#' @rdname clustering
#' @export
-clusteringTask1 = function(indices, getContribs, K1, algoClust1, nb_items_clust,
+clusteringTask1 <- function(indices, getContribs, K1, algoClust1, nb_items_clust,
ncores_clust=1, verbose=FALSE, parll=TRUE)
{
if (parll)
{
- cl = parallel::makeCluster(ncores_clust, outfile = "")
+ # outfile=="" to see stderr/stdout on terminal
+ cl <- parallel::makeCluster(ncores_clust, outfile = "")
parallel::clusterExport(cl, c("getContribs","K1","verbose"), envir=environment())
}
# Iterate clustering algorithm 1 until K1 medoids are found
while (length(indices) > K1)
{
# Balance tasks by splitting the indices set - as evenly as possible
- indices_workers = .splitIndices(indices, nb_items_clust, min_size=K1+1)
+ indices_workers <- .splitIndices(indices, nb_items_clust, min_size=K1+1)
if (verbose)
cat(paste("*** [iterated] Clustering task 1 on ",length(indices)," series\n", sep=""))
indices <-
#' @rdname clustering
#' @export
-clusteringTask2 = function(indices, getSeries, K2, algoClust2, nb_series_per_chunk,
- nvoice, nbytes, endian, ncores_clust=1, verbose=FALSE, parll=TRUE)
+clusteringTask2 <- function(indices, getSeries, K2, algoClust2, nb_series_per_chunk,
+ smooth_lvl, nvoice, nbytes, endian, ncores_clust=1, verbose=FALSE, parll=TRUE)
{
if (verbose)
cat(paste("*** Clustering task 2 on ",length(indices)," medoids\n", sep=""))
return (indices)
# A) Compute the WER distances (Wavelets Extended coefficient of deteRmination)
- distances = computeWerDists(indices, getSeries, nb_series_per_chunk,
- nvoice, nbytes, endian, ncores_clust, verbose, parll)
+ distances <- computeWerDists(indices, getSeries, nb_series_per_chunk,
+ smooth_lvl, nvoice, nbytes, endian, ncores_clust, verbose, parll)
# B) Apply clustering algorithm 2 on the WER distances matrix
if (verbose)