectorised form, rather through loop, preferable way of doing things in r.