X-Git-Url: https://git.auder.net/?p=epclust.git;a=blobdiff_plain;f=epclust%2FR%2Fmain.R;h=f45c9450dab724258047c4161439f3adafc83718;hp=e7943517587e46abbace81bb5b6397628e72328a;hb=7b13d0c28da62d91684a29ced50c740120e2b7a9;hpb=d300b49cd63d0539d29bbee120fa8237f7acee9b

diff --git a/epclust/R/main.R b/epclust/R/main.R
index e794351..f45c945 100644
--- a/epclust/R/main.R
+++ b/epclust/R/main.R
@@ -40,7 +40,7 @@
 epclust = function(data, K1, K2, ntasks=1, nb_series_per_chunk=50*K1, min_series_per_chunk=5*K1,
 	wf="haar", WER="end", ncores_tasks=1, ncores_clust=4, random=TRUE)
 {
-	#0) check arguments
+	# Check arguments
 	if (!is.data.frame(data) && !is.function(data))
 	{
 		tryCatch(
@@ -66,7 +66,7 @@ epclust = function(data, K1, K2, ntasks=1, nb_series_per_chunk=50*K1, min_series
 	if (WER!="end" && WER!="mix")
 		stop("WER takes values in {'end','mix'}")
 
-	#1) Serialize all wavelets coefficients (+ IDs) onto a file
+	# Serialize all wavelets coefficients (+ IDs) onto a file
 	coeffs_file = ".coeffs"
 	index = 1
 	nb_curves = 0
@@ -84,20 +84,15 @@ epclust = function(data, K1, K2, ntasks=1, nb_series_per_chunk=50*K1, min_series
 			nb_coeffs = ncol(coeffs_chunk)-1
 	}
 
-#	finalizeSerialization(coeffs_file) ........, nb_curves, )
-#TODO: is it really useful ?! we will always have these informations (nb_curves, nb_coeffs)
-
 	if (nb_curves < min_series_per_chunk)
 		stop("Not enough data: less rows than min_series_per_chunk!")
 	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!")
 
-	#2) Cluster coefficients in parallel (by nb_series_per_chunk)
-	# All indices, relative to complete dataset
-	indices = if (random) sample(nb_curves) else seq_len(nb_curves)
-	# Indices to be processed in each task
-	indices_tasks = list()
+	# 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))
 	{
 		upper_bound = ifelse( i<ntasks, min(nb_series_per_task*i,nb_curves), nb_curves )
@@ -105,12 +100,13 @@ epclust = function(data, K1, K2, ntasks=1, nb_series_per_chunk=50*K1, min_series
 	}
 	library(parallel, quietly=TRUE)
 	cl_tasks = parallel::makeCluster(ncores_tasks)
-	parallel::clusterExport(cl_tasks, ..........ncores_clust, indices_tasks, nb_series_per_chunk, processChunk, K1,
-									 K2, WER, )
-	ranks = parallel::parSapply(cl_tasks, seq_along(indices_tasks), oneIteration)
+	#parallel::clusterExport(cl=cl_tasks, varlist=c("ncores_clust", ...), envir=environment())
+	indices = parallel::parLapply(cl_tasks, indices_tasks, clusteringStep12, )
 	parallel::stopCluster(cl_tasks)
 
-	#3) Run step1+2 step on resulting ranks
-	ranks = oneIteration(.........)
+##TODO: passer data ?!
+
+	# Run step1+2 step on resulting ranks
+	ranks = clusteringStep12()
 	return (list("ranks"=ranks, "medoids"=getSeries(data, ranks)))
 }