X-Git-Url: https://git.auder.net/?a=blobdiff_plain;f=epclust%2FR%2Fclustering.R;h=886bfbcca2fbd1b52239c2403e3c521d0c2c7f18;hb=e0154a59e55143dac0fbd2a4739a3509bc958e76;hp=a8f1d3e705a42bbe5fdee78ff2820e118f68d7ab;hpb=282342bafdc9ff65c5df98c6e2304d63b33b9fb2;p=epclust.git

diff --git a/epclust/R/clustering.R b/epclust/R/clustering.R
index a8f1d3e..886bfbc 100644
--- a/epclust/R/clustering.R
+++ b/epclust/R/clustering.R
@@ -1,14 +1,14 @@
 #' 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
@@ -23,12 +23,22 @@ NULL
 #' @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 (verbose)
+		cat(paste("*** Clustering task 1 on ",length(indices)," series [start]\n", sep=""))
+
+	if (length(indices) <= K1)
+		return (indices)
+
 	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
@@ -36,8 +46,6 @@ 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)
 			{
@@ -52,6 +60,10 @@ 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)
@@ -62,7 +74,7 @@ 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=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=""))