From: Benjamin Auder <benjamin.auder@somewhere>
Date: Fri, 3 Mar 2017 14:29:08 +0000 (+0100)
Subject: export vars to nodes
X-Git-Url: https://git.auder.net/variants/%24%7Bvname%7D/current/mini-custom.min.css?a=commitdiff_plain;h=48108c3999d28d973443fa5e78f73a0a9f2bfc07;p=epclust.git

export vars to nodes
---

diff --git a/epclust/R/clustering.R b/epclust/R/clustering.R
index 42e894c..578b2f3 100644
--- a/epclust/R/clustering.R
+++ b/epclust/R/clustering.R
@@ -1,20 +1,19 @@
 # Cluster one full task (nb_curves / ntasks series)
-clusteringTask = function(K1, K2, WER, nb_series_per_chunk, indices_tasks, ncores_clust)
+clusteringTask = function(indices_clust)
 {
 	cl_clust = parallel::makeCluster(ncores_clust)
-	#parallel::clusterExport(cl=cl_clust, varlist=c("fonctions_du_package"), envir=environment())
-	indices_clust = indices_task[[i]]
+	parallel::clusterExport(cl_clust,
+		varlist=c("K1","K2","WER"),
+		envir=environment())
 	repeat
 	{
 		nb_workers = max( 1, round( length(indices_clust) / nb_series_per_chunk ) )
-		indices_workers = list()
-		for (i in 1:nb_workers)
-		{
+		indices_workers = lapply(seq_len(nb_workers), function(i) {
 			upper_bound = ifelse( i<nb_workers,
 				min(nb_series_per_chunk*i,length(indices_clust)), length(indices_clust) )
-			indices_workers[[i]] = indices_clust[(nb_series_per_chunk*(i-1)+1):upper_bound]
-		}
-		indices_clust = parallel::parLapply(cl, indices_workers, clusterChunk, K1, K2*(WER=="mix"))
+			indices_clust[(nb_series_per_chunk*(i-1)+1):upper_bound]
+		})
+		indices_clust = parallel::parLapply(cl, indices_workers, clusterChunk)
 		# TODO: soft condition between K2 and K1, before applying final WER step
 		if ((WER=="end" && length(indices_clust)==K1) || (WER=="mix" && length(indices_clust)==K2))
 			break
@@ -24,17 +23,17 @@ clusteringTask = function(K1, K2, WER, nb_series_per_chunk, indices_tasks, ncore
 }
 
 # Cluster a chunk of series inside one task (~max nb_series_per_chunk)
-clusterChunk = function(indices, K1, K2)
+clusterChunk = function(indices_chunk)
 {
-	coeffs = getCoeffs(indices)
+	coeffs = readCoeffs(indices_chunk)
 	cl = computeClusters(as.matrix(coeffs[,2:ncol(coeffs)]), K1, diss=FALSE)
-	if (K2 > 0)
+	if (WER=="mix" > 0)
 	{
 		curves = computeSynchrones(cl)
 		dists = computeWerDists(curves)
 		cl = computeClusters(dists, K2, diss=TRUE)
 	}
-	indices[cl]
+	indices_chunk[cl]
 }
 
 # Apply the clustering algorithm (PAM) on a coeffs or distances matrix
diff --git a/epclust/R/main.R b/epclust/R/main.R
index f5ad81a..75041a4 100644
--- a/epclust/R/main.R
+++ b/epclust/R/main.R
@@ -67,8 +67,7 @@ epclust = function(data, K1, K2, ntasks=1, nb_series_per_chunk=50*K1, min_series
 		stop("WER takes values in {'end','mix'}")
 
 	# Serialize all wavelets coefficients (+ IDs) onto a file
-	coeffs_file = ".coeffs"
-	ids_files = ".ids"
+	unlink(".coeffs")
 	index = 1
 	nb_curves = 0
 	nb_coeffs = NA
@@ -77,7 +76,7 @@ epclust = function(data, K1, K2, ntasks=1, nb_series_per_chunk=50*K1, min_series
 		coeffs_chunk = computeCoeffs(data, index, nb_series_per_chunk, wf)
 		if (is.null(coeffs_chunk))
 			break
-		serialize(coeffs_chunk, coeffs_file, append=TRUE)
+		writeCoeffs(coeffs_chunk)
 		index = index + nb_series_per_chunk
 		nb_curves = nb_curves + nrow(coeffs_chunk)
 		if (is.na(nb_coeffs))
@@ -91,22 +90,21 @@ epclust = function(data, K1, K2, ntasks=1, nb_series_per_chunk=50*K1, min_series
 		stop("Too many tasks: less series in one task than min_series_per_chunk!")
 
 	# Cluster coefficients in parallel (by nb_series_per_chunk)
-	indices = if (random) sample(nb_curves) else seq_len(nb_curves) #all indices
-	indices_tasks = list() #indices to be processed in each task
-	for (i in seq_len(ntasks))
-	{
+	indices = if (random) sample(nb_curves) else seq_len(nb_curves)
+	indices_tasks = lapply(seq_len(ntasks), function(i) {
 		upper_bound = ifelse( i<ntasks, min(nb_series_per_task*i,nb_curves), nb_curves )
-		indices_task[[i]] = indices[((i-1)*nb_series_per_task+1):upper_bound]
-	}
+		indices[((i-1)*nb_series_per_task+1):upper_bound]
+	})
 	library(parallel, quietly=TRUE)
 	cl_tasks = parallel::makeCluster(ncores_tasks)
-	#parallel::clusterExport(cl=cl_tasks, varlist=c("ncores_clust", ...), envir=environment())
-	indices = parallel::parLapply(cl_tasks, indices_tasks, clusteringStep12, )
+	parallel::clusterExport(cl_tasks,
+		varlist=c("K1","K2","WER","nb_series_per_chunk","ncores_clust"),#TODO: pass also
+						#nb_coeffs...and filename (in a list... ?)
+		envir=environment())
+	indices = parallel::parLapply(cl_tasks, indices_tasks, clusteringTask)
 	parallel::stopCluster(cl_tasks)
 
-##TODO: passer data ?!
-
 	# Run step1+2 step on resulting ranks
-	ranks = clusteringStep12()
-	return (list("ranks"=ranks, "medoids"=getSeries(data, ranks)))
+	indices = clusterChunk(indices, K1, K2)
+	return (list("indices"=indices, "medoids"=getSeries(data, indices)))
 }
diff --git a/epclust/R/utils.R b/epclust/R/utils.R
index 347c2c6..e0f25ec 100644
--- a/epclust/R/utils.R
+++ b/epclust/R/utils.R
@@ -10,8 +10,9 @@ toInteger <- function(x, condition)
 	x
 }
 
-serialize = function(coeffs, file, append)
+writeCoeffs = function(coeffs)
 {
+	file = ".coeffs"
 	#.........
 	#C function (from data.frame, type of IDs ??! force integers ? [yes])
 	#return raw vector
@@ -19,9 +20,10 @@ serialize = function(coeffs, file, append)
 #TODO: appendCoeffs() en C --> serialize et append to file
 }
 
-deserialize = function(file, range, ncoefs)
+readCoeffs = function(indices)
 {
 	#......
+	file = ".coeffs"
 	#C function (from file name)
 }
 
@@ -29,8 +31,3 @@ getSeries(data, rank=NULL, id=NULL)
 {
 	#TODO:
 }
-
-getCoeffs(.....) #FROM BINARY FILE !!!
-{
-
-}