X-Git-Url: https://git.auder.net/?p=epclust.git;a=blobdiff_plain;f=epclust%2FR%2Fclustering.R;h=b91d512b23b6b1cd82eb4f8689e96ea44448b420;hp=bea073a660e3c4f201546caa87d539522d4671a9;hb=3c5a4b0880db63367a474a568e1322b3999932fe;hpb=40f12a2f66d06fd77183ea02b996f5c66f90761c

diff --git a/epclust/R/clustering.R b/epclust/R/clustering.R
index bea073a..b91d512 100644
--- a/epclust/R/clustering.R
+++ b/epclust/R/clustering.R
@@ -1,30 +1,28 @@
-#' @name clustering
-#' @rdname clustering
-#' @aliases clusteringTask1 clusteringTask2 computeClusters1 computeClusters2
+#' Two-stage clustering, within one task (see \code{claws()})
 #'
-#' @title Two-stage clustering, withing 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
+#' 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.
 #'
-#' @description \code{clusteringTask1()} runs one full stage-1 task, which consists in
-#'   iterated stage 1 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 indices Range of series indices to cluster
 #' @param getContribs Function to retrieve contributions from initial series indices:
 #'   \code{getContribs(indices)} outputs a contributions matrix
 #' @inheritParams claws
 #' @inheritParams computeSynchrones
+#' @inheritParams computeWerDists
+#'
+#' @return The indices of the computed (resp. K1 and K2) medoids.
 #'
-#' @return For \code{clusteringTask1()}, the indices of the computed (K1) medoids.
-#'   Indices are irrelevant for stage 2 clustering, thus \code{clusteringTask2()}
-#'   outputs a big.matrix of medoids (of size LxK2, K2 = final number of clusters)
+#' @name clustering
+#' @rdname clustering
+#' @aliases clusteringTask1 clusteringTask2
 NULL
 
 #' @rdname clustering
 #' @export
-clusteringTask1 = function(indices, getContribs, K1, algoClust1, nb_series_per_chunk,
+clusteringTask1 = function(indices, getContribs, K1, algoClust1, nb_items_clust,
 	ncores_clust=1, verbose=FALSE, parll=TRUE)
 {
 	if (parll)
@@ -36,7 +34,7 @@ clusteringTask1 = function(indices, getContribs, K1, algoClust1, nb_series_per_c
 	while (length(indices) > K1)
 	{
 		# Balance tasks by splitting the indices set - as evenly as possible
-		indices_workers = .splitIndices(indices, nb_items_clust1)
+		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 <-
@@ -66,22 +64,17 @@ clusteringTask2 = function(indices, getSeries, K2, algoClust2, nb_series_per_chu
 	nvoice, nbytes, endian, ncores_clust=1, verbose=FALSE, parll=TRUE)
 {
 	if (verbose)
-		cat(paste("*** Clustering task 2 on ",ncol(medoids)," synchrones\n", sep=""))
-
-	if (ncol(medoids) <= K2)
-		return (medoids)
+		cat(paste("*** Clustering task 2 on ",length(indices)," medoids\n", sep=""))
 
-	# A) Obtain synchrones, that is to say the cumulated power consumptions
-	#    for each of the K1 initial groups
-	synchrones = computeSynchrones(medoids, getRefSeries, nb_ref_curves,
-		nb_series_per_chunk, ncores_clust, verbose, parll)
+	if (length(indices) <= K2)
+		return (indices)
 
-	# B) Compute the WER distances (Wavelets Extended coefficient of deteRmination)
-	distances = computeWerDists(
-		synchrones, nvoice, nbytes, endian, ncores_clust, verbose, parll)
+	# 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)
 
-	# C) Apply clustering algorithm 2 on the WER distances matrix
+	# B) Apply clustering algorithm 2 on the WER distances matrix
 	if (verbose)
 		cat(paste("*** algoClust2() on ",nrow(distances)," items\n", sep=""))
-	medoids[ ,algoClust2(distances,K2) ]
+	indices[ algoClust2(distances,K2) ]
 }