X-Git-Url: https://git.auder.net/doc/html/img/rock_paper_scissors_lizard_spock.gif?a=blobdiff_plain;f=epclust%2FR%2Fclustering.R;h=c8bad664cb65b14e37cb4546418518089fe86210;hb=e205f2187f0ccdff00bffc47642392ec5e33214d;hp=6090517c6b6464d4c253ba52b8efdf29cb56c823;hpb=0e2dce80a3fddaca50c96c6c27a8b32468095d6c;p=epclust.git
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 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