X-Git-Url: https://git.auder.net/?p=epclust.git;a=blobdiff_plain;f=epclust%2FR%2Fclustering.R;h=c8bad664cb65b14e37cb4546418518089fe86210;hp=42e894c805eab138e80b038eaa836d78cfa74103;hb=e205f2187f0ccdff00bffc47642392ec5e33214d;hpb=74f571a72fd63ae92466d944a9ab4a111d177121

diff --git a/epclust/R/clustering.R b/epclust/R/clustering.R
index 42e894c..c8bad66 100644
--- a/epclust/R/clustering.R
+++ b/epclust/R/clustering.R
@@ -1,57 +1,60 @@
 # Cluster one full task (nb_curves / ntasks series)
-clusteringTask = function(K1, K2, WER, nb_series_per_chunk, indices_tasks, ncores_clust)
+clusteringTask = function(indices,getSeries,getSeriesForSynchrones,synchrones_file,
+	getCoefs,K1,K2,nb_series_per_chunk,ncores,to_file)
 {
-	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)
 	repeat
 	{
-		nb_workers = max( 1, round( length(indices_clust) / nb_series_per_chunk ) )
-		indices_workers = list()
-		for (i in 1:nb_workers)
-		{
+		nb_workers = max( 1, round( length(indices) / 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_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"))
-		# 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))
+				min(nb_series_per_chunk*i,length(indices)), length(indices) )
+			indices[(nb_series_per_chunk*(i-1)+1):upper_bound]
+		})
+		indices = unlist( parallel::parLapply(cl, indices_workers, function(inds)
+			computeClusters1(inds, getCoefs, K1)) )
+		if (length(indices_clust) == K1)
 			break
 	}
-	parallel::stopCluster(cl_clust)
-	unlist(indices_clust)
+	parallel::stopCluster(cl)
+	if (K2 == 0)
+		return (indices)
+	computeClusters2(indices, K2, getSeries, getSeriesForSynchrones, to_file)
+	vector("integer",0)
 }
 
-# Cluster a chunk of series inside one task (~max nb_series_per_chunk)
-clusterChunk = function(indices, K1, K2)
+# Apply the clustering algorithm (PAM) on a coeffs or distances matrix
+computeClusters1 = function(indices, getCoefs, K1)
 {
-	coeffs = getCoeffs(indices)
-	cl = computeClusters(as.matrix(coeffs[,2:ncol(coeffs)]), K1, diss=FALSE)
-	if (K2 > 0)
-	{
-		curves = computeSynchrones(cl)
-		dists = computeWerDists(curves)
-		cl = computeClusters(dists, K2, diss=TRUE)
-	}
-	indices[cl]
+	coefs = getCoefs(indices)
+	indices[ cluster::pam(coefs, K1, diss=FALSE)$id.med ]
 }
 
-# Apply the clustering algorithm (PAM) on a coeffs or distances matrix
-computeClusters = function(md, K, diss)
+# Cluster a chunk of series inside one task (~max nb_series_per_chunk)
+computeClusters2 = function(indices, K2, getSeries, getSeriesForSynchrones, to_file)
 {
-	if (!require(cluster, quietly=TRUE))
-		stop("Unable to load cluster library")
-	cluster::pam(md, K, diss=diss)$id.med
+	curves = computeSynchrones(indices, getSeries, getSeriesForSynchrones)
+	dists = computeWerDists(curves)
+	medoids = cluster::pam(dists, K2, diss=TRUE)$medoids
+	if (to_file)
+	{
+		serialize(medoids, synchrones_file)
+		return (NULL)
+	}
+	medoids
 }
 
 # Compute the synchrones curves (sum of clusters elements) from a clustering result
-computeSynchrones = function(indices)
+computeSynchrones = function(indices, getSeries, getSeriesForSynchrones)
 {
-	colSums( getData(indices) )
+	#les getSeries(indices) sont les medoides --> init vect nul pour chacun, puis incr avec les
+	#courbes (getSeriesForSynchrones) les plus proches... --> au sens de la norme L2 ?
+	series = getSeries(indices)
+	#...........
+	#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 rows)
 computeWerDist = function(curves)
 {
 	if (!require("Rwave", quietly=TRUE))
@@ -89,7 +92,7 @@ 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)