Remove parll arg (redundant with ncores_XX)

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,
-       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=""))
@@ -31,6 +31,7 @@ clusteringTask1 <- function(indices, getContribs, K1, algoClust1, nb_items_clust
        if (length(indices) <= K1)
                return (indices)
 
+       parll <- (ncores_clust > 1)
        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,
-       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=""))
@@ -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,
-               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)

diff --git a/epclust/R/computeSynchrones.R b/epclust/R/computeSynchrones.R
index 3c1959a..3a29546 100644 (file)
--- a/epclust/R/computeSynchrones.R
+++ b/epclust/R/computeSynchrones.R
@@ -12,7 +12,7 @@
 #'
 #' @export
 computeSynchrones <- function(medoids, getSeries, nb_curves,
-       nb_series_per_chunk, ncores=3, verbose=FALSE, parll=TRUE)
+       nb_series_per_chunk, ncores=3, verbose=FALSE)
 {
        # Synchrones computation is embarassingly parallel: compute it by chunks of series
        computeSynchronesChunk <- function(indices)
@@ -43,7 +43,7 @@ computeSynchrones <- function(medoids, getSeries, nb_curves,
        # 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)
+       parll <- (ncores > 1 && requireNamespace("synchronicity",quietly=TRUE)
                && Sys.info()['sysname'] != "Windows")
 
        if (parll)

diff --git a/epclust/R/computeWerDists.R b/epclust/R/computeWerDists.R
index c6fa633..5f12896 100644 (file)
--- a/epclust/R/computeWerDists.R
+++ b/epclust/R/computeWerDists.R
@@ -13,7 +13,7 @@
 #'
 #' @export
 computeWerDists <- function(indices, getSeries, nb_series_per_chunk, smooth_lvl=3, nvoice=4,
-       nbytes=4, endian=.Platform$endian, ncores=3, verbose=FALSE, parll=TRUE)
+       nbytes=4, endian=.Platform$endian, ncores=3, verbose=FALSE)
 {
        n <- length(indices)
        L <- length(getSeries(1)) #TODO: not very neat way to get L
@@ -97,6 +97,7 @@ computeWerDists <- function(indices, getSeries, nb_series_per_chunk, smooth_lvl=
        for (inds in indices_cwt)
                computeSaveCWT(inds)
 
+       parll <- (ncores > 1)
        if (parll)
        {
                # outfile=="" to see stderr/stdout on terminal

diff --git a/epclust/R/main.R b/epclust/R/main.R
index 4fdc5ae..b6eb511 100644 (file)
--- a/epclust/R/main.R
+++ b/epclust/R/main.R
@@ -70,7 +70,6 @@
 #' @param nbytes 4 or 8 bytes to (de)serialize a floating-point number
 #' @param endian Endianness for (de)serialization: "little" or "big"
 #' @param verbose FALSE: nothing printed; TRUE: some execution traces
-#' @param parll TRUE: run in parallel. FALSE: run sequentially
 #'
 #' @return A list:
 #' \itemize{
@@ -87,7 +86,7 @@
 #' @examples
 #' \dontrun{
 #' # WER distances computations are too long for CRAN (for now)
-#' parll = FALSE #on this small example, sequential run is faster
+#' # Note: on this small example, sequential run is faster
 #'
 #' # Random series around cos(x,2x,3x)/sin(x,2x,3x)
 #' x <- seq(0,50,0.05)
@@ -101,12 +100,12 @@
 #' series = series[,permut]
 #' #dim(series) #c(240,1001)
 #' res_ascii <- claws(series, K1=30, K2=6, nb_series_per_chunk=500,
-#'   nb_items_clust=100, random=FALSE, verbose=TRUE, parll=parll)
+#'   nb_items_clust=100, random=FALSE, verbose=TRUE, ncores_clust=1)
 #'
 #' # Same example, from CSV file
 #' csv_file <- tempfile(pattern="epclust_series.csv_")
 #' write.table(t(series), csv_file, sep=",", row.names=FALSE, col.names=FALSE)
-#' res_csv <- claws(csv_file, 30, 6, 500, 100, random=FALSE, parll=parll)
+#' res_csv <- claws(csv_file, 30, 6, 500, 100, random=FALSE, ncores_clust=1)
 #'
 #' # Same example, from binary file
 #' bin_file <- tempfile(pattern="epclust_series.bin_")
@@ -114,7 +113,7 @@
 #' endian <- "little"
 #' binarize(csv_file, bin_file, 500, ",", nbytes, endian)
 #' getSeries <- function(indices) getDataInFile(indices, bin_file, nbytes, endian)
-#' res_bin <- claws(getSeries, 30, 6, 500, 100, random=FALSE, parll=parll)
+#' res_bin <- claws(getSeries, 30, 6, 500, 100, random=FALSE, ncores_clust=1)
 #' unlink(csv_file)
 #' unlink(bin_file)
 #'
@@ -147,7 +146,7 @@
 #'   df_series <- dbGetQuery(series_db, request)
 #'   matrix(df_series[,"value"], nrow=serie_length)
 #' }
-#' res_db <- claws(getSeries, 30, 6, 500, 100, random=FALSE, parll=parll)
+#' res_db <- claws(getSeries, 30, 6, 500, 100, random=FALSE, ncores_clust=1)
 #' dbDisconnect(series_db)
 #'
 #' # All results should be equal:
@@ -161,7 +160,7 @@ claws <- function(series, K1, K2, nb_series_per_chunk, nb_items_clust=5*K1,
        algoClust2=function(dists,K) cluster::pam(dists,K,diss=TRUE,pamonce=1)$id.med,
        wav_filt="d8", contrib_type="absolute", WER="end", smooth_lvl=3, nvoice=4,
        random=TRUE, ntasks=1, ncores_tasks=1, ncores_clust=3, sep=",", nbytes=4,
-       endian=.Platform$endian, verbose=FALSE, parll=TRUE)
+       endian=.Platform$endian, verbose=FALSE)
 {
        # Check/transform arguments
        if (!is.matrix(series) && !bigmemory::is.big.matrix(series)
@@ -191,7 +190,6 @@ claws <- function(series, K1, K2, nb_series_per_chunk, nb_items_clust=5*K1,
                stop("'sep': character")
        nbytes <- .toInteger(nbytes, function(x) x==4 || x==8)
        verbose <- .toLogical(verbose)
-       parll <- .toLogical(parll)
 
        # Binarize series if it is not a function; the aim is to always use a function,
        # to uniformize treatments. An equally good alternative would be to use a file-backed
@@ -243,6 +241,7 @@ claws <- function(series, K1, K2, nb_series_per_chunk, nb_items_clust=5*K1,
                indices_all[((i-1)*nb_series_per_task+1):upper_bound]
        })
 
+       parll <- (ncores_tasks > 1)
        if (parll && ntasks>1)
        {
                # Initialize parallel runs: outfile="" allow to output verbose traces in the console
@@ -252,7 +251,7 @@ claws <- function(series, K1, K2, nb_series_per_chunk, nb_items_clust=5*K1,
                                parallel::makeCluster(ncores_tasks, outfile="")
                        else
                                parallel::makeCluster(ncores_tasks)
-               varlist <- c("ncores_clust","verbose","parll", #task 1 & 2
+               varlist <- c("ncores_clust","verbose", #task 1 & 2
                        "K1","getContribs","algoClust1","nb_items_clust") #task 1
                if (WER=="mix")
                {
@@ -274,11 +273,11 @@ claws <- function(series, K1, K2, nb_series_per_chunk, nb_items_clust=5*K1,
                if (parll && ntasks>1)
                        require("epclust", quietly=TRUE)
                indices_medoids <- clusteringTask1(inds, getContribs, K1, algoClust1,
-                       nb_items_clust, ncores_clust, verbose, parll)
+                       nb_items_clust, ncores_clust, verbose)
                if (WER=="mix")
                {
                        indices_medoids <- clusteringTask2(indices_medoids, getSeries, K2, algoClust2,
-                               nb_series_per_chunk,smooth_lvl,nvoice,nbytes,endian,ncores_clust,verbose,parll)
+                               nb_series_per_chunk,smooth_lvl,nvoice,nbytes,endian,ncores_clust,verbose)
                }
                indices_medoids
        }
@@ -313,16 +312,16 @@ claws <- function(series, K1, K2, nb_series_per_chunk, nb_items_clust=5*K1,
        if (verbose)
                cat("...Run final // stage 1 + stage 2\n")
        indices_medoids <- clusteringTask1(indices_medoids_all, getContribs, K1, algoClust1,
-               nb_items_clust, ncores_tasks*ncores_clust, verbose, parll)
+               nb_items_clust, ncores_tasks*ncores_clust, verbose)
 
        indices_medoids <- clusteringTask2(indices_medoids, getSeries, K2, algoClust2,
-               nb_series_per_chunk,smooth_lvl,nvoice,nbytes,endian,ncores_last_stage,verbose,parll)
+               nb_series_per_chunk,smooth_lvl,nvoice,nbytes,endian,ncores_last_stage,verbose)
 
        # Compute synchrones, that is to say the cumulated power consumptions for each of the K2
        # final groups.
        medoids <- getSeries(indices_medoids)
        synchrones <- computeSynchrones(medoids, getSeries, nb_curves, nb_series_per_chunk,
-               ncores_last_stage, verbose, parll)
+               ncores_last_stage, verbose)
 
        # NOTE: no need to use big.matrix here, since there are only K2 << K1 << N remaining curves
        list("medoids"=medoids, "ranks"=indices_medoids, "synchrones"=synchrones)