X-Git-Url: https://git.auder.net/?p=epclust.git;a=blobdiff_plain;f=epclust%2FR%2Fclustering.R;h=87a5f914e137cb3f509443b58a1e59b80505b011;hp=c8bad664cb65b14e37cb4546418518089fe86210;hb=3eef8d3df59ded9a281cff51f79fe824198a7427;hpb=e205f2187f0ccdff00bffc47642392ec5e33214d

diff --git a/epclust/R/clustering.R b/epclust/R/clustering.R
index c8bad66..87a5f91 100644
--- a/epclust/R/clustering.R
+++ b/epclust/R/clustering.R
@@ -1,6 +1,6 @@
 # Cluster one full task (nb_curves / ntasks series)
 clusteringTask = function(indices,getSeries,getSeriesForSynchrones,synchrones_file,
-	getCoefs,K1,K2,nb_series_per_chunk,ncores,to_file)
+	getCoefs,K1,K2,nb_series_per_chunk,ncores,to_file,ftype)
 {
 	cl = parallel::makeCluster(ncores)
 	repeat
@@ -19,7 +19,8 @@ clusteringTask = function(indices,getSeries,getSeriesForSynchrones,synchrones_fi
 	parallel::stopCluster(cl)
 	if (K2 == 0)
 		return (indices)
-	computeClusters2(indices, K2, getSeries, getSeriesForSynchrones, to_file)
+	computeClusters2(indices, K2, getSeries, getSeriesForSynchrones, to_file,
+									 nb_series_per_chunk,ftype)
 	vector("integer",0)
 }
 
@@ -31,27 +32,42 @@ computeClusters1 = function(indices, getCoefs, K1)
 }
 
 # Cluster a chunk of series inside one task (~max nb_series_per_chunk)
-computeClusters2 = function(indices, K2, getSeries, getSeriesForSynchrones, to_file)
+computeClusters2 = function(indices, K2, getSeries, getSeriesForSynchrones, to_file,
+														nb_series_per_chunk, ftype)
 {
-	curves = computeSynchrones(indices, getSeries, getSeriesForSynchrones)
+	curves = computeSynchrones(indices, getSeries, getSeriesForSynchrones, nb_series_per_chunk)
 	dists = computeWerDists(curves)
 	medoids = cluster::pam(dists, K2, diss=TRUE)$medoids
 	if (to_file)
 	{
-		serialize(medoids, synchrones_file)
+		serialize(medoids, synchrones_file, ftype, nb_series_per_chunk)
 		return (NULL)
 	}
 	medoids
 }
 
 # Compute the synchrones curves (sum of clusters elements) from a clustering result
-computeSynchrones = function(indices, getSeries, getSeriesForSynchrones)
+computeSynchrones = function(indices, getSeries, getSeriesForSynchrones, 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 ?
-	series = getSeries(indices)
-	#...........
-	#sapply(seq_along(inds), colMeans(getSeries(inds[[i]]$indices,inds[[i]]$ids)))
+	medoids = getSeries(indices)
+	K = nrow(medoids)
+	synchrones = matrix(0, nrow=K, ncol=ncol(medoids))
+	counts = rep(0,K)
+	index = 1
+	repeat
+	{
+		series = getSeriesForSynchrones((index-1)+seq_len(nb_series_per_chunk))
+		if (is.null(series))
+			break
+		#get medoids indices for this chunk of series
+		index = which.min( rowSums( sweep(medoids, 2, series[i,], '-')^2 ) )
+		synchrones[index,] = synchrones[index,] + series[i,]
+		counts[index] = counts[index] + 1
+	}
+	#NOTE: odds for some clusters to be empty? (when series already come from stage 2)
+	synchrones = sweep(synchrones, 1, counts, '/')
 }
 
 # Compute the WER distance between the synchrones curves (in rows)