X-Git-Url: https://git.auder.net/?a=blobdiff_plain;f=epclust%2FR%2Fclustering.R;h=6090517c6b6464d4c253ba52b8efdf29cb56c823;hb=0e2dce80a3fddaca50c96c6c27a8b32468095d6c;hp=e27ea353e479fb8185afb124f02fbcab56c9d4e0;hpb=5c6529795907ba1b34d4552cbfd0e0cbb77cac0f;p=epclust.git

diff --git a/epclust/R/clustering.R b/epclust/R/clustering.R
index e27ea35..6090517 100644
--- a/epclust/R/clustering.R
+++ b/epclust/R/clustering.R
@@ -1,71 +1,66 @@
-oneIteration = function(..........)
+# Cluster one full task (nb_curves / ntasks series)
+clusteringTask = function(indices, ncores)
 {
-		cl_clust = parallel::makeCluster(ncores_clust)
-		parallel::clusterExport(cl_clust, .............., envir=........)
-		indices_clust = indices_task[[i]]
-		repeat
-		{
-			nb_workers = max( 1, round( length(indices_clust) / nb_series_per_chunk ) )
-			indices_workers = list()
-			#indices[[i]] == (start_index,number_of_elements)
-			for (i in 1:nb_workers)
-			{
-				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::parSapply(cl, indices_workers, processChunk, K1, K2*(WER=="mix"))
-			if ( (WER=="end" && length(indices_clust) == K1) ||
-				(WER=="mix" && length(indices_clust) == K2) )
-			{
-				break
-			}
-		}
-		parallel::stopCluster(cl_clust)
-		res_clust
+	cl = parallel::makeCluster(ncores)
+	parallel::clusterExport(cl,
+		varlist=c("K1","getCoefs"),
+		envir=environment())
+	repeat
+	{
+		nb_workers = max( 1, round( length(indices_clust) / nb_series_per_chunk ) )
+		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_clust[(nb_series_per_chunk*(i-1)+1):upper_bound]
+		})
+		indices_clust = unlist( parallel::parLapply(cl, indices_workers, function(indices)
+			computeClusters1(indices, getCoefs, K1)) )
+		if (length(indices_clust) == K1)
+			break
+	}
+	parallel::stopCluster(cl_clust)
+	if (WER == "end")
+		return (indices_clust)
+	#WER=="mix"
+	computeClusters2(indices_clust, K2, getSeries, to_file=TRUE)
 }
 
-processChunk = function(indices, K1, K2)
+# Apply the clustering algorithm (PAM) on a coeffs or distances matrix
+computeClusters1 = function(indices, getCoefs, K1)
+	indices[ cluster::pam(getCoefs(indices), K1, diss=FALSE)$id.med ]
+
+# Cluster a chunk of series inside one task (~max nb_series_per_chunk)
+computeClusters2 = function(indices, K2, getSeries, to_file)
 {
-	#1) retrieve data (coeffs)
-	coeffs = getCoeffs(indices)
-	#2) cluster
-	cl = computeClusters(as.matrix(coeffs[,2:ncol(coeffs)]), K1)
-	#3) WER (optional)
-	if (K2 > 0)
+	if (is.null(indices))
+	{
+		#get series from file
+	}
+#Puis K-means après WER...
+	if (WER=="mix" > 0)
 	{
-		curves = computeSynchrones(cl)
+		curves = computeSynchrones(indices)
 		dists = computeWerDists(curves)
-		cl = computeClusters(dists, K2)
+		indices = computeClusters(dists, K2, diss=TRUE)
 	}
-	cl
+	if (to_file)
+		#write results to file (JUST series ; no possible ID here)
 }
 
-computeClusters = function(data, K)
-{
-	library(cluster)
-	pam_output = cluster::pam(data, K)
-	return ( list( clusts=pam_output$clustering, medoids=pam_output$medoids,
-		ranks=pam_output$id.med ) )
-}
-
-#TODO: appendCoeffs() en C --> serialize et append to file
-
-computeSynchrones = function(...)
-{
-
-}
+# Compute the synchrones curves (sum of clusters elements) from a clustering result
+computeSynchrones = function(inds)
+	sapply(seq_along(inds), colMeans(getSeries(inds[[i]]$indices,inds[[i]]$ids)))
 
-#Entrée : courbes synchrones, soit après étape 1 itérée, soit après chaqure étape 1
-computeWerDist = function(conso)
+# Compute the WER distance between the synchrones curves (in columns)
+computeWerDist = function(curves)
 {
 	if (!require("Rwave", quietly=TRUE))
 		stop("Unable to load Rwave library")
-	n <- nrow(conso)
-	delta <- ncol(conso)
+	n <- nrow(curves)
+	delta <- ncol(curves)
 	#TODO: automatic tune of all these parameters ? (for other users)
 	nvoice   <- 4
-	# noctave = 2^13 = 8192 half hours ~ 180 days ; ~log2(ncol(conso))
+	# noctave = 2^13 = 8192 half hours ~ 180 days ; ~log2(ncol(curves))
 	noctave = 13
 	# 4 here represent 2^5 = 32 half-hours ~ 1 day
 	#NOTE: default scalevector == 2^(0:(noctave * nvoice) / nvoice) * s0 (?)
@@ -79,7 +74,7 @@ computeWerDist = function(conso)
 
 	# (normalized) observations node with CWT
 	Xcwt4 <- lapply(seq_len(n), function(i) {
-		ts <- scale(ts(conso[i,]), center=TRUE, scale=scaled)
+		ts <- scale(ts(curves[,i]), center=TRUE, scale=scaled)
 		totts.cwt = Rwave::cwt(ts,totnoct,nvoice,w0,plot=0)
 		ts.cwt = totts.cwt[,s0log:(s0log+noctave*nvoice)]
 		#Normalization
@@ -94,7 +89,7 @@ computeWerDist = function(conso)
 	{
 		for (j in (i+1):n)
 		{
-			#TODO: later, compute CWT here (because not enough storage space for 32M series)
+			#TODO: later, compute CWT here (because not enough storage space for 200k series)
 			#      'circular=TRUE' is wrong, should just take values on the sides; to rewrite in C
 			num <- filter(Mod(Xcwt4[[i]] * Conj(Xcwt4[[j]])), fcoefs, circular=TRUE)
 			WX <- filter(Mod(Xcwt4[[i]] * Conj(Xcwt4[[i]])), fcoefs, circular=TRUE)