X-Git-Url: https://git.auder.net/?a=blobdiff_plain;f=epclust%2FR%2Fclustering.R;h=fce1b1c695f4b6094580375e28a6cd5cd0f1e805;hb=56857861dc15088cf58e7438968fe5714b22168e;hp=578b2f399035023eb68b0b004be10f18d87cac84;hpb=48108c3999d28d973443fa5e78f73a0a9f2bfc07;p=epclust.git

diff --git a/epclust/R/clustering.R b/epclust/R/clustering.R
index 578b2f3..fce1b1c 100644
--- a/epclust/R/clustering.R
+++ b/epclust/R/clustering.R
@@ -1,56 +1,64 @@
-# Cluster one full task (nb_curves / ntasks series)
-clusteringTask = function(indices_clust)
+# Cluster one full task (nb_curves / ntasks series); only step 1
+clusteringTask = function(indices, getCoefs, K1, nb_series_per_chunk, ncores)
 {
-	cl_clust = parallel::makeCluster(ncores_clust)
-	parallel::clusterExport(cl_clust,
-		varlist=c("K1","K2","WER"),
-		envir=environment())
+	cl = parallel::makeCluster(ncores)
 	repeat
 	{
-		nb_workers = max( 1, round( length(indices_clust) / nb_series_per_chunk ) )
+		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_clust[(nb_series_per_chunk*(i-1)+1):upper_bound]
+				min(nb_series_per_chunk*i,length(indices)), length(indices) )
+			indices[(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 = unlist( parallel::parLapply(cl, indices_workers, function(inds)
+			computeClusters1(getCoefs(inds), K1)) )
+		if (length(indices) == K1)
 			break
 	}
-	parallel::stopCluster(cl_clust)
-	unlist(indices_clust)
-}
-
-# Cluster a chunk of series inside one task (~max nb_series_per_chunk)
-clusterChunk = function(indices_chunk)
-{
-	coeffs = readCoeffs(indices_chunk)
-	cl = computeClusters(as.matrix(coeffs[,2:ncol(coeffs)]), K1, diss=FALSE)
-	if (WER=="mix" > 0)
-	{
-		curves = computeSynchrones(cl)
-		dists = computeWerDists(curves)
-		cl = computeClusters(dists, K2, diss=TRUE)
-	}
-	indices_chunk[cl]
+	parallel::stopCluster(cl)
+	indices #medoids
 }
 
 # Apply the clustering algorithm (PAM) on a coeffs or distances matrix
-computeClusters = function(md, K, diss)
+computeClusters1 = function(coefs, K1)
+	indices[ cluster::pam(coefs, K1, diss=FALSE)$id.med ]
+
+# Cluster a chunk of series inside one task (~max nb_series_per_chunk)
+computeClusters2 = function(medoids, K2, getRefSeries, nb_series_per_chunk)
 {
-	if (!require(cluster, quietly=TRUE))
-		stop("Unable to load cluster library")
-	cluster::pam(md, K, diss=diss)$id.med
+	synchrones = computeSynchrones(medoids, getRefSeries, nb_series_per_chunk)
+	cluster::pam(computeWerDists(synchrones), K2, diss=TRUE)$medoids
 }
 
 # Compute the synchrones curves (sum of clusters elements) from a clustering result
-computeSynchrones = function(indices)
+computeSynchrones = function(medoids, getRefSeries, nb_series_per_chunk)
 {
-	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 ?
+	K = nrow(medoids)
+	synchrones = matrix(0, nrow=K, ncol=ncol(medoids))
+	counts = rep(0,K)
+	index = 1
+	repeat
+	{
+		range = (index-1) + seq_len(nb_series_per_chunk)
+		ref_series = getRefSeries(range)
+		if (is.null(ref_series))
+			break
+		#get medoids indices for this chunk of series
+		for (i in seq_len(nrow(ref_series)))
+		{
+			j = which.min( rowSums( sweep(medoids, 2, series[i,], '-')^2 ) )
+			synchrones[j,] = synchrones[j,] + series[i,]
+			counts[j] = counts[j] + 1
+		}
+		index = index + nb_series_per_chunk
+	}
+	#NOTE: odds for some clusters to be empty? (when series already come from stage 2)
+	sweep(synchrones, 1, counts, '/')
 }
 
-# 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))
@@ -88,7 +96,7 @@ 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)