#' and then WER distances computations, before applying the clustering algorithm.
#' \code{computeClusters1()} and \code{computeClusters2()} correspond to the atomic
#' clustering procedures respectively for stage 1 and 2. The former applies the
-#' clustering algorithm (PAM) on a contributions matrix, while the latter clusters
-#' a chunk of series inside one task (~max nb_series_per_chunk)
+#' first clustering algorithm on a contributions matrix, while the latter clusters
+#' a set of series inside one task (~nb_items_clust)
#'
#' @param indices Range of series indices to cluster in parallel (initial data)
#' @param getContribs Function to retrieve contributions from initial series indices:
#' @rdname clustering
#' @export
clusteringTask1 = function(
- indices, getContribs, K1, nb_items_per_chunk, ncores_clust=1, verbose=FALSE, parll=TRUE)
+ indices, getContribs, K1, nb_per_chunk, nb_items_clust, ncores_clust=1,
+ verbose=FALSE, parll=TRUE)
{
if (verbose)
cat(paste("*** Clustering task 1 on ",length(indices)," lines\n", sep=""))
+
+
+
+
+
+##TODO: reviser le spreadIndices, tenant compte de nb_items_clust
+
+ ##TODO: reviser / harmoniser avec getContribs qui en récupère pt'et + pt'et - !!
+
+
+
if (parll)
{
cl = parallel::makeCluster(ncores_clust)