#' Two-stage clustering, within one task (see \code{claws()})
#'
#' \code{clusteringTask1()} runs one full stage-1 task, which consists in iterated
-#' stage 1 clustering on nb_curves / ntasks energy contributions, computed through
+#' clustering on nb_curves / ntasks energy contributions, computed through
#' discrete wavelets coefficients.
#' \code{clusteringTask2()} runs a full stage-2 task, which consists in WER distances
#' computations between medoids (indices) output from stage 1, before applying
#' the second clustering algorithm on the distances matrix.
#'
#' @param getContribs Function to retrieve contributions from initial series indices:
-#' \code{getContribs(indices)} outputs a contributions matrix
+#' \code{getContribs(indices)} outputs a contributions matrix, in columns
#' @inheritParams claws
#' @inheritParams computeSynchrones
#' @inheritParams computeWerDists
#' @rdname clustering
#' @export
clusteringTask1 <- function(indices, getContribs, K1, algoClust1, nb_items_clust,
- ncores_clust=1, verbose=FALSE, parll=TRUE)
+ ncores_clust=3, verbose=FALSE, parll=TRUE)
{
if (parll)
{
# outfile=="" to see stderr/stdout on terminal
- cl <- parallel::makeCluster(ncores_clust, outfile = "")
+ cl <-
+ if (verbose)
+ parallel::makeCluster(ncores_clust, outfile = "")
+ else
+ parallel::makeCluster(ncores_clust)
parallel::clusterExport(cl, c("getContribs","K1","verbose"), envir=environment())
}
# Iterate clustering algorithm 1 until K1 medoids are found
#' @rdname clustering
#' @export
clusteringTask2 <- function(indices, getSeries, K2, algoClust2, nb_series_per_chunk,
- smooth_lvl, nvoice, nbytes, endian, ncores_clust=1, verbose=FALSE, parll=TRUE)
+ smooth_lvl, nvoice, nbytes, endian, ncores_clust=3, verbose=FALSE, parll=TRUE)
{
if (verbose)
cat(paste("*** Clustering task 2 on ",length(indices)," medoids\n", sep=""))