R/normalizeWeight.R

In LearnClust: Learning Hierarchical Clustering Algorithms

#' @title To normalize weight values.
#' @description To normalize weight values if \code{normalize} = TRUE.
#' @param normalize is a boolean value.
#' @param weight is a numeric vector.
#' @param data is a data.frame.
#' @details This function allows users to normalize weights.
#' @details If there is not any weight, the function will create a numeric vector of "1".
#' @details If normalize = TRUE, the function will make every weight value as a "[0:1]" value.
#' @details If normalize = FALSE, the function will not make any changes, weights will be the same.
#' @author Roberto Alcántara \email{roberto.alcantara@@edu.uah.es}
#' @author Juan José Cuadrado \email{jjcg@@uah.es}
#' @author Universidad de Alcalá de Henares
#' @return Numeric vector with updated weights.
#' @examples
#'
#' data <- data.frame(matrix(c(1:10),ncol = 2))
#'
#' weight1 <- c(0.6,0.4)
#' weight2 <- c(2,4)
#'
#' normalizeWeight(FALSE, weight1, data)
#'
#' normalizeWeight(TRUE, weight2, data)
#'
#' normalizeWeight(FALSE, weight2, data)
#'
#' @export


normalizeWeight <- function(normalize,weight,data){
if(is.null(weight)){
  weight <- c()
  value <- 1
  for (j in c(1:ncol(data))) {
    weight <- c(weight, value)
  }
}
res <- c()
if(normalize){
  total <- sum(weight)
  for (index in c(1:length(weight))) {
    currentValue <- weight[index]
    normalizedValue <- currentValue/total
    res  <- c(res, normalizedValue)
  }

} else {
  res <- weight
}
res
}