R/eDistanceW.R

In LearnClust: Learning Hierarchical Clustering Algorithms

#' @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 Juan José Cuadrado \email{jjcg@@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
}