# 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))
# (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