X-Git-Url: https://git.auder.net/?a=blobdiff_plain;f=epclust%2FR%2Fclustering.R;h=6090517c6b6464d4c253ba52b8efdf29cb56c823;hb=0e2dce80a3fddaca50c96c6c27a8b32468095d6c;hp=077becf0add865d3bec19d0584d7590257cf867d;hpb=7b13d0c28da62d91684a29ced50c740120e2b7a9;p=epclust.git

diff --git a/epclust/R/clustering.R b/epclust/R/clustering.R
index 077becf..6090517 100644
--- a/epclust/R/clustering.R
+++ b/epclust/R/clustering.R
@@ -1,56 +1,57 @@
 # Cluster one full task (nb_curves / ntasks series)
-clusteringTask = function(K1, K2, WER, nb_series_per_chunk, indices_tasks, ncores_clust)
+clusteringTask = function(indices, ncores)
 {
-	cl_clust = parallel::makeCluster(ncores_clust)
-	#parallel::clusterExport(cl=cl_clust, varlist=c("fonctions_du_package"), envir=environment())
-	indices_clust = indices_task[[i]]
+	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 = 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"))
-		if ((WER=="end" && length(indices_clust)==K1) || (WER=="mix" && length(indices_clust)==K2))
+			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)
-	unlist(indices_clust)
+	if (WER == "end")
+		return (indices_clust)
+	#WER=="mix"
+	computeClusters2(indices_clust, K2, getSeries, to_file=TRUE)
 }
 
+# Apply the clustering algorithm (PAM) on a coeffs or distances matrix
+computeClusters1 = function(indices, getCoefs, K1)
+	indices[ cluster::pam(getCoefs(indices), K1, diss=FALSE)$id.med ]
+
 # Cluster a chunk of series inside one task (~max nb_series_per_chunk)
-clusterChunk = function(indices, K1, K2)
+computeClusters2 = function(indices, K2, getSeries, to_file)
 {
-	coeffs = getCoeffs(indices)
-	cl = computeClusters(as.matrix(coeffs[,2:ncol(coeffs)]), K1, diss=FALSE)
-	if (K2 > 0)
+	if (is.null(indices))
+	{
+		#get series from file
+	}
+#Puis K-means après WER...
+	if (WER=="mix" > 0)
 	{
-		curves = computeSynchrones(cl)
+		curves = computeSynchrones(indices)
 		dists = computeWerDists(curves)
-		cl = computeClusters(dists, K2, diss=TRUE)
+		indices = computeClusters(dists, K2, diss=TRUE)
 	}
-	indices[cl]
-}
-
-# Apply the clustering algorithm (PAM) on a coeffs or distances matrix
-computeClusters = function(md, K, diss)
-{
-	if (!require(cluster, quietly=TRUE))
-		stop("Unable to load cluster library")
-	cluster::pam(md, K, diss=diss)$id.med
+	if (to_file)
+		#write results to file (JUST series ; no possible ID here)
 }
 
 # Compute the synchrones curves (sum of clusters elements) from a clustering result
-computeSynchrones = function(indices)
-{
-	colSums( getData(indices) )
-}
+computeSynchrones = function(inds)
+	sapply(seq_along(inds), colMeans(getSeries(inds[[i]]$indices,inds[[i]]$ids)))
 
-# Compute the WER distance between the synchrones curves
+# Compute the WER distance between the synchrones curves (in columns)
 computeWerDist = function(curves)
 {
 	if (!require("Rwave", quietly=TRUE))
@@ -73,7 +74,7 @@ computeWerDist = function(curves)
 
 	# (normalized) observations node with CWT
 	Xcwt4 <- lapply(seq_len(n), function(i) {
-		ts <- scale(ts(curves[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
@@ -88,7 +89,7 @@ computeWerDist = function(curves)
 	{
 		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)