X-Git-Url: https://git.auder.net/?a=blobdiff_plain;f=epclust%2FR%2Fclustering.R;h=5b5f6684763321b1b854d0cdbc2de6bb2b8ded16;hb=074a48c472fcbdf99a36fae333dd8dbb568c06a0;hp=3e7fd3866922d1c56b48741642ca88fd55d5082c;hpb=dc86eb0c992e6e4ab119d48398d040c4cf3a75fd;p=epclust.git

diff --git a/epclust/R/clustering.R b/epclust/R/clustering.R
index 3e7fd38..5b5f668 100644
--- a/epclust/R/clustering.R
+++ b/epclust/R/clustering.R
@@ -23,14 +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\n", sep=""))
+		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
@@ -62,8 +63,7 @@ clusteringTask1 <- function(indices, getContribs, K1, algoClust1, nb_items_clust
 			}
 		if (verbose)
 		{
-			cat(paste("*** [iterated] Clustering task 1: now ",
-				length(indices)," medoids\n", sep=""))
+			cat(paste("*** Clustering task 1 on ",length(indices)," medoids [iter]\n", sep=""))
 		}
 	}
 	if (parll)
@@ -75,7 +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=""))
@@ -85,7 +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)