#' @param contrib_type Type of contribution: "relative", "logit" or "absolute" (any prefix)
#' @param WER "end" to apply stage 2 after stage 1 has fully iterated, or "mix" to apply
#' stage 2 at the end of each task
+#' @param smooth_lvl Smoothing level: odd integer, 1 == no smoothing. 3 seems good
#' @param nvoice Number of voices within each octave for CWT computations
#' @param random TRUE (default) for random chunks repartition
#' @param ntasks Number of tasks (parallel iterations to obtain K1 [if WER=="end"]
#' # WER distances computations are 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)), ncol=6 )
+#' 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)), ncol=6 )
#' library(wmtsa)
-#' series = do.call( cbind, lapply( 1:6, function(i)
+#' series <- do.call( cbind, lapply( 1:6, function(i)
#' do.call(cbind, wmtsa::wavBootstrap(ref_series[,i], n.realization=400)) ) )
#' #dim(series) #c(2400,10001)
-#' res_ascii = claws(series, K1=60, K2=6, 200, verbose=TRUE)
+#' res_ascii <- claws(series, K1=60, K2=6, 200, verbose=TRUE)
#'
#' # Same example, from CSV file
-#' csv_file = "/tmp/epclust_series.csv"
+#' csv_file <- "/tmp/epclust_series.csv"
#' write.table(series, csv_file, sep=",", row.names=FALSE, col.names=FALSE)
-#' res_csv = claws(csv_file, K1=60, K2=6, 200)
+#' res_csv <- claws(csv_file, K1=60, K2=6, 200)
#'
#' # Same example, from binary file
#' bin_file <- "/tmp/epclust_series.bin"
#' # 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)) )
+#' id <- rep(1:n,each=L),
+#' time <- rep( as.POSIXct(1800*(0:n),"GMT",origin="2001-01-01"), L ),
+#' value <- as.double(t(series)) )
#' dbWriteTable(series_db, "times_values", times_values)
#' # Fill associative array, map index to identifier
#' indexToID_inDB <- as.character(
#' else
#' NULL
#' }
-#' res_db = claws(getSeries, K1=60, K2=6, 200))
+#' res_db <- claws(getSeries, K1=60, K2=6, 200))
#' dbDisconnect(series_db)
#'
#' # All results should be the same:
claws <- function(series, K1, K2, nb_series_per_chunk, nb_items_clust=7*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", nvoice=4, random=TRUE,
- ntasks=1, ncores_tasks=1, ncores_clust=4, sep=",", nbytes=4,
+ 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$endian, verbose=FALSE, parll=TRUE)
{
# Check/transform arguments
nb_series_per_chunk <- .toInteger(nb_series_per_chunk, function(x) x>=1)
nb_items_clust <- .toInteger(nb_items_clust, function(x) x>K1)
random <- .toLogical(random)
- tryCatch( {ignored <- wavelets::wt.filter(wav_filt)},
- error = function(e) stop("Invalid wavelet filter; see ?wavelets::wt.filter") )
- ctypes = c("relative","absolute","logit")
- contrib_type = ctypes[ pmatch(contrib_type,ctypes) ]
+ tryCatch({ignored <- wavelets::wt.filter(wav_filt)},
+ error=function(e) stop("Invalid wavelet filter; see ?wavelets::wt.filter") )
+ ctypes <- c("relative","absolute","logit")
+ contrib_type <- ctypes[ pmatch(contrib_type,ctypes) ]
if (is.na(contrib_type))
stop("'contrib_type' in {'relative','absolute','logit'}")
if (WER!="end" && WER!="mix")
{
if (verbose)
cat("...Serialize time-series (or retrieve past binary file)\n")
- series_file = ".series.epclust.bin"
+ series_file <- ".series.epclust.bin"
if (!file.exists(series_file))
binarize(series, series_file, nb_series_per_chunk, sep, nbytes, endian)
- getSeries = function(inds) getDataInFile(inds, series_file, nbytes, endian)
+ getSeries <- function(inds) getDataInFile(inds, series_file, nbytes, endian)
}
else
- getSeries = series
+ getSeries <- series
# Serialize all computed wavelets contributions into a file
- contribs_file = ".contribs.epclust.bin"
- index = 1
- nb_curves = 0
+ contribs_file <- ".contribs.epclust.bin"
+ index <- 1
+ nb_curves <- 0
if (verbose)
cat("...Compute contributions and serialize them (or retrieve past binary file)\n")
if (!file.exists(contribs_file))
{
- nb_curves = binarizeTransform(getSeries,
+ nb_curves <- binarizeTransform(getSeries,
function(curves) curvesToContribs(curves, wav_filt, contrib_type),
contribs_file, nb_series_per_chunk, nbytes, endian)
}
else
{
# TODO: duplicate from getDataInFile() in de_serialize.R
- contribs_size = file.info(contribs_file)$size #number of bytes in the file
- contrib_length = readBin(contribs_file, "integer", n=1, size=8, endian=endian)
- nb_curves = (contribs_size-8) / (nbytes*contrib_length)
+ contribs_size <- file.info(contribs_file)$size #number of bytes in the file
+ contrib_length <- readBin(contribs_file, "integer", n=1, size=8, endian=endian)
+ nb_curves <- (contribs_size-8) / (nbytes*contrib_length)
}
- getContribs = function(indices) getDataInFile(indices, contribs_file, nbytes, endian)
+ getContribs <- function(indices) getDataInFile(indices, contribs_file, nbytes, endian)
# A few sanity checks: do not continue if too few data available.
if (nb_curves < K2)
stop("Not enough data: less series than final number of clusters")
- nb_series_per_task = round(nb_curves / ntasks)
+ nb_series_per_task <- round(nb_curves / ntasks)
if (nb_series_per_task < K2)
stop("Too many tasks: less series in one task than final number of clusters")
# Generate a random permutation of 1:N (if random==TRUE);
# otherwise just use arrival (storage) order.
- indices_all = if (random) sample(nb_curves) else seq_len(nb_curves)
+ indices_all <- if (random) sample(nb_curves) else seq_len(nb_curves)
# Split (all) indices into ntasks groups of ~same size
- indices_tasks = lapply(seq_len(ntasks), function(i) {
- upper_bound = ifelse( i<ntasks, min(nb_series_per_task*i,nb_curves), 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_all[((i-1)*nb_series_per_task+1):upper_bound]
})
{
# Initialize parallel runs: outfile="" allow to output verbose traces in the console
# under Linux. All necessary variables are passed to the workers.
- cl = parallel::makeCluster(ncores_tasks, outfile="")
- varlist = c("ncores_clust","verbose","parll", #task 1 & 2
+ cl <- parallel::makeCluster(ncores_tasks, outfile="")
+ varlist <- c("ncores_clust","verbose","parll", #task 1 & 2
"K1","getContribs","algoClust1","nb_items_clust") #task 1
if (WER=="mix")
{
# Add variables for task 2
- varlist = c(varlist, "K2","getSeries","algoClust2","nb_series_per_chunk",
- "nvoice","nbytes","endian")
+ varlist <- c(varlist, "K2","getSeries","algoClust2","nb_series_per_chunk",
+ "smooth_lvl","nvoice","nbytes","endian")
}
- parallel::clusterExport(cl, varlist, envir = environment())
+ parallel::clusterExport(cl, varlist, envir <- environment())
}
# This function achieves one complete clustering task, divided in stage 1 + stage 2.
# stage 1: n indices --> clusteringTask1(...) --> K1 medoids (indices)
# stage 2: K1 indices --> K1xK1 WER distances --> clusteringTask2(...) --> K2 medoids,
- # where n = N / ntasks, N being the total number of curves.
- runTwoStepClustering = function(inds)
+ # where n == N / ntasks, N being the total number of curves.
+ runTwoStepClustering <- function(inds)
{
# When running in parallel, the environment is blank: we need to load the required
# packages, and pass useful variables.
if (parll && ntasks>1)
require("epclust", quietly=TRUE)
- indices_medoids = clusteringTask1(inds, getContribs, K1, algoClust1,
+ indices_medoids <- clusteringTask1(inds, getContribs, K1, algoClust1,
nb_items_clust, ncores_clust, verbose, parll)
if (WER=="mix")
{
- indices_medoids = clusteringTask2(indices_medoids, getSeries, K2, algoClust2,
- nb_series_per_chunk, nvoice, nbytes, endian, ncores_clust, verbose, parll)
+ indices_medoids <- clusteringTask2(indices_medoids, getSeries, K2, algoClust2,
+ nb_series_per_chunk,smooth_lvl,nvoice,nbytes,endian,ncores_clust,verbose,parll)
}
indices_medoids
}
if (verbose)
{
- message = paste("...Run ",ntasks," x stage 1", sep="")
+ message <- paste("...Run ",ntasks," x stage 1", sep="")
if (WER=="mix")
- message = paste(message," + stage 2", sep="")
+ message <- paste(message," + stage 2", sep="")
cat(paste(message,"\n", sep=""))
}
# 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,
+ 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,
- nb_series_per_chunk, nvoice, nbytes, endian, ncores_last_stage, verbose, parll)
+ indices_medoids <- clusteringTask2(indices_medoids, getContribs, 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
# final groups.
- medoids = getSeries(indices_medoids)
- synchrones = computeSynchrones(medoids, getSeries, nb_curves, nb_series_per_chunk,
+ medoids <- getSeries(indices_medoids)
+ synchrones <- computeSynchrones(medoids, getSeries, nb_curves, nb_series_per_chunk,
ncores_last_stage, verbose, parll)
# NOTE: no need to use big.matrix here, since there are only K2 << K1 << N remaining curves
projects / epclust.git / blobdiff