--- /dev/null
+#' 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","getRefSeries"))
+ }
+
+ if (verbose)
+ cat(paste("--- Compute ",K," synchrones with ",nb_curves," series\n", sep=""))
+
+ # Balance tasks by splitting 1:nb_ref_curves into groups of size <= nb_series_per_chunk
+ indices_workers = .splitIndices(seq_len(nb_ref_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[,])
+}