X-Git-Url: https://git.auder.net/?p=epclust.git;a=blobdiff_plain;f=epclust%2FR%2Fclustering.R;h=14915abf861bace1b6d4bd4f9f68283c004bfff9;hp=a4c273a7ce8dbc91ba3480fac4a83d61d0b70ae6;hb=eef6f6c97277ea3ce760981e5244cbde7fc904a0;hpb=a174b8ea1f322992068ab42810df017a2b9620ee

diff --git a/epclust/R/clustering.R b/epclust/R/clustering.R
index a4c273a..14915ab 100644
--- a/epclust/R/clustering.R
+++ b/epclust/R/clustering.R
@@ -1,6 +1,6 @@
 #' @name clustering
 #' @rdname clustering
-#' @aliases clusteringTask1 computeClusters1 computeClusters2
+#' @aliases clusteringTask1 clusteringTask2 computeClusters1 computeClusters2
 #'
 #' @title Two-stage clustering, withing one task (see \code{claws()})
 #'
@@ -31,7 +31,7 @@ NULL
 #' @rdname clustering
 #' @export
 clusteringTask1 = function(
-	indices, getContribs, K1, nb_series_per_chunk, ncores_clust=1, verbose=FALSE, parll=TRUE)
+	indices, getContribs, K1, nb_items_per_chunk, ncores_clust=1, verbose=FALSE, parll=TRUE)
 {
 	if (verbose)
 		cat(paste("*** Clustering task 1 on ",length(indices)," lines\n", sep=""))
@@ -87,7 +87,7 @@ computeClusters1 = function(contribs, K1, verbose=FALSE)
 {
 	if (verbose)
 		cat(paste("   computeClusters1() on ",nrow(contribs)," lines\n", sep=""))
-	cluster::pam(contribs, K1, diss=FALSE)$id.med
+	cluster::pam(        t(contribs)       , K1, diss=FALSE)$id.med
 }
 
 #' @rdname clustering
@@ -96,7 +96,7 @@ computeClusters2 = function(distances, K2, verbose=FALSE)
 {
 	if (verbose)
 		cat(paste("   computeClusters2() on ",nrow(distances)," lines\n", sep=""))
-	cluster::pam(distances, K2, diss=TRUE)$id.med
+	cluster::pam(       distances        , K2, diss=TRUE)$id.med
 }
 
 #' computeSynchrones
@@ -110,7 +110,7 @@ computeClusters2 = function(distances, K2, verbose=FALSE)
 #' @param nb_ref_curves How many reference series? (This number is known at this stage)
 #' @inheritParams claws
 #'
-#' @return A big.matrix of size K1 x L where L = data_length
+#' @return A big.matrix of size L x K1 where L = length of a serie
 #'
 #' @export
 computeSynchrones = function(medoids, getRefSeries,
@@ -142,8 +142,8 @@ computeSynchrones = function(medoids, getRefSeries,
 		{
 			if (parll)
 				synchronicity::lock(m)
-			synchrones[ mi[i], ] = synchrones[ mi[i], ] + ref_series[i,]
-			counts[ mi[i] ] = counts[ mi[i] ] + 1 #TODO: remove counts?
+			synchrones[, mi[i] ] = synchrones[, mi[i] ] + ref_series[,i]
+			counts[ mi[i] ] = counts[ mi[i] ] + 1 #TODO: remove counts? ...or as arg?!
 			if (parll)
 				synchronicity::unlock(m)
 		}
@@ -152,7 +152,7 @@ computeSynchrones = function(medoids, getRefSeries,
 	K = nrow(medoids) ; L = ncol(medoids)
 	# Use bigmemory (shared==TRUE by default) + synchronicity to fill synchrones in //
 	# TODO: if size > RAM (not our case), use file-backed big.matrix
-	synchrones = bigmemory::big.matrix(nrow=K, ncol=L, type="double", init=0.)
+	synchrones = bigmemory::big.matrix(nrow=L, ncol=K, type="double", init=0.)
 	counts = bigmemory::big.matrix(nrow=K, ncol=1, type="double", init=0)
 	# synchronicity is only for Linux & MacOS; on Windows: run sequentially
 	parll = (requireNamespace("synchronicity",quietly=TRUE)
@@ -181,14 +181,14 @@ computeSynchrones = function(medoids, getRefSeries,
 
 	#TODO: can we avoid this loop? ( synchrones = sweep(synchrones, 1, counts, '/') )
 	for (i in seq_len(K))
-		synchrones[i,] = synchrones[i,] / counts[i,1]
+		synchrones[,i] = synchrones[,i] / counts[i]
 	#NOTE: odds for some clusters to be empty? (when series already come from stage 2)
 	#      ...maybe; but let's hope resulting K1' be still quite bigger than K2
-	noNA_rows = sapply(seq_len(K), function(i) all(!is.nan(synchrones[i,])))
+	noNA_rows = sapply(seq_len(K), function(i) all(!is.nan(synchrones[,i])))
 	if (all(noNA_rows))
 		return (synchrones)
 	# Else: some clusters are empty, need to slice synchrones
-	synchrones[noNA_rows,]
+	bigmemory::as.big.matrix(synchrones[,noNA_rows])
 }
 
 #' computeWerDists
@@ -272,7 +272,7 @@ computeWerDists = function(synchrones, nbytes,endian,ncores_clust=1,verbose=FALS
 	{
 		#from cwt_file ...
 		res <- getDataInFile(c(2*index-1,2*index), cwt_file, nbytes, endian)
-	###############TODO:	
+	###############TODO:
 	}
 
 	# Distance between rows i and j