X-Git-Url: https://git.auder.net/?p=epclust.git;a=blobdiff_plain;f=epclust%2FR%2Fmain.R;h=1347fae27a623a43cf9db53a74580c85690f5313;hp=5e47f192e9bb95373f4b8ac92428d7e13d5872e9;hb=4efef8ccd1522278f53aa5ce265f3a6cfb6fbd9f;hpb=31daec0264f0ebf5e97a62c82af5e884d7dc1c43

diff --git a/epclust/R/main.R b/epclust/R/main.R
index 5e47f19..1347fae 100644
--- a/epclust/R/main.R
+++ b/epclust/R/main.R
@@ -32,20 +32,74 @@ NULL
 #' @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
 #'
-#' @return A matrix of the final medoids curves
+#' @return A matrix of the final medoids curves (K2) in rows
 #'
 #' @examples
-#' getData = function(start, n) {
-#'   con = dbConnect(drv = RSQLite::SQLite(), dbname = "mydata.sqlite")
-#'   df = dbGetQuery(con, paste(
-#'     "SELECT * FROM times_values GROUP BY id OFFSET ",start,
+#' \dontrun{
+#' # WER distances computations are a bit 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)),
+#'   byrows=TRUE, ncol=L )
+#' library(wmtsa)
+#' series = do.call( rbind, lapply( 1:6, function(i)
+#'   do.call(rbind, wmtsa::wavBootstrap(ref_series[i,], n.realization=400)) ) )
+#' #dim(series) #c(2400,10001)
+#' medoids_ascii = claws(series_RData, K1=60, K2=6, wf="d8", nb_series_per_chunk=500)
+#'
+#' # 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, wf="d8", nb_series_per_chunk=500)
+#'
+#' # Same example, from binary file
+#' bin_file = "/tmp/epclust_series.bin"
+#' nbytes = 8
+#' endian = "little"
+#' epclust::serialize(csv_file, bin_file, 500, nbytes, endian)
+#' getSeries = function(indices) getDataInFile(indices, bin_file, nbytes, endian)
+#' medoids_bin = claws(getSeries, K1=60, K2=6, wf="d8", nb_series_per_chunk=500)
+#' unlink(csv_file)
+#' unlink(bin_file)
+#'
+#' # Same example, from SQLite database
+#' library(DBI)
+#' series_db <- dbConnect(RSQLite::SQLite(), "file::memory:")
+#' # Prepare data.frame in DB-format
+#' n = nrow(series)
+#' formatted_series = data.frame(
+#'   ID = rep(1:n,each=L),
+#'   time = as.POSIXct(1800*(0:n),"GMT",origin="2001-01-01"),
+#'   value
+
+
+
+
+#' TODO
+
+
+#' times_values = as.data.frame(series)
+#' dbWriteTable(series_db, "times_values", times_values)
+#' # NOTE: assume that DB internal data is not reorganized when computing coefficients
+#' indexToID_inDB <<- list()
+#' getSeries = function(indices) {
+#'   con = dbConnect(drv = RSQLite::SQLite(), dbname = db_file)
+#'   if (indices %in% indexToID_inDB)
+#'   {
+#'     df = dbGetQuery(con, paste(
+#'     "SELECT value FROM times_values GROUP BY id OFFSET ",start,
 #'     "LIMIT ", n, " ORDER BY date", sep=""))
-#'   return (df)
+#'     return (df)
+#'   }
+#'   else
+#'   {
+#'     ...
+#'   }
+#' }
+#' dbDisconnect(mydb)
 #' }
-#' #####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
 claws = function(getSeries, K1, K2,
 	random=TRUE, #randomize series order?
@@ -121,6 +175,7 @@ claws = function(getSeries, K1, K2,
 	cl = parallel::makeCluster(ncores_tasks)
 	# 1000*K1 indices [if WER=="end"], or empty vector [if WER=="mix"] --> series on file
 	indices = unlist( parallel::parLapply(cl, indices_tasks, function(inds) {
+		require("epclust", quietly=TRUE)
 		indices_medoids = clusteringTask(inds,getCoefs,K1,nb_series_per_chunk,ncores_clust)
 		if (WER=="mix")
 		{