#' @rdname clustering
#' @export
clusteringTask1 <- function(indices, getContribs, K1, algoClust1, nb_items_clust,
- ncores_clust=3, verbose=FALSE, parll=TRUE)
+ ncores_clust=3, verbose=FALSE)
{
if (verbose)
cat(paste("*** Clustering task 1 on ",length(indices)," series [start]\n", sep=""))
if (length(indices) <= K1)
return (indices)
+ parll <- (ncores_clust > 1)
if (parll)
{
# outfile=="" to see stderr/stdout on terminal
#' @rdname clustering
#' @export
clusteringTask2 <- function(indices, getSeries, K2, algoClust2, nb_series_per_chunk,
- smooth_lvl, nvoice, nbytes, endian, ncores_clust=3, verbose=FALSE, parll=TRUE)
+ smooth_lvl, nvoice, nbytes, endian, ncores_clust=3, verbose=FALSE)
{
if (verbose)
cat(paste("*** Clustering task 2 on ",length(indices)," medoids\n", sep=""))
# A) Compute the WER distances (Wavelets Extended coefficient of deteRmination)
distances <- computeWerDists(indices, getSeries, nb_series_per_chunk,
- smooth_lvl, nvoice, nbytes, endian, ncores_clust, verbose, parll)
+ smooth_lvl, nvoice, nbytes, endian, ncores_clust, verbose)
# B) Apply clustering algorithm 2 on the WER distances matrix
if (verbose)