R/canberraDistanceW.R In LearnClust: Learning Hierarchical Clustering Algorithms

#' @title To calculate the Canberra distance applying weights.
#' @description To calculate the Canberra 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 Canberra 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 canberra 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)
#'
#'
#'
#' @export

res <- 0
if(is.null(weight)){
buffer <- 0
for (index in c(1:ncol(cluster1))) {
if((abs(cluster2[index]) + abs(cluster1[index])) == 0){
aux <- 0
} else {
aux <- (abs((cluster2[index] - cluster1[index]))/(abs(cluster2[index]) + abs(cluster1[index])))
}
buffer <- buffer + aux
}
res <- buffer
} else {
buffer <- 0
for (index in c(1:ncol(cluster1))) {
if((abs(cluster2[index]) + abs(cluster1[index])) == 0){
aux <- 0
} else {
aux <- (abs((cluster2[index] - cluster1[index]))/(abs(cluster2[index]) + abs(cluster1[index])))
}
buffer <- buffer + (weight[[index]] * aux)
}
res <- buffer
}
res
}

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