#' @title Jaccard’s similarity

#' @description Robustness of the clusters can be assessed by Jaccard’s

#'   similarity, which reflects the reproducibility of individual clusters

#'   across bootstrapping runs. Jaccard’s similarity is the intersect of two

#'   clusters divided by the union.

#' @export

#' @param object \code{DISCBIO} class object.

#' @param Clustering Clustering has to be one of the following:

#'   ["K-means","MB"]. Default is "K-means"

#' @param K A numeric value of the number of clusters

#' @param plot if `TRUE`, plots the mean Jaccard similarities

#' @param R number of bootstrap replicates

#' @importFrom graphics barplot box

#' @return A plot of the mean Jaccard similarity coefficient per cluster.

Jaccard <- function(object, Clustering = "K-means", K, plot = TRUE, R = 100) {

  JACCARD <- vector()

  # Validation

  if (!(Clustering %in% c("K-means", "MB"))) {

    stop("Clustering has to be either K-means or MB")

  }

  for (i in 1:K) {

    # Optimize by avoiding if every loop. Only thing variable is data

    if (Clustering == "K-means") {

      target_col <- object@kmeans$kpart

    } else if (Clustering == "MB") {

      target_col <- object@MBclusters$clusterid

    }

    results <- bootstrap(object@fdata[, which(target_col == i)], R)

    # to get the mean of all bootstrappings (mean of mean Jaccard values)

    JACCARD[i] <- round(mean(results), digits = 3)

  }

  if (plot) {

    barplot(

      height    = JACCARD,

      names.arg = seq_len(length(JACCARD)),

      ylab      = "Mean Jaccard's similarity values",

      xlab      = "Clusters",

      las       = 1,

      ylim      = c(0, 1),

      col       = c("black", "blue", "green", "red", "yellow", "gray")

    )

    box()

  }

  return(JACCARD)

}