#' @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{
#' 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)
#'
#' 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,
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)
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
# 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)
# 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