#' @title Silhouette Plot for K-means clustering
#' @description The silhouette provides a representation of how well each point
#'   is represented by its cluster in comparison to the closest neighboring
#'   cluster. It computes for each point the difference between the average
#'   similarity to all points in the same cluster and to all points in the
#'   closest neighboring cluster. This difference it normalize such that it can
#'   take values between -1 and 1 with higher values reflecting better
#'   representation of a point by its cluster.
#' @param object \code{DISCBIO} class object.
#' @param K A numeric value of the number of clusters
#' @importFrom stats as.dist cor
#' @importFrom cluster silhouette
#' @return A silhouette plot
setGeneric(
  name = "plotSilhouette",
  def = function(object, K) standardGeneric("plotSilhouette")
)

#' @export
#' @rdname plotSilhouette
setMethod(
  f = "plotSilhouette",
  signature = "DISCBIO",
  definition = function(object, K) {
    # ======================================================================
    # Validation
    # ======================================================================
    ran_clustexp <- length(object@kmeans$kpart) > 0
    ran_exprmclust <- length(object@MBclusters) > 0
    if (ran_clustexp) {
      kpart <- object@kmeans$kpart
      DIS <- object@distances
    } else if (ran_exprmclust) {
      kpart <- object@MBclusters$clusterid
      y <- clustfun(
        object@fdata,
        clustnr = 3, bootnr = 50,
        metric = "pearson", do.gap = TRUE, SE.method = "Tibs2001SEmax",
        SE.factor = .25, B.gap = 50, cln = 0, rseed = NULL, quiet = TRUE
      )
      DIS <- as.matrix(y$di)
    } else {
      stop("run clustexp or exprmclust before plotSilhouette")
    }
    if (length(unique(kpart)) < 2) {
      stop("only a single cluster: no silhouette plot")
    }
    # ======================================================================
    # Plotting
    # ======================================================================
    col <- c("black", "blue", "green", "red", "yellow", "gray")
    distances <- DIS
    si <- silhouette(kpart, distances)
    plot(si, col = col[1:K])
  }
)