#' @title Computing tSNE

#' @description This function is used to compute the t-Distributed Stochastic

#'   Neighbor Embedding (t-SNE).

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

#' @param rseed Random integer to to yield reproducible maps across different

#' runs

#' @param max_iter maximum number of iterations to perform.

#' @param epoch The number of iterations in between update messages.

#' @param quiet if `TRUE`, suppresses intermediate output

#' @param ... other parameters to be passed to `tsne::tsne`

#' @importFrom tsne tsne

#' @importFrom stats as.dist cor

#' @return The DISCBIO-class object input with the tsne slot filled.

#' @examples

#' sc <- DISCBIO(valuesG1msTest) # changes signature of data

#' sc <- Clustexp(sc, cln = 2) # data must be clustered before plottin

#' sc <- comptSNE(sc, max_iter = 30)

#' head(sc@tsne)

#'

setGeneric(

  name = "comptSNE",

  def = function(

      object, rseed = NULL, max_iter = 5000, epoch = 500, quiet = FALSE, ...) {

    standardGeneric("comptSNE")

  }

)

#' @rdname comptSNE

#' @export

setMethod(

  f = "comptSNE",

  signature = "DISCBIO",

  definition = function(object, rseed, max_iter, epoch, quiet, ...) {

    # ======================================================================

    # Validating

    # ======================================================================

    ran_k <- length(object@kmeans$kpart) > 0

    ran_m <- length(object@MBclusters) > 0

    if (ran_k) {

      di <- dist.gen(as.matrix(object@distances))

    } else if (ran_m) {

      di <- dist.gen(as.matrix(t(object@fdata)))

    } else {

      stop("run clustexp before comptSNE")

    }

    # ======================================================================

    # Computing

    # ======================================================================

    set.seed(rseed)

    if (quiet) {

      ts <- suppressMessages(

        tsne(di, max_iter = max_iter, epoch = epoch, ...)

      )

    } else {

      message("This function may take time")

      ts <- tsne(di, max_iter = max_iter, epoch = epoch, ...)

    }

    # ======================================================================

    # Filling output

    # ======================================================================

    if (ran_k) {

      object@tsne <- as.data.frame(ts)

    } else if (ran_m) {

      object@MBtsne <- as.data.frame(ts)

    }

    return(object)

  }

)