X-Git-Url: https://git.auder.net/?a=blobdiff_plain;f=epclust%2FR%2Fmain.R;h=e3fa807d34d54b8837e1e5c6949d077f76cab216;hb=dc86eb0c992e6e4ab119d48398d040c4cf3a75fd;hp=6d3c842d43dac77c81ad4095f16f88036ce60199;hpb=3fb6e823601002c44ffbf913e83c8d24cfa1e819;p=epclust.git

diff --git a/epclust/R/main.R b/epclust/R/main.R
index 6d3c842..e3fa807 100644
--- a/epclust/R/main.R
+++ b/epclust/R/main.R
@@ -71,6 +71,7 @@
 #' @param endian Endianness for (de)serialization: "little" or "big"
 #' @param verbose FALSE: nothing printed; TRUE: some execution traces
 #' @param parll TRUE: run in parallel. FALSE: run sequentially
+#' @param reuse_bin Re-use previously stored binary series and contributions
 #'
 #' @return A list:
 #' \itemize{
@@ -95,21 +96,24 @@
 #' library(wmtsa)
 #' series <- do.call( cbind, lapply( 1:6, function(i)
 #'   do.call(cbind, wmtsa::wavBootstrap(ref_series[,i], n.realization=40)) ) )
+#' # Mix series so that all groups are evenly spread
+#' permut <- (0:239)%%6 * 40 + (0:239)%/%6 + 1
+#' series = series[,permut]
 #' #dim(series) #c(240,1001)
-#' res_ascii <- claws(series, K1=30, K2=6, 100, verbose=TRUE)
+#' res_ascii <- claws(series, K1=30, K2=6, 100, random=FALSE, verbose=TRUE)
 #'
 #' # Same example, from CSV file
 #' csv_file <- tempfile(pattern="epclust_series.csv_")
 #' write.table(t(series), csv_file, sep=",", row.names=FALSE, col.names=FALSE)
-#' res_csv <- claws(csv_file, K1=30, K2=6, 100)
+#' res_csv <- claws(csv_file, K1=30, K2=6, 100, random=FALSE)
 #'
 #' # Same example, from binary file
 #' bin_file <- tempfile(pattern="epclust_series.bin_")
 #' nbytes <- 8
 #' endian <- "little"
-#' binarize(csv_file, bin_file, 500, nbytes, endian)
+#' binarize(csv_file, bin_file, 500, ",", nbytes, endian)
 #' getSeries <- function(indices) getDataInFile(indices, bin_file, nbytes, endian)
-#' res_bin <- claws(getSeries, K1=30, K2=6, 100)
+#' res_bin <- claws(getSeries, K1=30, K2=6, 100, random=FALSE)
 #' unlink(csv_file)
 #' unlink(bin_file)
 #'
@@ -117,37 +121,39 @@
 #' library(DBI)
 #' series_db <- dbConnect(RSQLite::SQLite(), "file::memory:")
 #' # Prepare data.frame in DB-format
-#' n <- nrow(series)
-#' time_values <- data.frame(
-#'   id <- rep(1:n,each=L),
-#'   time <- rep( as.POSIXct(1800*(0:n),"GMT",origin="2001-01-01"), L ),
-#'   value <- as.double(t(series)) )
+#' n <- ncol(series)
+#' times_values <- data.frame(
+#'   id = rep(1:n,each=L),
+#'   time = rep( as.POSIXct(1800*(1:L),"GMT",origin="2001-01-01"), n ),
+#'   value = as.double(series) )
 #' dbWriteTable(series_db, "times_values", times_values)
 #' # Fill associative array, map index to identifier
 #' indexToID_inDB <- as.character(
-#'   dbGetQuery(series_db, 'SELECT DISTINCT id FROM time_values')[,"id"] )
+#'   dbGetQuery(series_db, 'SELECT DISTINCT id FROM times_values')[,"id"] )
 #' serie_length <- as.integer( dbGetQuery(series_db,
-#'   paste("SELECT COUNT * FROM time_values WHERE id == ",indexToID_inDB[1],sep="")) )
+#'   paste("SELECT COUNT(*) FROM times_values WHERE id == ",indexToID_inDB[1],sep="")) )
 #' getSeries <- function(indices) {
 #'   request <- "SELECT id,value FROM times_values WHERE id in ("
-#'   for (i in indices)
-#'     request <- paste(request, indexToID_inDB[i], ",", sep="")
+#'   for (i in seq_along(indices)) {
+#'     request <- paste(request, indexToID_inDB[ indices[i] ],  sep="")
+#'     if (i < length(indices))
+#'       request <- paste(request, ",", sep="")
+#'   }
 #'   request <- paste(request, ")", sep="")
 #'   df_series <- dbGetQuery(series_db, request)
-#'   if (length(df_series) >= 1)
-#'     as.matrix(df_series[,"value"], nrow=serie_length)
+#'   if (nrow(df_series) >= 1)
+#'     matrix(df_series[,"value"], nrow=serie_length)
 #'   else
 #'     NULL
 #' }
-#' res_db <- claws(getSeries, K1=30, K2=6, 100))
+#' # reuse_bin==FALSE: DB do not garantee ordering
+#' res_db <- claws(getSeries, K1=30, K2=6, 100, random=FALSE, reuse_bin=FALSE)
 #' dbDisconnect(series_db)
 #'
-#' # All results should be the same:
-#' library(digest)
-#' digest::sha1(res_ascii)
-#' digest::sha1(res_csv)
-#' digest::sha1(res_bin)
-#' digest::sha1(res_db)
+#' # All results should be equal:
+#' all(res_ascii$ranks == res_csv$ranks
+#'   & res_ascii$ranks == res_bin$ranks
+#'   & res_ascii$ranks == res_db$ranks)
 #' }
 #' @export
 claws <- function(series, K1, K2, nb_series_per_chunk, nb_items_clust=7*K1,
@@ -155,8 +161,13 @@ claws <- function(series, K1, K2, nb_series_per_chunk, nb_items_clust=7*K1,
 	algoClust2=function(dists,K) cluster::pam(dists,K,diss=TRUE,pamonce=1)$id.med,
 	wav_filt="d8", contrib_type="absolute", WER="end", smooth_lvl=3, nvoice=4,
 	random=TRUE, ntasks=1, ncores_tasks=1, ncores_clust=3, sep=",", nbytes=4,
-	endian=.Platform$endian, verbose=FALSE, parll=TRUE)
+	endian=.Platform$endian, verbose=FALSE, parll=TRUE, reuse_bin=TRUE)
 {
+
+
+#TODO: comprendre differences.......... debuguer getSeries for DB
+
+
 	# Check/transform arguments
 	if (!is.matrix(series) && !bigmemory::is.big.matrix(series)
 		&& !is.function(series)
@@ -195,8 +206,11 @@ claws <- function(series, K1, K2, nb_series_per_chunk, nb_items_clust=7*K1,
 		if (verbose)
 			cat("...Serialize time-series (or retrieve past binary file)\n")
 		series_file <- ".series.epclust.bin"
-		if (!file.exists(series_file))
+		if (!file.exists(series_file) || !reuse_bin)
+		{
+			unlink(series_file,)
 			binarize(series, series_file, nb_series_per_chunk, sep, nbytes, endian)
+		}
 		getSeries <- function(inds) getDataInFile(inds, series_file, nbytes, endian)
 	}
 	else
@@ -204,12 +218,11 @@ claws <- function(series, K1, K2, nb_series_per_chunk, nb_items_clust=7*K1,
 
 	# Serialize all computed wavelets contributions into a file
 	contribs_file <- ".contribs.epclust.bin"
-	index <- 1
-	nb_curves <- 0
 	if (verbose)
 		cat("...Compute contributions and serialize them (or retrieve past binary file)\n")
-	if (!file.exists(contribs_file))
+	if (!file.exists(contribs_file) || !reuse_bin)
 	{
+		unlink(contribs_file,)
 		nb_curves <- binarizeTransform(getSeries,
 			function(curves) curvesToContribs(curves, wav_filt, contrib_type),
 			contribs_file, nb_series_per_chunk, nbytes, endian)
@@ -305,18 +318,13 @@ claws <- function(series, K1, K2, nb_series_per_chunk, nb_items_clust=7*K1,
 	# it's better to just re-use ncores_clust
 	ncores_last_stage <- ncores_clust
 
-
-
-#TODO: here, save all inputs to clusteringTask2 and compare :: must have differences...
-
-
-
 	# Run last clustering tasks to obtain only K2 medoids indices
 	if (verbose)
 		cat("...Run final // stage 1 + stage 2\n")
 	indices_medoids <- clusteringTask1(indices_medoids_all, getContribs, K1, algoClust1,
 		nb_items_clust, ncores_tasks*ncores_clust, verbose, parll)
-	indices_medoids <- clusteringTask2(indices_medoids, getContribs, K2, algoClust2,
+
+	indices_medoids <- clusteringTask2(indices_medoids, getSeries, K2, algoClust2,
 		nb_series_per_chunk,smooth_lvl,nvoice,nbytes,endian,ncores_last_stage,verbose,parll)
 
 	# Compute synchrones, that is to say the cumulated power consumptions for each of the K2