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Remove parll arg (redundant with ncores_XX)
[epclust.git]
/
epclust
/
R
/
clustering.R
diff --git
a/epclust/R/clustering.R
b/epclust/R/clustering.R
index
886bfbc
..
5b5f668
100644
(file)
--- a/
epclust/R/clustering.R
+++ b/
epclust/R/clustering.R
@@
-23,7
+23,7
@@
NULL
#' @rdname clustering
#' @export
clusteringTask1 <- function(indices, getContribs, K1, algoClust1, nb_items_clust,
#' @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 (verbose)
cat(paste("*** Clustering task 1 on ",length(indices)," series [start]\n", sep=""))
@@
-31,6
+31,7
@@
clusteringTask1 <- function(indices, getContribs, K1, algoClust1, nb_items_clust
if (length(indices) <= K1)
return (indices)
if (length(indices) <= K1)
return (indices)
+ parll <- (ncores_clust > 1)
if (parll)
{
# outfile=="" to see stderr/stdout on terminal
if (parll)
{
# outfile=="" to see stderr/stdout on terminal
@@
-74,7
+75,7
@@
clusteringTask1 <- function(indices, getContribs, K1, algoClust1, nb_items_clust
#' @rdname clustering
#' @export
clusteringTask2 <- function(indices, getSeries, K2, algoClust2, nb_series_per_chunk,
#' @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=""))
{
if (verbose)
cat(paste("*** Clustering task 2 on ",length(indices)," medoids\n", sep=""))
@@
-84,7
+85,7
@@
clusteringTask2 <- function(indices, getSeries, K2, algoClust2, nb_series_per_ch
# A) Compute the WER distances (Wavelets Extended coefficient of deteRmination)
distances <- computeWerDists(indices, getSeries, nb_series_per_chunk,
# 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)
# B) Apply clustering algorithm 2 on the WER distances matrix
if (verbose)