X-Git-Url: https://git.auder.net/?a=blobdiff_plain;f=epclust%2FR%2Fclustering.R;h=5b5f6684763321b1b854d0cdbc2de6bb2b8ded16;hb=074a48c472fcbdf99a36fae333dd8dbb568c06a0;hp=1774b19e686c657ea4a81768920d0ffc238bc8a7;hpb=3fb6e823601002c44ffbf913e83c8d24cfa1e819;p=epclust.git

diff --git a/epclust/R/clustering.R b/epclust/R/clustering.R
index 1774b19..5b5f668 100644
--- a/epclust/R/clustering.R
+++ b/epclust/R/clustering.R
@@ -23,8 +23,15 @@ NULL
 #' @rdname clustering
 #' @export
 clusteringTask1 <- function(indices, getContribs, K1, algoClust1, nb_items_clust,
-	ncores_clust=3, verbose=FALSE, parll=TRUE)
+	ncores_clust=3, verbose=FALSE)
 {
+	if (verbose)
+		cat(paste("*** Clustering task 1 on ",length(indices)," series [start]\n", sep=""))
+
+	if (length(indices) <= K1)
+		return (indices)
+
+	parll <- (ncores_clust > 1)
 	if (parll)
 	{
 		# outfile=="" to see stderr/stdout on terminal
@@ -40,8 +47,6 @@ clusteringTask1 <- function(indices, getContribs, K1, algoClust1, nb_items_clust
 	{
 		# Balance tasks by splitting the indices set - as evenly as possible
 		indices_workers <- .splitIndices(indices, nb_items_clust, min_size=K1+1)
-		if (verbose)
-			cat(paste("*** [iterated] Clustering task 1 on ",length(indices)," series\n", sep=""))
 		indices <-
 			if (parll)
 			{
@@ -56,6 +61,10 @@ clusteringTask1 <- function(indices, getContribs, K1, algoClust1, nb_items_clust
 					inds[ algoClust1(getContribs(inds), K1) ]
 				) )
 			}
+		if (verbose)
+		{
+			cat(paste("*** Clustering task 1 on ",length(indices)," medoids [iter]\n", sep=""))
+		}
 	}
 	if (parll)
 		parallel::stopCluster(cl)
@@ -66,7 +75,7 @@ clusteringTask1 <- function(indices, getContribs, K1, algoClust1, nb_items_clust
 #' @rdname clustering
 #' @export
 clusteringTask2 <- function(indices, getSeries, K2, algoClust2, nb_series_per_chunk,
-	smooth_lvl, nvoice, nbytes, endian, ncores_clust=3, verbose=FALSE, parll=TRUE)
+	smooth_lvl, nvoice, nbytes, endian, ncores_clust=3, verbose=FALSE)
 {
 	if (verbose)
 		cat(paste("*** Clustering task 2 on ",length(indices)," medoids\n", sep=""))
@@ -76,7 +85,7 @@ clusteringTask2 <- function(indices, getSeries, K2, algoClust2, nb_series_per_ch
 
 	# A) Compute the WER distances (Wavelets Extended coefficient of deteRmination)
 	distances <- computeWerDists(indices, getSeries, nb_series_per_chunk,
-		smooth_lvl, nvoice, nbytes, endian, ncores_clust, verbose, parll)
+		smooth_lvl, nvoice, nbytes, endian, ncores_clust, verbose)
 
 	# B) Apply clustering algorithm 2 on the WER distances matrix
 	if (verbose)