X-Git-Url: https://git.auder.net/?p=epclust.git;a=blobdiff_plain;f=epclust%2FR%2Fclustering.R;h=6090517c6b6464d4c253ba52b8efdf29cb56c823;hp=578b2f399035023eb68b0b004be10f18d87cac84;hb=0e2dce80a3fddaca50c96c6c27a8b32468095d6c;hpb=48108c3999d28d973443fa5e78f73a0a9f2bfc07

diff --git a/epclust/R/clustering.R b/epclust/R/clustering.R
index 578b2f3..6090517 100644
--- a/epclust/R/clustering.R
+++ b/epclust/R/clustering.R
@@ -1,9 +1,9 @@
 # Cluster one full task (nb_curves / ntasks series)
-clusteringTask = function(indices_clust)
+clusteringTask = function(indices, ncores)
 {
-	cl_clust = parallel::makeCluster(ncores_clust)
-	parallel::clusterExport(cl_clust,
-		varlist=c("K1","K2","WER"),
+	cl = parallel::makeCluster(ncores)
+	parallel::clusterExport(cl,
+		varlist=c("K1","getCoefs"),
 		envir=environment())
 	repeat
 	{
@@ -13,44 +13,45 @@ clusteringTask = function(indices_clust)
 				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 = 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))
+		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)$id.med ]
+
 # Cluster a chunk of series inside one task (~max nb_series_per_chunk)
-clusterChunk = function(indices_chunk)
+computeClusters2 = function(indices, K2, getSeries, to_file)
 {
-	coeffs = readCoeffs(indices_chunk)
-	cl = computeClusters(as.matrix(coeffs[,2:ncol(coeffs)]), K1, diss=FALSE)
+	if (is.null(indices))
+	{
+		#get series from file
+	}
+#Puis K-means après WER...
 	if (WER=="mix" > 0)
 	{
-		curves = computeSynchrones(cl)
+		curves = computeSynchrones(indices)
 		dists = computeWerDists(curves)
-		cl = computeClusters(dists, K2, diss=TRUE)
+		indices = computeClusters(dists, K2, diss=TRUE)
 	}
-	indices_chunk[cl]
-}
-
-# Apply the clustering algorithm (PAM) on a coeffs or distances matrix
-computeClusters = function(md, K, diss)
-{
-	if (!require(cluster, quietly=TRUE))
-		stop("Unable to load cluster library")
-	cluster::pam(md, K, diss=diss)$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)