X-Git-Url: https://git.auder.net/?a=blobdiff_plain;f=epclust%2FR%2Fclustering.R;h=36b476987452e84e2978e2093957d2f9d25c25e0;hb=37c82bbafbffc19e8b47a521952bac58f189e9ea;hp=14915abf861bace1b6d4bd4f9f68283c004bfff9;hpb=eef6f6c97277ea3ce760981e5244cbde7fc904a0;p=epclust.git

diff --git a/epclust/R/clustering.R b/epclust/R/clustering.R
index 14915ab..36b4769 100644
--- a/epclust/R/clustering.R
+++ b/epclust/R/clustering.R
@@ -11,8 +11,8 @@
 #'   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:
@@ -30,20 +30,20 @@ NULL
 
 #' @rdname clustering
 #' @export
-clusteringTask1 = function(
-	indices, getContribs, K1, nb_items_per_chunk, ncores_clust=1, verbose=FALSE, parll=TRUE)
+clusteringTask1 = function(indices, getContribs, K1, nb_items_clust1,
+	ncores_clust=1, verbose=FALSE, parll=TRUE)
 {
 	if (verbose)
 		cat(paste("*** Clustering task 1 on ",length(indices)," lines\n", sep=""))
 
 	if (parll)
 	{
-		cl = parallel::makeCluster(ncores_clust)
+		cl = parallel::makeCluster(ncores_clust, outfile = "")
 		parallel::clusterExport(cl, varlist=c("getContribs","K1","verbose"), envir=environment())
 	}
 	while (length(indices) > K1)
 	{
-		indices_workers = .spreadIndices(indices, nb_series_per_chunk)
+		indices_workers = .spreadIndices(indices, nb_items_clust1, K1+1)
 		indices <-
 			if (parll)
 			{
@@ -317,20 +317,31 @@ computeWerDists = function(synchrones, nbytes,endian,ncores_clust=1,verbose=FALS
 }
 
 # Helper function to divide indices into balanced sets
-.spreadIndices = function(indices, nb_per_chunk)
+.spreadIndices = function(indices, max_per_set, min_nb_per_set = 1)
 {
 	L = length(indices)
-	nb_workers = floor( L / nb_per_chunk )
-	if (nb_workers == 0)
+	min_nb_workers = floor( L / max_per_set )
+	rem = L %% max_per_set
+	if (nb_workers == 0 || (nb_workers==1 && rem==0))
 	{
-		# L < nb_series_per_chunk, simple case
+		# L <= max_nb_per_set, simple case
 		indices_workers = list(indices)
 	}
 	else
 	{
 		indices_workers = lapply( seq_len(nb_workers), function(i)
-			indices[(nb_per_chunk*(i-1)+1):(nb_per_chunk*i)] )
-		# Spread the remaining load among the workers
+			indices[(max_nb_per_set*(i-1)+1):(max_per_set*i)] )
+		# Two cases: remainder is >= min_per_set (easy)...
+		if (rem >= min_nb_per_set)
+			indices_workers = c( indices_workers, list(tail(indices,rem)) )
+		#...or < min_per_set: harder, need to remove indices from current sets to feed
+		# the too-small remainder. It may fail: then fallback to "slightly bigger sets"
+		else
+		{
+			save_indices_workers = indices_workers
+			small_set = tail(indices,rem)
+			# Try feeding small_set until it reaches min_per_set, whle keeping the others big enough
+			# Spread the remaining load among the workers
 		rem = L %% nb_per_chunk
 		while (rem > 0)
 		{