From: Benjamin Auder <benjamin.auder@somewhere>
Date: Sat, 4 Mar 2017 23:03:37 +0000 (+0100)
Subject: before computeSynchrones
X-Git-Url: https://git.auder.net/%7B%7B%20asset%28%27mixstore/images/css/pieces/doc/mini-custom.min.css?a=commitdiff_plain;h=e205f2187f0ccdff00bffc47642392ec5e33214d;p=epclust.git

before computeSynchrones
---

diff --git a/epclust/R/clustering.R b/epclust/R/clustering.R
index 6090517..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(indices, ncores)
+clusteringTask = function(indices,getSeries,getSeriesForSynchrones,synchrones_file,
+	getCoefs,K1,K2,nb_series_per_chunk,ncores,to_file)
 {
 	cl = parallel::makeCluster(ncores)
-	parallel::clusterExport(cl,
-		varlist=c("K1","getCoefs"),
-		envir=environment())
 	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 = unlist( parallel::parLapply(cl, indices_workers, function(indices)
-			computeClusters1(indices, getCoefs, K1)) )
+		indices = unlist( parallel::parLapply(cl, indices_workers, function(inds)
+			computeClusters1(inds, getCoefs, K1)) )
 		if (length(indices_clust) == K1)
 			break
 	}
-	parallel::stopCluster(cl_clust)
-	if (WER == "end")
-		return (indices_clust)
-	#WER=="mix"
-	computeClusters2(indices_clust, K2, getSeries, to_file=TRUE)
+	parallel::stopCluster(cl)
+	if (K2 == 0)
+		return (indices)
+	computeClusters2(indices, K2, getSeries, getSeriesForSynchrones, to_file)
+	vector("integer",0)
 }
 
 # Apply the clustering algorithm (PAM) on a coeffs or distances matrix
 computeClusters1 = function(indices, getCoefs, K1)
-	indices[ cluster::pam(getCoefs(indices), K1, diss=FALSE)$id.med ]
+{
+	coefs = getCoefs(indices)
+	indices[ cluster::pam(coefs, K1, diss=FALSE)$id.med ]
+}
 
 # Cluster a chunk of series inside one task (~max nb_series_per_chunk)
-computeClusters2 = function(indices, K2, getSeries, to_file)
+computeClusters2 = function(indices, K2, getSeries, getSeriesForSynchrones, to_file)
 {
-	if (is.null(indices))
-	{
-		#get series from file
-	}
-#Puis K-means après WER...
-	if (WER=="mix" > 0)
+	curves = computeSynchrones(indices, getSeries, getSeriesForSynchrones)
+	dists = computeWerDists(curves)
+	medoids = cluster::pam(dists, K2, diss=TRUE)$medoids
+	if (to_file)
 	{
-		curves = computeSynchrones(indices)
-		dists = computeWerDists(curves)
-		indices = computeClusters(dists, K2, diss=TRUE)
+		serialize(medoids, synchrones_file)
+		return (NULL)
 	}
-	if (to_file)
-		#write results to file (JUST series ; no possible ID here)
+	medoids
 }
 
 # Compute the synchrones curves (sum of clusters elements) from a clustering result
-computeSynchrones = function(inds)
-	sapply(seq_along(inds), colMeans(getSeries(inds[[i]]$indices,inds[[i]]$ids)))
+computeSynchrones = function(indices, getSeries, getSeriesForSynchrones)
+{
+	#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 (in columns)
+# Compute the WER distance between the synchrones curves (in rows)
 computeWerDist = function(curves)
 {
 	if (!require("Rwave", quietly=TRUE))
@@ -74,7 +77,7 @@ 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
diff --git a/epclust/R/main.R b/epclust/R/main.R
index ac4ea8d..0b59832 100644
--- a/epclust/R/main.R
+++ b/epclust/R/main.R
@@ -34,6 +34,7 @@
 #'     "LIMIT ", n, " ORDER BY date", sep=""))
 #'   return (df)
 #' }
+#' #####TODO: if DB, array rank --> ID at first retrieval, when computing coeffs; so:: NO use of IDs !
 #'   #TODO: 3 examples, data.frame / binary file / DB sqLite
 #'   + sampleCurves : wavBootstrap de package wmtsa
 #' cl = epclust(getData, K1=200, K2=15, ntasks=1000, nb_series_per_chunk=5000, WER="mix")
@@ -98,7 +99,6 @@ epclust = function(series,K1,K2,ntasks=1,nb_series_per_chunk=50*K1,min_series_pe
 		nb_curves = nb_curves + nrow(coeffs_chunk)
 	}
 	getCoefs = function(indices) getDataInFile(indices, coefs_file)
-######TODO: if DB, array rank --> ID at first retrieval, when computing coeffs; so:: NO use of IDs !
 
 	if (nb_curves < min_series_per_chunk)
 		stop("Not enough data: less rows than min_series_per_chunk!")
@@ -112,17 +112,36 @@ epclust = function(series,K1,K2,ntasks=1,nb_series_per_chunk=50*K1,min_series_pe
 		upper_bound = ifelse( i<ntasks, min(nb_series_per_task*i,nb_curves), nb_curves )
 		indices[((i-1)*nb_series_per_task+1):upper_bound]
 	})
-	cl_tasks = parallel::makeCluster(ncores_tasks)
-	parallel::clusterExport(cl_tasks,
-		varlist=c("getSeries","getCoefs","K1","K2","WER","nb_series_per_chunk","ncores_clust"),
-		envir=environment())
+	cl = parallel::makeCluster(ncores_tasks)
 	#1000*K1 (or K2) indices (or NOTHING--> series on file)
-	indices = parallel::parLapply(cl_tasks, indices_tasks, clusteringTask)
-	parallel::stopCluster(cl_tasks)
+	indices = unlist( parallel::parLapply(cl, indices_tasks, function(inds) {
+		clusteringTask(inds, getSeries, getSeries, getCoefs, K1, K2*(WER=="mix"),
+			nb_series_per_chunk,ncores_clust,to_file=TRUE)
+	}) )
+	parallel::stopCluster(cl)
 
-	#Now series must be retrieved from synchrones_file, and have no ID
-	getSeries = function(indices, ids) getDataInFile(indices, synchrones_file)
+	getSeriesForSynchrones = getSeries
+	synchrones_file = paste(bin_dir,"synchrones",sep="")
+	if (WER=="mix")
+	{
+		indices = seq_len(ntasks*K2)
+		#Now series must be retrieved from synchrones_file
+		getSeries = function(inds) getDataInFile(inds, synchrones_file)
+		#Coefs must be re-computed
+		unlink(coefs_file)
+		index = 1
+		repeat
+		{
+			series = getSeries((index-1)+seq_len(nb_series_per_chunk))
+			if (is.null(series))
+				break
+			coeffs_chunk = curvesToCoeffs(series, wf)
+			serialize(coeffs_chunk, coefs_file)
+			index = index + nb_series_per_chunk
+		}
+	}
 
 	# Run step2 on resulting indices or series (from file)
-	computeClusters2(indices=if (WER=="end") indices else NULL, K2, to_file=FALSE)
+	clusteringTask(indices, getSeries, getSeriesForSynchrones, getCoefs, K1, K2,
+		nb_series_per_chunk, ncores_tasks*ncores_clust, to_file=FALSE)
 }