# R/eDistanceW.R In LearnClust: Learning Hierarchical Clustering Algorithms

#### Documented in edistanceW

```#' @title To calculate the Euclidean distance applying weights.
#' @description To calculate the Euclidean distance between clusters applying weights given.
#' @param cluster1 is a cluster.
#' @param cluster2 is a cluster.
#' @param weight is a numeric vector.
#' @details The function calculates the Euclidean distance value from \code{cluster1} and \code{cluster2}, applying weights to the cluster's components.
#' @author Roberto Alcántara \email{roberto.alcantara@@edu.uah.es}
#' @author Universidad de Alcalá de Henares
#' @return Euclidean distance applying weights value.
#' @examples
#'
#' cluster1 <- matrix(c(1,2),ncol=2)
#' cluster2 <- matrix(c(1,3),ncol=2)
#'
#' weight1 <- c(0.4,0.6)
#' weight2 <- c(2,12)
#'
#' edistanceW(cluster1,cluster2,weight1)
#'
#' edistanceW(cluster1,cluster2,weight2)
#'
#' @export

edistanceW <- function(cluster1,cluster2,weight){
res <- 0
if(is.null(weight)){
buffer <- 0
for (index in c(1:ncol(cluster1))) {
aux <- (cluster2[index]-cluster1[index])^2
buffer <- buffer + aux
}
res <- sqrt(buffer)
} else {
buffer <- 0
for (index in c(1:ncol(cluster1))) {
aux <- weight[[index]] * (cluster2[index]-cluster1[index])^2
buffer <- buffer + aux
}
res <- sqrt(buffer)
}
res
}
```

## Try the LearnClust package in your browser

Any scripts or data that you put into this service are public.

LearnClust documentation built on Nov. 30, 2020, 1:09 a.m.