computeClusters
computeSynchrones

# 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,ftype)
{
	cl = parallel::makeCluster(ncores)
	repeat
	{
		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)), 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)
	if (K2 == 0)
		return (indices)
	computeClusters2(indices, K2, getSeries, getSeriesForSynchrones, to_file,
									 nb_series_per_chunk,ftype)
	vector("integer",0)
}

# Apply the clustering algorithm (PAM) on a coeffs or distances matrix
computeClusters1 = function(indices, getCoefs, K1)
{
	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, getSeriesForSynchrones, to_file,