X-Git-Url: https://git.auder.net/?p=epclust.git;a=blobdiff_plain;f=epclust%2FR%2Fclustering.R;h=c22678632dcee57df836d4e75978299bb9eaa027;hp=7e06c437570706ed941da90ab313b8dc86ecfd48;hb=6ad3f3fd0ec4f3cd1fd5de4a287c1893293e5bcc;hpb=2c14dbea13c897c6964f49f9cd17622f4c9733c0

diff --git a/epclust/R/clustering.R b/epclust/R/clustering.R
index 7e06c43..c226786 100644
--- a/epclust/R/clustering.R
+++ b/epclust/R/clustering.R
@@ -121,35 +121,29 @@ computeSynchrones = function(medoids, getRefSeries,
 
 	computeSynchronesChunk = function(indices)
 	{
-		ref_series = getRefSeries(indices)
-		nb_series = nrow(ref_series)
-
 		if (parll)
 		{
 			require("bigmemory", quietly=TRUE)
-			require("synchronicity", quietly=TRUE)
+			requireNamespace("synchronicity", quietly=TRUE)
 			require("epclust", quietly=TRUE)
 			synchrones <- bigmemory::attach.big.matrix(synchrones_desc)
+			counts <- bigmemory::attach.big.matrix(counts_desc)
 			medoids <- bigmemory::attach.big.matrix(medoids_desc)
 			m <- synchronicity::attach.mutex(m_desc)
 		}
 
+		ref_series = getRefSeries(indices)
+		nb_series = nrow(ref_series)
+
 		#get medoids indices for this chunk of series
 		mi = computeMedoidsIndices(medoids@address, ref_series)
-#		#R-equivalent, requiring a matrix (thus potentially breaking "fit-in-memory" hope)
-#		mat_meds = medoids[,]
-#		mi = rep(NA,nb_series)
-#		for (i in 1:nb_series)
-#			mi[i] <- which.min( rowSums( sweep(mat_meds, 2, ref_series[i,], '-')^2 ) )
-#		rm(mat_meds); gc()
 
 		for (i in seq_len(nb_series))
 		{
 			if (parll)
 				synchronicity::lock(m)
-			synchrones[mi[i],] = synchrones[mi[i],] + ref_series[i,]
-#TODO: remove counts
-			counts[mi[i],1] = counts[mi[i],1] + 1
+			synchrones[ mi[i], ] = synchrones[ mi[i], ] + ref_series[i,]
+			counts[ mi[i] ] = counts[ mi[i] ] + 1 #TODO: remove counts?
 			if (parll)
 				synchronicity::unlock(m)
 		}
@@ -168,12 +162,11 @@ computeSynchrones = function(medoids, getRefSeries,
 		m <- synchronicity::boost.mutex()
 		m_desc <- synchronicity::describe(m)
 		synchrones_desc = bigmemory::describe(synchrones)
+		counts_desc = bigmemory::describe(counts)
 		medoids_desc = bigmemory::describe(medoids)
-
 		cl = parallel::makeCluster(ncores_clust)
-		parallel::clusterExport(cl,
-			varlist=c("synchrones_desc","counts","verbose","m_desc","medoids_desc","getRefSeries"),
-			envir=environment())
+		parallel::clusterExport(cl, varlist=c("synchrones_desc","counts_desc","counts",
+			"verbose","m_desc","medoids_desc","getRefSeries"), envir=environment())
 	}
 
 	indices_workers = .spreadIndices(seq_len(nb_ref_curves), nb_series_per_chunk)
@@ -215,6 +208,15 @@ computeWerDists = function(synchrones, ncores_clust=1,verbose=FALSE,parll=TRUE)
 	if (verbose)
 		cat(paste("--- Compute WER dists\n", sep=""))
 
+
+
+
+#TODO: serializer les CWT, les récupérer via getDataInFile 
+#--> OK, faut juste stocker comme séries simples de taille delta*ncol (53*17519)
+
+
+
+
 	n <- nrow(synchrones)
 	delta <- ncol(synchrones)
 	#TODO: automatic tune of all these parameters ? (for other users)
@@ -232,16 +234,25 @@ computeWerDists = function(synchrones, ncores_clust=1,verbose=FALSE,parll=TRUE)
 	totnoct = noctave + as.integer(s0log/nvoice) + 1
 
 	Xwer_dist <- bigmemory::big.matrix(nrow=n, ncol=n, type="double")
-	fcoefs = rep(1/3, 3) #moving average on 3 values
 
 	# Generate n(n-1)/2 pairs for WER distances computations
+#	pairs = list()
+#	V = seq_len(n)
+#	for (i in 1:n)
+#	{
+#		V = V[-1]
+#		pairs = c(pairs, lapply(V, function(v) c(i,v)))
+#	}
+	# Generate "smart" pairs for WER distances computations
 	pairs = list()
-	V = seq_len(n)
-	for (i in 1:n)
+	F = floor(2*n/3)
+	for (i in 1:F)
+		pairs = c(pairs, lapply((i+1):n, function(v) c(i,v)))
+	V = (F+1):n
+	for (i in (F+1):(n-1))
 	{
 		V = V[-1]
-		pairs = c(pairs, lapply(V, function(v) c(i,v)))
-	}
+		pairs = c(pairs, 
 
 	# Distance between rows i and j
 	computeDistancesIJ = function(pair)
@@ -264,21 +275,21 @@ computeWerDists = function(synchrones, ncores_clust=1,verbose=FALSE,parll=TRUE)
 			sqres <- sweep(ts.cwt,2,sqs,'*')
 			sqres / max(Mod(sqres))
 		}
-#browser()
+
 		i = pair[1] ; j = pair[2]
 		if (verbose && j==i+1)
 			cat(paste("   Distances (",i,",",j,"), (",i,",",j+1,") ...\n", sep=""))
-print(system.time( {		cwt_i <- computeCWT(i)
-		cwt_j <- computeCWT(j) } ))
+		cwt_i <- computeCWT(i)
+		cwt_j <- computeCWT(j)
 
-print(system.time( {
+#print(system.time( {
 		num <- epclustFilter(Mod(cwt_i * Conj(cwt_j)))
 		WX  <- epclustFilter(Mod(cwt_i * Conj(cwt_i)))
 		WY  <- epclustFilter(Mod(cwt_j * Conj(cwt_j)))
 		wer2 <- sum(colSums(num)^2) / sum(colSums(WX) * colSums(WY))
 		Xwer_dist[i,j] <- sqrt(delta * ncol(cwt_i) * max(1 - wer2, 0.)) #FIXME: wer2 should be < 1
 		Xwer_dist[j,i] <- Xwer_dist[i,j]
-} ) )
+#} ) )
 		Xwer_dist[i,i] = 0.
 	}
 
@@ -291,7 +302,7 @@ print(system.time( {
 		parallel::clusterExport(cl, varlist=c("synchrones_desc","Xwer_dist_desc","totnoct",
 			"nvoice","w0","s0log","noctave","s0","verbose"), envir=environment())
 	}
-browser()
+
 	ignored <-
 		if (parll)
 			parallel::parLapply(cl, pairs, computeDistancesIJ)
@@ -300,7 +311,7 @@ browser()
 
 	if (parll)
 		parallel::stopCluster(cl)
-#browser()
+
 	Xwer_dist[n,n] = 0.
 	distances <- Xwer_dist[,]
 	rm(Xwer_dist) ; gc()