# Cluster one full task (nb_curves / ntasks series)
-clusteringTask = function(K1, K2, WER, nb_series_per_chunk, indices_tasks, ncores_clust)
+clusteringTask = function(indices,getSeries,getSeriesForSynchrones,synchrones_file,
+ getCoefs,K1,K2,nb_series_per_chunk,ncores,to_file)
{
- cl_clust = parallel::makeCluster(ncores_clust)
- #parallel::clusterExport(cl=cl_clust, varlist=c("fonctions_du_package"), envir=environment())
- indices_clust = indices_task[[i]]
+ cl = parallel::makeCluster(ncores)
repeat
{
- nb_workers = max( 1, round( length(indices_clust) / nb_series_per_chunk ) )
- indices_workers = list()
- for (i in 1:nb_workers)
- {
+ 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_workers[[i]] = indices_clust[(nb_series_per_chunk*(i-1)+1):upper_bound]
- }
- indices_clust = parallel::parLapply(cl, indices_workers, clusterChunk, K1, K2*(WER=="mix"))
- if ((WER=="end" && length(indices_clust)==K1) || (WER=="mix" && length(indices_clust)==K2))
+ min(nb_series_per_chunk*i,length(indices)), length(indices) )
+ indices[(nb_series_per_chunk*(i-1)+1):upper_bound]
+ })
+ indices = unlist( parallel::parLapply(cl, indices_workers, function(inds)
+ computeClusters1(inds, getCoefs, K1)) )
+ if (length(indices_clust) == K1)
break
}
- parallel::stopCluster(cl_clust)
- unlist(indices_clust)
+ parallel::stopCluster(cl)
+ if (K2 == 0)
+ return (indices)
+ computeClusters2(indices, K2, getSeries, getSeriesForSynchrones, to_file)
+ vector("integer",0)
}
-# Cluster a chunk of series inside one task (~max nb_series_per_chunk)
-clusterChunk = function(indices, K1, K2)
+# Apply the clustering algorithm (PAM) on a coeffs or distances matrix
+computeClusters1 = function(indices, getCoefs, K1)
{
- coeffs = getCoeffs(indices)
- cl = computeClusters(as.matrix(coeffs[,2:ncol(coeffs)]), K1, diss=FALSE)
- if (K2 > 0)
- {
- curves = computeSynchrones(cl)
- dists = computeWerDists(curves)
- cl = computeClusters(dists, K2, diss=TRUE)
- }
- indices[cl]
+ coefs = getCoefs(indices)
+ indices[ cluster::pam(coefs, K1, diss=FALSE)$id.med ]
}
-# Apply the clustering algorithm (PAM) on a coeffs or distances matrix
-computeClusters = function(md, K, diss)
+# Cluster a chunk of series inside one task (~max nb_series_per_chunk)
+computeClusters2 = function(indices, K2, getSeries, getSeriesForSynchrones, to_file)
{
- if (!require(cluster, quietly=TRUE))
- stop("Unable to load cluster library")
- cluster::pam(md, K, diss=diss)$id.med
+ curves = computeSynchrones(indices, getSeries, getSeriesForSynchrones)
+ dists = computeWerDists(curves)
+ medoids = cluster::pam(dists, K2, diss=TRUE)$medoids
+ if (to_file)
+ {
+ serialize(medoids, synchrones_file)
+ return (NULL)
+ }
+ medoids
}
# Compute the synchrones curves (sum of clusters elements) from a clustering result
-computeSynchrones = function(indices)
+computeSynchrones = function(indices, getSeries, getSeriesForSynchrones)
{
- colSums( getData(indices) )
+ #les getSeries(indices) sont les medoides --> init vect nul pour chacun, puis incr avec les
+ #courbes (getSeriesForSynchrones) les plus proches... --> au sens de la norme L2 ?
+ series = getSeries(indices)
+ #...........
+ #sapply(seq_along(inds), colMeans(getSeries(inds[[i]]$indices,inds[[i]]$ids)))
}
-# Compute the WER distance between the synchrones curves
+# Compute the WER distance between the synchrones curves (in rows)
computeWerDist = function(curves)
{
if (!require("Rwave", quietly=TRUE))
{
for (j in (i+1):n)
{
- #TODO: later, compute CWT here (because not enough storage space for 32M series)
+ #TODO: later, compute CWT here (because not enough storage space for 200k series)
# 'circular=TRUE' is wrong, should just take values on the sides; to rewrite in C
num <- filter(Mod(Xcwt4[[i]] * Conj(Xcwt4[[j]])), fcoefs, circular=TRUE)
WX <- filter(Mod(Xcwt4[[i]] * Conj(Xcwt4[[i]])), fcoefs, circular=TRUE)