X-Git-Url: https://git.auder.net/?a=blobdiff_plain;f=epclust%2FR%2Fclustering.R;h=fce1b1c695f4b6094580375e28a6cd5cd0f1e805;hb=56857861dc15088cf58e7438968fe5714b22168e;hp=e27ea353e479fb8185afb124f02fbcab56c9d4e0;hpb=5c6529795907ba1b34d4552cbfd0e0cbb77cac0f;p=epclust.git

diff --git a/epclust/R/clustering.R b/epclust/R/clustering.R
index e27ea35..fce1b1c 100644
--- a/epclust/R/clustering.R
+++ b/epclust/R/clustering.R
@@ -1,71 +1,73 @@
-oneIteration = function(..........)
+# 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, .............., 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
-}
-
-processChunk = function(indices, K1, K2)
-{
-	#1) retrieve data (coeffs)
-	coeffs = getCoeffs(indices)
-	#2) cluster
-	cl = computeClusters(as.matrix(coeffs[,2:ncol(coeffs)]), K1)
-	#3) WER (optional)
-	if (K2 > 0)
+	cl = parallel::makeCluster(ncores)
+	repeat
 	{
-		curves = computeSynchrones(cl)
-		dists = computeWerDists(curves)
-		cl = computeClusters(dists, K2)
+		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)), length(indices) )
+			indices[(nb_series_per_chunk*(i-1)+1):upper_bound]
+		})
+		indices = unlist( parallel::parLapply(cl, indices_workers, function(inds)
+			computeClusters1(getCoefs(inds), K1)) )
+		if (length(indices) == K1)
+			break
 	}
-	cl
+	parallel::stopCluster(cl)
+	indices #medoids
 }
 
-computeClusters = function(data, K)
+# Apply the clustering algorithm (PAM) on a coeffs or distances matrix
+computeClusters1 = function(coefs, K1)
+	indices[ cluster::pam(coefs, K1, diss=FALSE)$id.med ]
+
+# Cluster a chunk of series inside one task (~max nb_series_per_chunk)
+computeClusters2 = function(medoids, K2, getRefSeries, nb_series_per_chunk)
 {
-	library(cluster)
-	pam_output = cluster::pam(data, K)
-	return ( list( clusts=pam_output$clustering, medoids=pam_output$medoids,
-		ranks=pam_output$id.med ) )
+	synchrones = computeSynchrones(medoids, getRefSeries, nb_series_per_chunk)
+	cluster::pam(computeWerDists(synchrones), K2, diss=TRUE)$medoids
 }
 
-#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(medoids, getRefSeries, nb_series_per_chunk)
 {
-
+	#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, '/')
 }
 
-#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 rows)
+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 +81,7 @@ 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 +96,7 @@ 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)