From eef6f6c97277ea3ce760981e5244cbde7fc904a0 Mon Sep 17 00:00:00 2001
From: Benjamin Auder <benjamin.auder@somewhere>
Date: Fri, 10 Mar 2017 08:10:49 +0100
Subject: [PATCH] TODO: args, et finir tests; relancer

---
 TODO                     |   3 +
 epclust/R/clustering.R   |  24 ++--
 epclust/R/de_serialize.R |  11 +-
 epclust/R/main.R         | 235 ++++++++++++++++++++++++---------------
 4 files changed, 168 insertions(+), 105 deletions(-)

diff --git a/TODO b/TODO
index 53b4c97..53a82b3 100644
--- a/TODO
+++ b/TODO
@@ -67,3 +67,6 @@ cwt : trim R part
 // : clever by rows retenir cwt...
 
 Stockage matrices : en colonnes systématiquement ?
+
+TODO: revoir les arguments, simplifier (dans les clustering...),
+  permettre algos de clustering quelconques, args: medoids (curves puis dists), K
diff --git a/epclust/R/clustering.R b/epclust/R/clustering.R
index a4c273a..14915ab 100644
--- a/epclust/R/clustering.R
+++ b/epclust/R/clustering.R
@@ -1,6 +1,6 @@
 #' @name clustering
 #' @rdname clustering
-#' @aliases clusteringTask1 computeClusters1 computeClusters2
+#' @aliases clusteringTask1 clusteringTask2 computeClusters1 computeClusters2
 #'
 #' @title Two-stage clustering, withing one task (see \code{claws()})
 #'
@@ -31,7 +31,7 @@ NULL
 #' @rdname clustering
 #' @export
 clusteringTask1 = function(
-	indices, getContribs, K1, nb_series_per_chunk, ncores_clust=1, verbose=FALSE, parll=TRUE)
+	indices, getContribs, K1, nb_items_per_chunk, ncores_clust=1, verbose=FALSE, parll=TRUE)
 {
 	if (verbose)
 		cat(paste("*** Clustering task 1 on ",length(indices)," lines\n", sep=""))
@@ -87,7 +87,7 @@ computeClusters1 = function(contribs, K1, verbose=FALSE)
 {
 	if (verbose)
 		cat(paste("   computeClusters1() on ",nrow(contribs)," lines\n", sep=""))
-	cluster::pam(contribs, K1, diss=FALSE)$id.med
+	cluster::pam(        t(contribs)       , K1, diss=FALSE)$id.med
 }
 
 #' @rdname clustering
@@ -96,7 +96,7 @@ computeClusters2 = function(distances, K2, verbose=FALSE)
 {
 	if (verbose)
 		cat(paste("   computeClusters2() on ",nrow(distances)," lines\n", sep=""))
-	cluster::pam(distances, K2, diss=TRUE)$id.med
+	cluster::pam(       distances        , K2, diss=TRUE)$id.med
 }
 
 #' computeSynchrones
@@ -110,7 +110,7 @@ computeClusters2 = function(distances, K2, verbose=FALSE)
 #' @param nb_ref_curves How many reference series? (This number is known at this stage)
 #' @inheritParams claws
 #'
-#' @return A big.matrix of size K1 x L where L = data_length
+#' @return A big.matrix of size L x K1 where L = length of a serie
 #'
 #' @export
 computeSynchrones = function(medoids, getRefSeries,
@@ -142,8 +142,8 @@ computeSynchrones = function(medoids, getRefSeries,
 		{
 			if (parll)
 				synchronicity::lock(m)
-			synchrones[ mi[i], ] = synchrones[ mi[i], ] + ref_series[i,]
-			counts[ mi[i] ] = counts[ mi[i] ] + 1 #TODO: remove counts?
+			synchrones[, mi[i] ] = synchrones[, mi[i] ] + ref_series[,i]
+			counts[ mi[i] ] = counts[ mi[i] ] + 1 #TODO: remove counts? ...or as arg?!
 			if (parll)
 				synchronicity::unlock(m)
 		}
@@ -152,7 +152,7 @@ computeSynchrones = function(medoids, getRefSeries,
 	K = nrow(medoids) ; L = ncol(medoids)
 	# Use bigmemory (shared==TRUE by default) + synchronicity to fill synchrones in //
 	# TODO: if size > RAM (not our case), use file-backed big.matrix
-	synchrones = bigmemory::big.matrix(nrow=K, ncol=L, type="double", init=0.)
+	synchrones = bigmemory::big.matrix(nrow=L, ncol=K, type="double", init=0.)
 	counts = bigmemory::big.matrix(nrow=K, ncol=1, type="double", init=0)
 	# synchronicity is only for Linux & MacOS; on Windows: run sequentially
 	parll = (requireNamespace("synchronicity",quietly=TRUE)
@@ -181,14 +181,14 @@ computeSynchrones = function(medoids, getRefSeries,
 
 	#TODO: can we avoid this loop? ( synchrones = sweep(synchrones, 1, counts, '/') )
 	for (i in seq_len(K))
-		synchrones[i,] = synchrones[i,] / counts[i,1]
+		synchrones[,i] = synchrones[,i] / counts[i]
 	#NOTE: odds for some clusters to be empty? (when series already come from stage 2)
 	#      ...maybe; but let's hope resulting K1' be still quite bigger than K2
-	noNA_rows = sapply(seq_len(K), function(i) all(!is.nan(synchrones[i,])))
+	noNA_rows = sapply(seq_len(K), function(i) all(!is.nan(synchrones[,i])))
 	if (all(noNA_rows))
 		return (synchrones)
 	# Else: some clusters are empty, need to slice synchrones
-	synchrones[noNA_rows,]
+	bigmemory::as.big.matrix(synchrones[,noNA_rows])
 }
 
 #' computeWerDists
@@ -272,7 +272,7 @@ computeWerDists = function(synchrones, nbytes,endian,ncores_clust=1,verbose=FALS
 	{
 		#from cwt_file ...
 		res <- getDataInFile(c(2*index-1,2*index), cwt_file, nbytes, endian)
-	###############TODO:	
+	###############TODO:
 	}
 
 	# Distance between rows i and j
diff --git a/epclust/R/de_serialize.R b/epclust/R/de_serialize.R
index b6684d2..f04c13a 100644
--- a/epclust/R/de_serialize.R
+++ b/epclust/R/de_serialize.R
@@ -45,7 +45,7 @@ binarize = function(data_ascii, data_bin_file, nb_per_chunk,
 		#number of items always on 8 bytes
 		writeBin(0L, data_bin, size=8, endian=endian)
 		if ( is_matrix )
-			data_length = ncol(data_ascii)
+			data_length = nrow(data_ascii)
 		else #connection
 		{
 			data_line = scan(data_ascii, double(), sep=sep, nlines=1, quiet=TRUE)
@@ -61,8 +61,8 @@ binarize = function(data_ascii, data_bin_file, nb_per_chunk,
 		if ( is_matrix )
 		{
 			data_chunk =
-				if (index <= nrow(data_ascii))
-					as.double(t(data_ascii[index:min(nrow(data_ascii),index+nb_per_chunk-1),]))
+				if (index <= ncol(data_ascii))
+					as.double(data_ascii[,index:min(nrow(data_ascii),index+nb_per_chunk-1)])
 				else
 					double(0)
 			index = index + nb_per_chunk
@@ -113,14 +113,13 @@ getDataInFile = function(indices, data_bin_file, nbytes=4, endian=.Platform$endi
 	data_bin = file(data_bin_file, "rb")
 	data_size = file.info(data_bin_file)$size
 	data_length = readBin(data_bin, "integer", n=1, size=8, endian=endian)
-	#Ou t(sapply(...)) (+ rapide ?)
-	data_ascii = do.call( rbind, lapply( indices, function(i) {
+	data_ascii = sapply( indices, function(i) {
 		offset = 8+(i-1)*data_length*nbytes
 		if (offset > data_size)
 			return (vector("double",0))
 		ignored = seek(data_bin, offset)
 		readBin(data_bin, "double", n=data_length, size=nbytes, endian=endian)
-	} ) )
+	} )
 	close(data_bin)
 	if (ncol(data_ascii)>0) data_ascii else NULL
 }
diff --git a/epclust/R/main.R b/epclust/R/main.R
index 28217c3..86dac64 100644
--- a/epclust/R/main.R
+++ b/epclust/R/main.R
@@ -1,67 +1,97 @@
 #' CLAWS: CLustering with wAvelets and Wer distanceS
 #'
-#' Groups electricity power curves (or any series of similar nature) by applying PAM
-#' algorithm in parallel to chunks of size \code{nb_series_per_chunk}. Input series
-#' must be sampled on the same time grid, no missing values.
+#' Cluster electricity power curves (or any series of similar nature) by applying a
+#' two stage procedure in parallel (see details).
+#' Input series must be sampled on the same time grid, no missing values.
+#'
+#' @details Summary of the function execution flow:
+#'   \enumerate{
+#'     \item Compute and serialize all contributions, obtained through discrete wavelet
+#'       decomposition (see Antoniadis & al. [2013])
+#'     \item Divide series into \code{ntasks} groups to process in parallel. In each task:
+#'     \enumerate{
+#'       \item iterate the first clustering algorithm on its aggregated outputs,
+#'         on inputs of size \code{nb_items_clust}
+#'       \item optionally, if WER=="mix":
+#'         a) compute the K1 synchrones curves,
+#'         b) compute WER distances (K1xK1 matrix) between synchrones and
+#'         c) apply the second clustering algorithm
+#'     }
+#'     \item Launch a final task on the aggregated outputs of all previous tasks:
+#'       in the case WER=="end" this task takes indices in input, otherwise
+#'       (medoid) curves
+#'   }
+#'   The main argument -- \code{getSeries} -- has a quite misleading name, since it can be
+#'   either a [big.]matrix, a CSV file, a connection or a user function to retrieve
+#'   series; the name was chosen because all types of arguments are converted to a function.
+#'   When \code{getSeries} is given as a function, it must take a single argument,
+#'   'indices', integer vector equal to the indices of the curves to retrieve;
+#'   see SQLite example. The nature and role of other arguments should be clear
 #'
 #' @param getSeries Access to the (time-)series, which can be of one of the three
 #'   following types:
 #'   \itemize{
-#'     \item [big.]matrix: each line contains all the values for one time-serie, ordered by time
+#'     \item [big.]matrix: each column contains the (time-ordered) values of one time-serie
 #'     \item connection: any R connection object providing lines as described above
 #'     \item character: name of a CSV file containing series in rows (no header)
 #'     \item function: a custom way to retrieve the curves; it has only one argument:
-#'       the indices of the series to be retrieved. See examples
+#'       the indices of the series to be retrieved. See SQLite example
 #'   }
-#' @inheritParams clustering
-#' @param K1 Number of super-consumers to be found after stage 1 (K1 << N)
+#' @param K1 Number of clusters to be found after stage 1 (K1 << N [number of series])
 #' @param K2 Number of clusters to be found after stage 2 (K2 << K1)
-#' @param wf Wavelet transform filter; see ?wavelets::wt.filter
-#' @param ctype Type of contribution: "relative" or "absolute" (or any prefix)
-#' @param WER "end" to apply stage 2 after stage 1 has fully iterated, or "mix" to apply stage 2
-#'   at the end of each task
+#' @param nb_per_chunk (Maximum) number of items to retrieve in one batch, for both types of
+#'   retrieval: resp. series and contribution; in a vector of size 2
+#' @param nb_items_clust1 (Maximum) number of items in input of the clustering algorithm
+#'   for stage 1
+#' @param wav_filt Wavelet transform filter; see ?wavelets::wt.filter
+#' @param contrib_type Type of contribution: "relative", "logit" or "absolute" (any prefix)
+#' @param WER "end" to apply stage 2 after stage 1 has fully iterated, or "mix" to apply
+#'   stage 2 at the end of each task
 #' @param random TRUE (default) for random chunks repartition
-#' @param ntasks Number of tasks (parallel iterations to obtain K1 medoids); default: 1.
-#'   Note: ntasks << N, so that N is "roughly divisible" by N (number of series)
-#' @param ncores_tasks "MPI" number of parallel tasks (1 to disable: sequential tasks)
-#' @param ncores_clust "OpenMP" number of parallel clusterings in one task
-#' @param nb_series_per_chunk (~Maximum) number of series in each group, inside a task
-#' @param min_series_per_chunk Minimum number of series in each group
+#' @param ntasks Number of tasks (parallel iterations to obtain K1 [if WER=="end"]
+#'   or K2 [if WER=="mix"] medoids); default: 1.
+#'   Note: ntasks << N (number of series), so that N is "roughly divisible" by ntasks
+#' @param ncores_tasks Number of parallel tasks (1 to disable: sequential tasks)
+#' @param ncores_clust Number of parallel clusterings in one task (4 should be a minimum)
 #' @param sep Separator in CSV input file (if any provided)
 #' @param nbytes Number of bytes to serialize a floating-point number; 4 or 8
-#' @param endian Endianness to use for (de)serialization. Use "little" or "big" for portability
+#' @param endian Endianness for (de)serialization ("little" or "big")
 #' @param verbose Level of verbosity (0/FALSE for nothing or 1/TRUE for all; devel stage)
 #' @param parll TRUE to fully parallelize; otherwise run sequentially (debug, comparison)
 #'
-#' @return A big.matrix of the final medoids curves (K2) in rows
+#' @return A matrix of the final K2 medoids curves, in columns
+#'
+#' @references Clustering functional data using Wavelets [2013];
+#'   A. Antoniadis, X. Brossat, J. Cugliari & J.-M. Poggi.
+#'   Inter. J. of Wavelets, Multiresolution and Information Procesing,
+#'   vol. 11, No 1, pp.1-30. doi:10.1142/S0219691313500033
 #'
 #' @examples
 #' \dontrun{
-#' # WER distances computations are a bit too long for CRAN (for now)
+#' # WER distances computations are too long for CRAN (for now)
 #'
 #' # Random series around cos(x,2x,3x)/sin(x,2x,3x)
 #' x = seq(0,500,0.05)
 #' L = length(x) #10001
-#' ref_series = matrix( c(cos(x), cos(2*x), cos(3*x), sin(x), sin(2*x), sin(3*x)),
-#'   byrow=TRUE, ncol=L )
+#' ref_series = matrix( c(cos(x),cos(2*x),cos(3*x),sin(x),sin(2*x),sin(3*x)), ncol=6 )
 #' library(wmtsa)
-#' series = do.call( rbind, lapply( 1:6, function(i)
-#'   do.call(rbind, wmtsa::wavBootstrap(ref_series[i,], n.realization=400)) ) )
+#' series = do.call( cbind, lapply( 1:6, function(i)
+#'   do.call(cbind, wmtsa::wavBootstrap(ref_series[i,], n.realization=400)) ) )
 #' #dim(series) #c(2400,10001)
-#' medoids_ascii = claws(series, K1=60, K2=6, "d8", "rel", nb_series_per_chunk=500)
+#' medoids_ascii = claws(series, K1=60, K2=6, nb_per_chunk=c(200,500), verbose=TRUE)
 #'
 #' # Same example, from CSV file
 #' csv_file = "/tmp/epclust_series.csv"
 #' write.table(series, csv_file, sep=",", row.names=FALSE, col.names=FALSE)
-#' medoids_csv = claws(csv_file, K1=60, K2=6, "d8", "rel", nb_series_per_chunk=500)
+#' medoids_csv = claws(csv_file, K1=60, K2=6, nb_per_chunk=c(200,500))
 #'
 #' # Same example, from binary file
-#' bin_file = "/tmp/epclust_series.bin"
-#' nbytes = 8
-#' endian = "little"
-#' epclust::binarize(csv_file, bin_file, 500, nbytes, endian)
-#' getSeries = function(indices) getDataInFile(indices, bin_file, nbytes, endian)
-#' medoids_bin = claws(getSeries, K1=60, K2=6, "d8", "rel", nb_series_per_chunk=500)
+#' bin_file <- "/tmp/epclust_series.bin"
+#' nbytes <- 8
+#' endian <- "little"
+#' binarize(csv_file, bin_file, 500, nbytes, endian)
+#' getSeries <- function(indices) getDataInFile(indices, bin_file, nbytes, endian)
+#' medoids_bin <- claws(getSeries, K1=60, K2=6, nb_per_chunk=c(200,500))
 #' unlink(csv_file)
 #' unlink(bin_file)
 #'
@@ -69,8 +99,8 @@
 #' library(DBI)
 #' series_db <- dbConnect(RSQLite::SQLite(), "file::memory:")
 #' # Prepare data.frame in DB-format
-#' n = nrow(series)
-#' time_values = data.frame(
+#' n <- nrow(series)
+#' time_values <- data.frame(
 #'   id = rep(1:n,each=L),
 #'   time = rep( as.POSIXct(1800*(0:n),"GMT",origin="2001-01-01"), L ),
 #'   value = as.double(t(series)) )
@@ -78,17 +108,17 @@
 #' # Fill associative array, map index to identifier
 #' indexToID_inDB <- as.character(
 #'   dbGetQuery(series_db, 'SELECT DISTINCT id FROM time_values')[,"id"] )
-#' getSeries = function(indices) {
-#'   request = "SELECT id,value FROM times_values WHERE id in ("
+#' serie_length <- as.integer( dbGetQuery(series_db,
+#'   paste("SELECT COUNT * FROM time_values WHERE id == ",indexToID_inDB[1],sep="")) )
+#' getSeries <- function(indices) {
+#'   request <- "SELECT id,value FROM times_values WHERE id in ("
 #'   for (i in indices)
-#'     request = paste(request, i, ",", sep="")
-#'   request = paste(request, ")", sep="")
-#'   df_series = dbGetQuery(series_db, request)
-#'   # Assume that all series share same length at this stage
-#'   ts_length = sum(df_series[,"id"] == df_series[1,"id"])
-#'   t( as.matrix(df_series[,"value"], nrow=ts_length) )
+#'     request <- paste(request, indexToID_inDB[i], ",", sep="")
+#'   request <- paste(request, ")", sep="")
+#'   df_series <- dbGetQuery(series_db, request)
+#'   as.matrix(df_series[,"value"], nrow=serie_length)
 #' }
-#' medoids_db = claws(getSeries, K1=60, K2=6, "d8", "rel", nb_series_per_chunk=500)
+#' medoids_db = claws(getSeries, K1=60, K2=6, nb_per_chunk=c(200,500))
 #' dbDisconnect(series_db)
 #'
 #' # All computed medoids should be the same:
@@ -98,12 +128,12 @@
 #' digest::sha1(medoids_db)
 #' }
 #' @export
-claws = function(getSeries, K1, K2,
-	wf,ctype, #stage 1
+claws <- function(getSeries, K1, K2,
+	nb_per_chunk,nb_items_clust1=7*K1 #volumes of data
+	wav_filt="d8",contrib_type="absolute", #stage 1
 	WER="end", #stage 2
 	random=TRUE, #randomize series order?
-	ntasks=1, ncores_tasks=1, ncores_clust=4, #control parallelism
-	nb_series_per_chunk=50*K1, min_series_per_chunk=5*K1, #chunk size
+	ntasks=1, ncores_tasks=1, ncores_clust=4, #parallelism
 	sep=",", #ASCII input separator
 	nbytes=4, endian=.Platform$endian, #serialization (write,read)
 	verbose=FALSE, parll=TRUE)
@@ -115,26 +145,39 @@ claws = function(getSeries, K1, K2,
 	{
 		stop("'getSeries': [big]matrix, function, file or valid connection (no NA)")
 	}
-	K1 = .toInteger(K1, function(x) x>=2)
-	K2 = .toInteger(K2, function(x) x>=2)
-	if (!is.logical(random))
-		stop("'random': logical")
-	tryCatch(
-		{ignored <- wavelets::wt.filter(wf)},
-		error = function(e) stop("Invalid wavelet filter; see ?wavelets::wt.filter"))
+	K1 <- .toInteger(K1, function(x) x>=2)
+	K2 <- .toInteger(K2, function(x) x>=2)
+	if (!is.numeric(nb_per_chunk) || length(nb_per_chunk)!=2)
+		stop("'nb_per_chunk': numeric, size 2")
+	nb_per_chunk[1] <- .toInteger(nb_per_chunk[1], function(x) x>=1)
+	# A batch of contributions should have at least as many elements as a batch of series,
+	# because it always contains much less values
+	nb_per_chunk[2] <- max(.toInteger(nb_per_chunk[2],function(x) x>=1), nb_per_chunk[1])
+	nb_items_clust1 <- .toInteger(nb_items_clust1, function(x) x>K1)
+	random <- .toLogical(random)
+	tryCatch
+	(
+		{ignored <- wavelets::wt.filter(wav_filt)},
+		error = function(e) stop("Invalid wavelet filter; see ?wavelets::wt.filter")
+	)
+	ctypes = c("relative","absolute","logit")
+	contrib_type = ctypes[ pmatch(contrib_type,ctypes) ]
+	if (is.na(contrib_type))
+		stop("'contrib_type' in {'relative','absolute','logit'}")
 	if (WER!="end" && WER!="mix")
-		stop("WER takes values in {'end','mix'}")
-	ntasks = .toInteger(ntasks, function(x) x>=1)
-	ncores_tasks = .toInteger(ncores_tasks, function(x) x>=1)
-	ncores_clust = .toInteger(ncores_clust, function(x) x>=1)
-	nb_series_per_chunk = .toInteger(nb_series_per_chunk, function(x) x>=K1)
-	min_series_per_chunk = .toInteger(K1, function(x) x>=K1 && x<=nb_series_per_chunk)
+		stop("'WER': in {'end','mix'}")
+	random <- .toLogical(random)
+	ntasks <- .toInteger(ntasks, function(x) x>=1)
+	ncores_tasks <- .toInteger(ncores_tasks, function(x) x>=1)
+	ncores_clust <- .toInteger(ncores_clust, function(x) x>=1)
 	if (!is.character(sep))
 		stop("'sep': character")
-	nbytes = .toInteger(nbytes, function(x) x==4 || x==8)
+	nbytes <- .toInteger(nbytes, function(x) x==4 || x==8)
+	verbose <- .toLogical(verbose)
+	parll <- .toLogical(parll)
 
 	# Serialize series if required, to always use a function
-	bin_dir = ".epclust_bin/"
+	bin_dir <- ".epclust_bin/"
 	dir.create(bin_dir, showWarnings=FALSE, mode="0755")
 	if (!is.function(getSeries))
 	{
@@ -156,11 +199,11 @@ claws = function(getSeries, K1, K2,
 		contribs_file, nb_series_per_chunk, nbytes, endian)
 	getContribs = function(indices) getDataInFile(indices, contribs_file, nbytes, endian)
 
-	if (nb_curves < min_series_per_chunk)
-		stop("Not enough data: less rows than min_series_per_chunk!")
+	if (nb_curves < K2)
+		stop("Not enough data: less series than final number of clusters")
 	nb_series_per_task = round(nb_curves / ntasks)
-	if (nb_series_per_task < min_series_per_chunk)
-		stop("Too many tasks: less series in one task than min_series_per_chunk!")
+	if (nb_series_per_task < K2)
+		stop("Too many tasks: less series in one task than final number of clusters")
 
 	runTwoStepClustering = function(inds)
 	{
@@ -170,7 +213,8 @@ claws = function(getSeries, K1, K2,
 			inds, getContribs, K1, nb_series_per_chunk, ncores_clust, verbose, parll)
 		if (WER=="mix")
 		{
-			require("bigmemory", quietly=TRUE)
+			if (parll && ntasks>1)
+				require("bigmemory", quietly=TRUE)
 			medoids1 = bigmemory::as.big.matrix( getSeries(indices_medoids) )
 			medoids2 = clusteringTask2(medoids1, K2, getSeries, nb_curves, nb_series_per_chunk,
 				nbytes, endian, ncores_clust, verbose, parll)
@@ -197,7 +241,7 @@ claws = function(getSeries, K1, K2,
 		{synchrones_file = paste(bin_dir,"synchrones",sep="") ; unlink(synchrones_file)}
 	if (parll && ntasks>1)
 	{
-		cl = parallel::makeCluster(ncores_tasks)
+		cl = parallel::makeCluster(ncores_tasks, outfile="")
 		varlist = c("getSeries","getContribs","K1","K2","verbose","parll",
 			"nb_series_per_chunk","ntasks","ncores_clust","sep","nbytes","endian")
 		if (WER=="mix")
@@ -206,10 +250,11 @@ claws = function(getSeries, K1, K2,
 	}
 
 	# 1000*K1 indices [if WER=="end"], or empty vector [if WER=="mix"] --> series on file
-	if (parll && ntasks>1)
-		indices = unlist( parallel::parLapply(cl, indices_tasks, runTwoStepClustering) )
-	else
-		indices = unlist( lapply(indices_tasks, runTwoStepClustering) )
+	indices <-
+		if (parll && ntasks>1)
+			unlist( parallel::parLapply(cl, indices_tasks, runTwoStepClustering) )
+		else
+			unlist( lapply(indices_tasks, runTwoStepClustering) )
 	if (parll && ntasks>1)
 		parallel::stopCluster(cl)
 
@@ -241,7 +286,7 @@ claws = function(getSeries, K1, K2,
 	# Cleanup
 	unlink(bin_dir, recursive=TRUE)
 
-	medoids2
+	medoids2[,]
 }
 
 #' curvesToContribs
@@ -249,36 +294,52 @@ claws = function(getSeries, K1, K2,
 #' Compute the discrete wavelet coefficients for each series, and aggregate them in
 #' energy contribution across scales as described in https://arxiv.org/abs/1101.4744v2
 #'
-#' @param series Matrix of series (in rows), of size n x L
+#' @param series [big.]matrix of series (in columns), of size L x n
 #' @inheritParams claws
 #'
-#' @return A matrix of size n x log(L) containing contributions in rows
+#' @return A [big.]matrix of size log(L) x n containing contributions in columns
 #'
 #' @export
-curvesToContribs = function(series, wf, ctype)
+curvesToContribs = function(series, wav_filt, contrib_type)
 {
-	L = length(series[1,])
+	L = nrow(series)
 	D = ceiling( log2(L) )
 	nb_sample_points = 2^D
-	cont_types = c("relative","absolute")
-	ctype = cont_types[ pmatch(ctype,cont_types) ]
-	t( apply(series, 1, function(x) {
+	apply(series, 2, function(x) {
 		interpolated_curve = spline(1:L, x, n=nb_sample_points)$y
 		W = wavelets::dwt(interpolated_curve, filter=wf, D)@W
 		nrj = rev( sapply( W, function(v) ( sqrt( sum(v^2) ) ) ) )
-		if (ctype=="relative") nrj / sum(nrj) else nrj
-	}) )
+		if (contrib_type!="absolute")
+			nrj = nrj / sum(nrj)
+		if (contrib_type=="logit")
+			nrj = - log(1 - nrj)
+		nrj
+	})
 }
 
 # Check integer arguments with functional conditions
 .toInteger <- function(x, condition)
 {
+	errWarn <- function(ignored)
+		paste("Cannot convert argument' ",substitute(x),"' to integer", sep="")
 	if (!is.integer(x))
-		tryCatch(
-			{x = as.integer(x)[1]},
-			error = function(e) paste("Cannot convert argument",substitute(x),"to integer")
-		)
+		tryCatch({x = as.integer(x)[1]; if (is.na(x)) stop()},
+			warning = errWarn, error = errWarn)
 	if (!condition(x))
-		stop(paste("Argument",substitute(x),"does not verify condition",body(condition)))
+	{
+		stop(paste("Argument '",substitute(x),
+			"' does not verify condition ",body(condition), sep=""))
+	}
+	x
+}
+
+# Check logical arguments
+.toLogical <- function(x)
+{
+	errWarn <- function(ignored)
+		paste("Cannot convert argument' ",substitute(x),"' to logical", sep="")
+	if (!is.logical(x))
+		tryCatch({x = as.logical(x)[1]; if (is.na(x)) stop()},
+			warning = errWarn, error = errWarn)
 	x
 }
-- 
2.44.0