X-Git-Url: https://git.auder.net/?a=blobdiff_plain;f=epclust%2FR%2FcomputeWerDists.R;h=8eb755c9920d86f2934d15eb90ec560ba4f596e7;hb=dc86eb0c992e6e4ab119d48398d040c4cf3a75fd;hp=568a8262adbf2989c6472f5b271a7b299a0262c3;hpb=3fb6e823601002c44ffbf913e83c8d24cfa1e819;p=epclust.git

diff --git a/epclust/R/computeWerDists.R b/epclust/R/computeWerDists.R
index 568a826..8eb755c 100644
--- a/epclust/R/computeWerDists.R
+++ b/epclust/R/computeWerDists.R
@@ -4,14 +4,16 @@
 #' obtaind by \code{getSeries(indices)}
 #'
 #' @param indices Range of series indices to cluster
+#' @param getSeries Function to retrieve series (argument: 'indices', integer vector),
+#'   as columns of a matrix
+#' @param ncores Number of cores for parallel runs
 #' @inheritParams claws
-#' @inheritParams computeSynchrones
 #'
 #' @return A distances matrix of size K x K where K == length(indices)
 #'
 #' @export
 computeWerDists <- function(indices, getSeries, nb_series_per_chunk, smooth_lvl, nvoice,
-	nbytes, endian, ncores_clust=3, verbose=FALSE, parll=TRUE)
+	nbytes, endian, ncores=3, verbose=FALSE, parll=TRUE)
 {
 	n <- length(indices)
 	L <- length(getSeries(1)) #TODO: not very neat way to get L
@@ -27,12 +29,6 @@ computeWerDists <- function(indices, getSeries, nb_series_per_chunk, smooth_lvl,
 	# Compute the getSeries(indices) CWT, and store the results in the binary file
 	computeSaveCWT <- function(indices)
 	{
-		if (parll)
-		{
-			# parallel workers start with an empty environment
-			require("epclust", quietly=TRUE)
-		}
-
 		# Obtain CWT as big vectors of real part + imaginary part (concatenate)
 		ts_cwt <- sapply(indices, function(i) {
 			ts <- scale(ts(getSeries(i)), center=TRUE, scale=FALSE)
@@ -54,7 +50,7 @@ computeWerDists <- function(indices, getSeries, nb_series_per_chunk, smooth_lvl,
 		re_part + 1i * im_part
 	}
 
-	# Compute distance between columns i and j for j>i
+	# Compute distances between columns i and j for j>i
 	computeDistances <- function(i)
 	{
 		if (parll)
@@ -87,30 +83,30 @@ computeWerDists <- function(indices, getSeries, nb_series_per_chunk, smooth_lvl,
 		Xwer_dist[i,i] <- 0.
 	}
 
+	if (verbose)
+		cat(paste("--- Precompute and serialize synchrones CWT\n", sep=""))
+
+	# Split indices by packets of length at most nb_cwt_per_chunk
+	indices_cwt <- .splitIndices(seq_len(n), nb_cwt_per_chunk)
+	# NOTE: next loop could potentially be run in //. Indices would be permuted (by
+	# serialization order), and synchronicity would be required because of concurrent
+	# writes. Probably not worth the effort - but possible to gain some bits of speed.
+	for (inds in indices_cwt)
+		computeSaveCWT(inds)
+
 	if (parll)
 	{
 		# outfile=="" to see stderr/stdout on terminal
 		cl <-
 			if (verbose)
-				parallel::makeCluster(ncores_clust, outfile="")
+				parallel::makeCluster(ncores, outfile="")
 			else
-				parallel::makeCluster(ncores_clust)
+				parallel::makeCluster(ncores)
 		Xwer_dist_desc <- bigmemory::describe(Xwer_dist)
-		parallel::clusterExport(cl, varlist=c("parll","nb_cwt_per_chunk","n","L",
-			"Xwer_dist_desc","noctave","nvoice","getCWT"), envir=environment())
+		parallel::clusterExport(cl, envir=environment(),
+			varlist=c("parll","n","L","Xwer_dist_desc","getCWT","verbose"))
 	}
 
-	if (verbose)
-		cat(paste("--- Precompute and serialize synchrones CWT\n", sep=""))
-
-	# Split indices by packets of length at most nb_cwt_per_chunk
-	indices_cwt <- .splitIndices(seq_len(n), nb_cwt_per_chunk)
-	ignored <-
-		if (parll)
-			parallel::parLapply(cl, indices_cwt, computeSaveCWT)
-		else
-			lapply(indices_cwt, computeSaveCWT)
-
 	if (verbose)
 		cat(paste("--- Compute WER distances\n", sep=""))