# Cluster one full task (nb_curves / ntasks series)
-clusteringTask = function(K1, K2, WER, nb_series_per_chunk, indices_tasks, ncores_clust)
+clusteringTask = function(indices, ncores)
{
- 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)
+ parallel::clusterExport(cl,
+ varlist=c("K1","getCoefs"),
+ envir=environment())
repeat
{
nb_workers = max( 1, round( length(indices_clust) / nb_series_per_chunk ) )
- indices_workers = list()
- for (i in 1:nb_workers)
- {
+ 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))
+ indices_clust[(nb_series_per_chunk*(i-1)+1):upper_bound]
+ })
+ indices_clust = unlist( parallel::parLapply(cl, indices_workers, function(indices)
+ computeClusters1(indices, getCoefs, K1)) )
+ if (length(indices_clust) == K1)
break
}
parallel::stopCluster(cl_clust)
- unlist(indices_clust)
+ if (WER == "end")
+ return (indices_clust)
+ #WER=="mix"
+ computeClusters2(indices_clust, K2, getSeries, to_file=TRUE)
}
+# Apply the clustering algorithm (PAM) on a coeffs or distances matrix
+computeClusters1 = function(indices, getCoefs, K1)
+ indices[ cluster::pam(getCoefs(indices), K1, diss=FALSE)$id.med ]
+
# Cluster a chunk of series inside one task (~max nb_series_per_chunk)
-clusterChunk = function(indices, K1, K2)
+computeClusters2 = function(indices, K2, getSeries, to_file)
{
- coeffs = getCoeffs(indices)
- cl = computeClusters(as.matrix(coeffs[,2:ncol(coeffs)]), K1, diss=FALSE)
- if (K2 > 0)
+ if (is.null(indices))
+ {
+ #get series from file
+ }
+#Puis K-means après WER...
+ if (WER=="mix" > 0)
{
- curves = computeSynchrones(cl)
+ curves = computeSynchrones(indices)
dists = computeWerDists(curves)
- cl = computeClusters(dists, K2, diss=TRUE)
+ indices = computeClusters(dists, K2, diss=TRUE)
}
- indices[cl]
-}
-
-# Apply the clustering algorithm (PAM) on a coeffs or distances matrix
-computeClusters = function(md, K, diss)
-{
- if (!require(cluster, quietly=TRUE))
- stop("Unable to load cluster library")
- cluster::pam(md, K, diss=diss)$id.med
+ if (to_file)
+ #write results to file (JUST series ; no possible ID here)
}
# Compute the synchrones curves (sum of clusters elements) from a clustering result
-computeSynchrones = function(indices)
-{
- colSums( getData(indices) )
-}
+computeSynchrones = function(inds)
+ 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 columns)
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
{
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)