3 #' Compute the synchrones curves (sums of clusters elements) from a matrix of medoids,
4 #' using euclidian distance.
6 #' @param medoids matrix of K medoids curves in columns
7 #' @param nb_curves How many series? (this is known, at this stage)
8 #' @inheritParams claws
9 #' @inheritParams computeWerDists
11 #' @return A matrix of K synchrones in columns (same length as the series)
14 computeSynchrones <- function(medoids, getSeries, nb_curves,
15 nb_series_per_chunk, ncores=3, verbose=FALSE)
17 # Synchrones computation is embarassingly parallel: compute it by chunks of series
18 computeSynchronesChunk <- function(indices)
20 # Obtain a chunk of reference series
21 series_chunk <- getSeries(indices)
22 nb_series_chunk <- ncol(series_chunk)
24 # Get medoids indices for this chunk of series
25 mi <- assignMedoids(series_chunk, medoids[,])
27 # Update synchrones using mi above, grouping it by values of mi (in 1...K)
28 # to avoid too many lock/unlock
31 # lock / unlock required because several writes at the same time
33 synchronicity::lock(m)
34 synchrones[,i] <- synchrones[,i] + rowSums(as.matrix(series_chunk[,mi==i]))
36 synchronicity::unlock(m)
43 # Use bigmemory (shared==TRUE by default) + synchronicity to fill synchrones in //
44 synchrones <- bigmemory::big.matrix(nrow=L, ncol=K, type="double", init=0.)
45 # NOTE: synchronicity is only for Linux & MacOS; on Windows: run sequentially
46 parll <- (ncores > 1 && requireNamespace("synchronicity",quietly=TRUE)
47 && Sys.info()['sysname'] != "Windows")
50 m <- synchronicity::boost.mutex() #for lock/unlock, see computeSynchronesChunk
53 cat(paste("--- Compute ",K," synchrones with ",nb_curves," series\n", sep=""))
55 # Balance tasks by splitting 1:nb_curves into groups of size <= nb_series_per_chunk
56 indices_workers <- .splitIndices(seq_len(nb_curves), nb_series_per_chunk)
60 parallel::mclapply(indices_workers,
61 function(inds) computeSynchronesChunk(inds), mc.cores=ncores)
64 lapply(indices_workers, computeSynchronesChunk)
66 return (synchrones[,])