X-Git-Url: https://git.auder.net/?p=epclust.git;a=blobdiff_plain;f=epclust%2FR%2Fclustering.R;h=a8f1d3e705a42bbe5fdee78ff2820e118f68d7ab;hp=b91d512b23b6b1cd82eb4f8689e96ea44448b420;hb=282342bafdc9ff65c5df98c6e2304d63b33b9fb2;hpb=3c5a4b0880db63367a474a568e1322b3999932fe

diff --git a/epclust/R/clustering.R b/epclust/R/clustering.R
index b91d512..a8f1d3e 100644
--- a/epclust/R/clustering.R
+++ b/epclust/R/clustering.R
@@ -22,19 +22,20 @@ NULL
 
 #' @rdname clustering
 #' @export
-clusteringTask1 = function(indices, getContribs, K1, algoClust1, nb_items_clust,
+clusteringTask1 <- function(indices, getContribs, K1, algoClust1, nb_items_clust,
 	ncores_clust=1, verbose=FALSE, parll=TRUE)
 {
 	if (parll)
 	{
-		cl = parallel::makeCluster(ncores_clust, outfile = "")
+		# outfile=="" to see stderr/stdout on terminal
+		cl <- parallel::makeCluster(ncores_clust, outfile = "")
 		parallel::clusterExport(cl, c("getContribs","K1","verbose"), envir=environment())
 	}
 	# Iterate clustering algorithm 1 until K1 medoids are found
 	while (length(indices) > K1)
 	{
 		# Balance tasks by splitting the indices set - as evenly as possible
-		indices_workers = .splitIndices(indices, nb_items_clust, min_size=K1+1)
+		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 <-
@@ -60,8 +61,8 @@ clusteringTask1 = function(indices, getContribs, K1, algoClust1, nb_items_clust,
 
 #' @rdname clustering
 #' @export
-clusteringTask2 = function(indices, getSeries, K2, algoClust2, nb_series_per_chunk,
-	nvoice, nbytes, endian, ncores_clust=1, verbose=FALSE, parll=TRUE)
+clusteringTask2 <- function(indices, getSeries, K2, algoClust2, nb_series_per_chunk,
+	smooth_lvl, nvoice, nbytes, endian, ncores_clust=1, verbose=FALSE, parll=TRUE)
 {
 	if (verbose)
 		cat(paste("*** Clustering task 2 on ",length(indices)," medoids\n", sep=""))
@@ -70,8 +71,8 @@ clusteringTask2 = function(indices, getSeries, K2, algoClust2, nb_series_per_chu
 		return (indices)
 
 	# A) Compute the WER distances (Wavelets Extended coefficient of deteRmination)
-	distances = computeWerDists(indices, getSeries, nb_series_per_chunk,
-		nvoice, nbytes, endian, ncores_clust, verbose, parll)
+	distances <- computeWerDists(indices, getSeries, nb_series_per_chunk,
+		smooth_lvl, nvoice, nbytes, endian, ncores_clust, verbose, parll)
 
 	# B) Apply clustering algorithm 2 on the WER distances matrix
 	if (verbose)