#' @param ncores_tasks Number of parallel tasks ('1' == sequential tasks)
#' @param ncores_clust Number of parallel clusterings in one task
#' @param sep Separator in CSV input file (if any provided)
-#' @param nbytes Number of bytes to serialize a floating-point number: 4 or 8
+#' @param nbytes 4 or 8 bytes to (de)serialize a floating-point number
#' @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{
#' @examples
#' \dontrun{
#' # WER distances computations are too long for CRAN (for now)
+#' # Note: on this small example, sequential run is faster
#'
#' # Random series around cos(x,2x,3x)/sin(x,2x,3x)
#' x <- seq(0,50,0.05)
#' 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, random=FALSE, verbose=TRUE)
+#' res_ascii <- claws(series, K1=30, K2=6, nb_series_per_chunk=500,
+#' nb_items_clust=100, random=FALSE, verbose=TRUE, ncores_clust=1)
#'
#' # 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, random=FALSE)
+#' res_csv <- claws(csv_file, 30, 6, 500, 100, random=FALSE, ncores_clust=1)
#'
#' # Same example, from binary file
#' bin_file <- tempfile(pattern="epclust_series.bin_")
#' endian <- "little"
#' 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, random=FALSE)
+#' res_bin <- claws(getSeries, 30, 6, 500, 100, random=FALSE, ncores_clust=1)
#' unlink(csv_file)
#' unlink(bin_file)
#'
#' serie_length <- as.integer( dbGetQuery(series_db,
#' paste("SELECT COUNT(*) FROM times_values WHERE id == ",indexToID_inDB[1],sep="")) )
#' getSeries <- function(indices) {
+#' indices = indices[ indices <= length(indexToID_inDB) ]
+#' if (length(indices) == 0)
+#' return (NULL)
#' request <- "SELECT id,value FROM times_values WHERE id in ("
#' for (i in seq_along(indices)) {
#' request <- paste(request, indexToID_inDB[ indices[i] ], sep="")
#' }
#' request <- paste(request, ")", sep="")
#' df_series <- dbGetQuery(series_db, request)
-#' if (nrow(df_series) >= 1)
-#' matrix(df_series[,"value"], nrow=serie_length)
-#' else
-#' NULL
+#' matrix(df_series[,"value"], nrow=serie_length)
#' }
-#' # reuse_bin==FALSE: DB do not garantee ordering
-#' res_db <- claws(getSeries, K1=30, K2=6, 100, random=FALSE, reuse_bin=FALSE)
+#' res_db <- claws(getSeries, 30, 6, 500, 100, random=FALSE, ncores_clust=1)
#' dbDisconnect(series_db)
#'
#' # All results should be equal:
#' & res_ascii$ranks == res_db$ranks)
#' }
#' @export
-claws <- function(series, K1, K2, nb_series_per_chunk, nb_items_clust=7*K1,
+claws <- function(series, K1, K2, nb_series_per_chunk, nb_items_clust=5*K1,
algoClust1=function(data,K) cluster::pam(t(data),K,diss=FALSE,pamonce=1)$id.med,
algoClust2=function(dists,K) cluster::pam(dists,K,diss=TRUE,pamonce=1)$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$endian, verbose=FALSE, parll=TRUE, reuse_bin=TRUE)
+ endian=.Platform$endian, verbose=FALSE)
{
-
-
-#TODO: comprendre differences.......... debuguer getSeries for DB
-
-
# Check/transform arguments
if (!is.matrix(series) && !bigmemory::is.big.matrix(series)
&& !is.function(series)
stop("'sep': character")
nbytes <- .toInteger(nbytes, function(x) x==4 || x==8)
verbose <- .toLogical(verbose)
- parll <- .toLogical(parll)
# Binarize series if it is not a function; the aim is to always use a function,
# to uniformize treatments. An equally good alternative would be to use a file-backed
if (verbose)
cat("...Serialize time-series (or retrieve past binary file)\n")
series_file <- ".series.epclust.bin"
- if (!file.exists(series_file) || !reuse_bin)
- {
- unlink(series_file,)
+ if (!file.exists(series_file))
binarize(series, series_file, nb_series_per_chunk, sep, nbytes, endian)
- }
getSeries <- function(inds) getDataInFile(inds, series_file, nbytes, endian)
}
else
contribs_file <- ".contribs.epclust.bin"
if (verbose)
cat("...Compute contributions and serialize them (or retrieve past binary file)\n")
- if (!file.exists(contribs_file) || !reuse_bin)
+ if (!file.exists(contribs_file))
{
- unlink(contribs_file,)
nb_curves <- binarizeTransform(getSeries,
function(curves) curvesToContribs(curves, wav_filt, contrib_type),
contribs_file, nb_series_per_chunk, nbytes, endian)
indices_all[((i-1)*nb_series_per_task+1):upper_bound]
})
+ parll <- (ncores_tasks > 1)
if (parll && ntasks>1)
{
# Initialize parallel runs: outfile="" allow to output verbose traces in the console
parallel::makeCluster(ncores_tasks, outfile="")
else
parallel::makeCluster(ncores_tasks)
- varlist <- c("ncores_clust","verbose","parll", #task 1 & 2
+ varlist <- c("ncores_clust","verbose", #task 1 & 2
"K1","getContribs","algoClust1","nb_items_clust") #task 1
if (WER=="mix")
{
if (parll && ntasks>1)
require("epclust", quietly=TRUE)
indices_medoids <- clusteringTask1(inds, getContribs, K1, algoClust1,
- nb_items_clust, ncores_clust, verbose, parll)
+ nb_items_clust, ncores_clust, verbose)
if (WER=="mix")
{
indices_medoids <- clusteringTask2(indices_medoids, getSeries, K2, algoClust2,
- nb_series_per_chunk,smooth_lvl,nvoice,nbytes,endian,ncores_clust,verbose,parll)
+ nb_series_per_chunk,smooth_lvl,nvoice,nbytes,endian,ncores_clust,verbose)
}
indices_medoids
}
}
# As explained above, we obtain after all runs ntasks*[K1 or K2] medoids indices,
- # depending wether WER=="end" or "mix", respectively.
+ # depending whether WER=="end" or "mix", respectively.
indices_medoids_all <-
if (parll && ntasks>1)
unlist( parallel::parLapply(cl, indices_tasks, runTwoStepClustering) )
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)
+ nb_items_clust, ncores_tasks*ncores_clust, verbose)
indices_medoids <- clusteringTask2(indices_medoids, getSeries, K2, algoClust2,
- nb_series_per_chunk,smooth_lvl,nvoice,nbytes,endian,ncores_last_stage,verbose,parll)
+ nb_series_per_chunk,smooth_lvl,nvoice,nbytes,endian,ncores_last_stage,verbose)
# Compute synchrones, that is to say the cumulated power consumptions for each of the K2
# final groups.
medoids <- getSeries(indices_medoids)
synchrones <- computeSynchrones(medoids, getSeries, nb_curves, nb_series_per_chunk,
- ncores_last_stage, verbose, parll)
+ ncores_last_stage, verbose)
# NOTE: no need to use big.matrix here, since there are only K2 << K1 << N remaining curves
list("medoids"=medoids, "ranks"=indices_medoids, "synchrones"=synchrones)