X-Git-Url: https://git.auder.net/?p=epclust.git;a=blobdiff_plain;f=epclust%2FR%2Fclustering.R;h=578b2f399035023eb68b0b004be10f18d87cac84;hp=42e894c805eab138e80b038eaa836d78cfa74103;hb=48108c3999d28d973443fa5e78f73a0a9f2bfc07;hpb=23844f603a363475e85ed1cb4b8620f139bcffdf

diff --git a/epclust/R/clustering.R b/epclust/R/clustering.R
index 42e894c..578b2f3 100644
--- a/epclust/R/clustering.R
+++ b/epclust/R/clustering.R
@@ -1,20 +1,19 @@
 # Cluster one full task (nb_curves / ntasks series)
-clusteringTask = function(K1, K2, WER, nb_series_per_chunk, indices_tasks, ncores_clust)
+clusteringTask = function(indices_clust)
 {
 	cl_clust = parallel::makeCluster(ncores_clust)
-	#parallel::clusterExport(cl=cl_clust, varlist=c("fonctions_du_package"), envir=environment())
-	indices_clust = indices_task[[i]]
+	parallel::clusterExport(cl_clust,
+		varlist=c("K1","K2","WER"),
+		envir=environment())
 	repeat
 	{
 		nb_workers = max( 1, round( length(indices_clust) / nb_series_per_chunk ) )
-		indices_workers = list()
-		for (i in 1:nb_workers)
-		{
+		indices_workers = lapply(seq_len(nb_workers), function(i) {
 			upper_bound = ifelse( i<nb_workers,
 				min(nb_series_per_chunk*i,length(indices_clust)), length(indices_clust) )
-			indices_workers[[i]] = indices_clust[(nb_series_per_chunk*(i-1)+1):upper_bound]
-		}
-		indices_clust = parallel::parLapply(cl, indices_workers, clusterChunk, K1, K2*(WER=="mix"))
+			indices_clust[(nb_series_per_chunk*(i-1)+1):upper_bound]
+		})
+		indices_clust = parallel::parLapply(cl, indices_workers, clusterChunk)
 		# TODO: soft condition between K2 and K1, before applying final WER step
 		if ((WER=="end" && length(indices_clust)==K1) || (WER=="mix" && length(indices_clust)==K2))
 			break
@@ -24,17 +23,17 @@ clusteringTask = function(K1, K2, WER, nb_series_per_chunk, indices_tasks, ncore
 }
 
 # Cluster a chunk of series inside one task (~max nb_series_per_chunk)
-clusterChunk = function(indices, K1, K2)
+clusterChunk = function(indices_chunk)
 {
-	coeffs = getCoeffs(indices)
+	coeffs = readCoeffs(indices_chunk)
 	cl = computeClusters(as.matrix(coeffs[,2:ncol(coeffs)]), K1, diss=FALSE)
-	if (K2 > 0)
+	if (WER=="mix" > 0)
 	{
 		curves = computeSynchrones(cl)
 		dists = computeWerDists(curves)
 		cl = computeClusters(dists, K2, diss=TRUE)
 	}
-	indices[cl]
+	indices_chunk[cl]
 }
 
 # Apply the clustering algorithm (PAM) on a coeffs or distances matrix