X-Git-Url: https://git.auder.net/?p=epclust.git;a=blobdiff_plain;f=epclust%2FR%2Fmain.R;h=0b598329b87935a3ea668f8953e6967f8bd9ea5f;hp=f5ad81a970e19611b3fcb45bd9b78a6963b509a1;hb=e205f2187f0ccdff00bffc47642392ec5e33214d;hpb=23844f603a363475e85ed1cb4b8620f139bcffdf

diff --git a/epclust/R/main.R b/epclust/R/main.R
index f5ad81a..0b59832 100644
--- a/epclust/R/main.R
+++ b/epclust/R/main.R
@@ -22,6 +22,7 @@
 #' @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 random Randomize chunks repartition
+#' @param ... Other arguments to be passed to \code{data} function
 #'
 #' @return A data.frame of the final medoids curves (identifiers + values)
 #'
@@ -33,29 +34,45 @@
 #'     "LIMIT ", n, " ORDER BY date", sep=""))
 #'   return (df)
 #' }
+#' #####TODO: if DB, array rank --> ID at first retrieval, when computing coeffs; so:: NO use of IDs !
 #'   #TODO: 3 examples, data.frame / binary file / DB sqLite
 #'   + sampleCurves : wavBootstrap de package wmtsa
 #' cl = epclust(getData, K1=200, K2=15, ntasks=1000, nb_series_per_chunk=5000, WER="mix")
 #' @export
-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)
+epclust = function(series,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,...)
 {
-	# Check arguments
-	if (!is.data.frame(data) && !is.function(data))
+	# Check/transform arguments
+	bin_dir = "epclust.bin/"
+	dir.create(bin_dir, showWarnings=FALSE, mode="0755")
+	if (!is.function(series))
+	{
+		series_file = paste(bin_dir,"data",sep="")
+		unlink(series_file)
+	}
+	if (is.matrix(series))
+		serialize(series, series_file)
+	else if (!is.function(series))
 	{
 		tryCatch(
 			{
-				if (is.character(data))
-					data_con = file(data, open="r")
-				else if (!isOpen(data))
+				if (is.character(series))
+					series_con = file(series, open="r")
+				else if (!isOpen(series))
 				{
-					open(data)
-					data_con = data
+					open(series)
+					series_con = series
 				}
+				serialize(series_con, series_file)
+				close(series_con)
 			},
-			error=function(e) "data should be a data.frame, a function or a valid connection"
+			error=function(e) "series should be a data.frame, a function or a valid connection"
 		)
 	}
+	if (!is.function(series))
+		series = function(indices) getDataInFile(indices, series_file)
+	getSeries = series
+
 	K1 = toInteger(K1, function(x) x>=2)
 	K2 = toInteger(K2, function(x) x>=2)
 	ntasks = toInteger(ntasks, function(x) x>=1)
@@ -67,22 +84,21 @@ 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"
+	coefs_file = paste(bin_dir,"coefs",sep="")
+	unlink(coefs_file)
 	index = 1
 	nb_curves = 0
-	nb_coeffs = NA
 	repeat
 	{
-		coeffs_chunk = computeCoeffs(data, index, nb_series_per_chunk, wf)
-		if (is.null(coeffs_chunk))
+		series = getSeries((index-1)+seq_len(nb_series_per_chunk))
+		if (is.null(series))
 			break
-		serialize(coeffs_chunk, coeffs_file, append=TRUE)
+		coeffs_chunk = curvesToCoeffs(series, wf)
+		serialize(coeffs_chunk, coefs_file)
 		index = index + nb_series_per_chunk
 		nb_curves = nb_curves + nrow(coeffs_chunk)
-		if (is.na(nb_coeffs))
-			nb_coeffs = ncol(coeffs_chunk)-1
 	}
+	getCoefs = function(indices) getDataInFile(indices, coefs_file)
 
 	if (nb_curves < min_series_per_chunk)
 		stop("Not enough data: less rows than min_series_per_chunk!")
@@ -91,22 +107,41 @@ 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]
-	}
-	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::stopCluster(cl_tasks)
+		indices[((i-1)*nb_series_per_task+1):upper_bound]
+	})
+	cl = parallel::makeCluster(ncores_tasks)
+	#1000*K1 (or K2) indices (or NOTHING--> series on file)
+	indices = unlist( parallel::parLapply(cl, indices_tasks, function(inds) {
+		clusteringTask(inds, getSeries, getSeries, getCoefs, K1, K2*(WER=="mix"),
+			nb_series_per_chunk,ncores_clust,to_file=TRUE)
+	}) )
+	parallel::stopCluster(cl)
 
-##TODO: passer data ?!
+	getSeriesForSynchrones = getSeries
+	synchrones_file = paste(bin_dir,"synchrones",sep="")
+	if (WER=="mix")
+	{
+		indices = seq_len(ntasks*K2)
+		#Now series must be retrieved from synchrones_file
+		getSeries = function(inds) getDataInFile(inds, synchrones_file)
+		#Coefs must be re-computed
+		unlink(coefs_file)
+		index = 1
+		repeat
+		{
+			series = getSeries((index-1)+seq_len(nb_series_per_chunk))
+			if (is.null(series))
+				break
+			coeffs_chunk = curvesToCoeffs(series, wf)
+			serialize(coeffs_chunk, coefs_file)
+			index = index + nb_series_per_chunk
+		}
+	}
 
-	# Run step1+2 step on resulting ranks
-	ranks = clusteringStep12()
-	return (list("ranks"=ranks, "medoids"=getSeries(data, ranks)))
+	# Run step2 on resulting indices or series (from file)
+	clusteringTask(indices, getSeries, getSeriesForSynchrones, getCoefs, K1, K2,
+		nb_series_per_chunk, ncores_tasks*ncores_clust, to_file=FALSE)
 }