'update'

[epclust.git] / epclust / R / computeSynchrones.R

#' computeSynchrones
#'
#' Compute the synchrones curves (sum of clusters elements) from a matrix of medoids,
#' using euclidian distance.
#'
#' @param medoids matrix of medoids in columns (curves of same length as the series)
#' @param getSeries Function to retrieve series (argument: 'indices', integer vector)
#' @param nb_curves How many series? (this is known, at this stage)
#' @inheritParams claws
#'
#' @return A matrix of K synchrones in columns (same length as the series)
#'
#' @export
computeSynchrones = function(medoids, getSeries, nb_curves,
	nb_series_per_chunk, ncores_clust=1,verbose=FALSE,parll=TRUE)
{
	# Synchrones computation is embarassingly parallel: compute it by chunks of series
	computeSynchronesChunk = function(indices)
	{
		if (parll)
		{
			require("bigmemory", quietly=TRUE)
			requireNamespace("synchronicity", quietly=TRUE)
			require("epclust", quietly=TRUE)
			# The big.matrix objects need to be attached to be usable on the workers
			synchrones <- bigmemory::attach.big.matrix(synchrones_desc)
			medoids <- bigmemory::attach.big.matrix(medoids_desc)
			m <- synchronicity::attach.mutex(m_desc)
		}

		# Obtain a chunk of reference series
		series_chunk = getSeries(indices)
		nb_series_chunk = ncol(series_chunk)

		# Get medoids indices for this chunk of series
		for (i in seq_len(nb_series_chunk))
			mi[i] <- which.min( colSums( sweep(medoids, 1, series_chunk[,i], '-')^2 ) )

		# Update synchrones using mi above, grouping it by values of mi (in 1...K)
		# to avoid too many lock/unlock
		for (i in seq_len(K))
		{
			# lock / unlock required because several writes at the same time
			if (parll)
				synchronicity::lock(m)
			synchrones[,i] = synchrones[,i] + rowSums(series_chunk[,mi==i])
			if (parll)
				synchronicity::unlock(m)
		}
		NULL
	}

	K = ncol(medoids)
	L = nrow(medoids)
	# Use bigmemory (shared==TRUE by default) + synchronicity to fill synchrones in //
	synchrones = bigmemory::big.matrix(nrow=L, ncol=K, type="double", init=0.)
	# NOTE: synchronicity is only for Linux & MacOS; on Windows: run sequentially
	parll = (parll && requireNamespace("synchronicity",quietly=TRUE)
		&& Sys.info()['sysname'] != "Windows")
	if (parll)
	{
		m <- synchronicity::boost.mutex() #for lock/unlock, see computeSynchronesChunk
		# mutex and big.matrix objects cannot be passed directly:
		# they will be accessed from their description
		m_desc <- synchronicity::describe(m)
		synchrones_desc = bigmemory::describe(synchrones)
		medoids <- bigmemory::as.big.matrix(medoids)
		medoids_desc <- bigmemory::describe(medoids)
		cl = parallel::makeCluster(ncores_clust)
		parallel::clusterExport(cl, envir=environment(),
			varlist=c("synchrones_desc","m_desc","medoids_desc","getSeries"))
	}

	if (verbose)
		cat(paste("--- Compute ",K," synchrones with ",nb_curves," series\n", sep=""))

	# Balance tasks by splitting 1:nb_curves into groups of size <= nb_series_per_chunk
	indices_workers = .splitIndices(seq_len(nb_curves), nb_series_per_chunk)
	ignored <-
		if (parll)
			parallel::parLapply(cl, indices_workers, computeSynchronesChunk)
		else
			lapply(indices_workers, computeSynchronesChunk)

	if (parll)
		parallel::stopCluster(cl)

	return (synchrones[,])
}