-# Cluster one full task (nb_curves / ntasks series); only step 1
-clusteringTask = function(indices, getCoefs, K1, nb_series_per_chunk, ncores)
+#' @name clustering
+#' @rdname clustering
+#' @aliases clusteringTask computeClusters1 computeClusters2
+#'
+#' @title Two-stages clustering, withing one task (see \code{claws()})
+#'
+#' @description \code{clusteringTask()} runs one full task, which consists in iterated stage 1
+#' clustering (on nb_curves / ntasks energy contributions, computed through discrete
+#' wavelets coefficients). \code{computeClusters1()} and \code{computeClusters2()}
+#' correspond to the atomic clustering procedures respectively for stage 1 and 2.
+#' The former applies the clustering algorithm (PAM) on a contributions matrix, while
+#' the latter clusters a chunk of series inside one task (~max nb_series_per_chunk)
+#'
+#' @param indices Range of series indices to cluster in parallel (initial data)
+#' @param getContribs Function to retrieve contributions from initial series indices:
+#' \code{getContribs(indices)} outpus a contributions matrix
+#' @param contribs matrix of contributions (e.g. output of \code{curvesToContribs()})
+#' @inheritParams computeSynchrones
+#' @inheritParams claws
+#'
+#' @return For \code{clusteringTask()} and \code{computeClusters1()}, the indices of the
+#' computed (K1) medoids. Indices are irrelevant for stage 2 clustering, thus
+#' \code{computeClusters2()} outputs a matrix of medoids
+#' (of size limited by nb_series_per_chunk)
+NULL
+
+#' @rdname clustering
+#' @export
+clusteringTask = function(indices, getContribs, K1, nb_series_per_chunk, ncores_clust)
{
- cl = parallel::makeCluster(ncores)
- parallel::clusterExport(cl, varlist=c("getCoefs","K1"), envir=environment())
+
+#NOTE: comment out parallel sections for debugging
+#propagate verbose arg ?!
+
+# cl = parallel::makeCluster(ncores_clust)
+# parallel::clusterExport(cl, varlist=c("getContribs","K1"), envir=environment())
repeat
{
+
+print(length(indices))
+
nb_workers = max( 1, floor( length(indices) / nb_series_per_chunk ) )
indices_workers = lapply( seq_len(nb_workers), function(i)
indices[(nb_series_per_chunk*(i-1)+1):(nb_series_per_chunk*i)] )
indices_workers[[index]] = c(indices_workers[[index]], tail(indices,rem))
rem = rem - 1
}
- indices = unlist( parallel::parLapply( cl, indices_workers, function(inds) {
- require("epclust", quietly=TRUE)
- inds[ computeClusters1(getCoefs(inds), K1) ]
+# indices = unlist( parallel::parLapply( cl, indices_workers, function(inds) {
+ indices = unlist( lapply( indices_workers, function(inds) {
+# require("epclust", quietly=TRUE)
+
+print(paste(" ",length(inds))) ## PROBLEME ICI : 21104 ??!
+
+ inds[ computeClusters1(getContribs(inds), K1) ]
} ) )
if (length(indices) == K1)
break
}
- parallel::stopCluster(cl)
+# parallel::stopCluster(cl)
indices #medoids
}
-# Apply the clustering algorithm (PAM) on a coeffs or distances matrix
-computeClusters1 = function(coefs, K1)
- cluster::pam(coefs, K1, diss=FALSE)$id.med
+#' @rdname clustering
+#' @export
+computeClusters1 = function(contribs, K1)
+ cluster::pam(contribs, K1, diss=FALSE)$id.med
-# Cluster a chunk of series inside one task (~max nb_series_per_chunk)
+#' @rdname clustering
+#' @export
computeClusters2 = function(medoids, K2, getRefSeries, nb_series_per_chunk)
{
synchrones = computeSynchrones(medoids, getRefSeries, nb_series_per_chunk)
medoids[ cluster::pam(computeWerDists(synchrones), K2, diss=TRUE)$medoids , ]
}
-# Compute the synchrones curves (sum of clusters elements) from a clustering result
+#' computeSynchrones
+#'
+#' Compute the synchrones curves (sum of clusters elements) from a matrix of medoids,
+#' using L2 distances.
+#'
+#' @param medoids Matrix of medoids (curves of same legnth as initial series)
+#' @param getRefSeries Function to retrieve initial series (e.g. in stage 2 after series
+#' have been replaced by stage-1 medoids)
+#' @inheritParams claws
+#'
+#' @export
computeSynchrones = function(medoids, getRefSeries, nb_series_per_chunk)
{
K = nrow(medoids)
synchrones[ sapply(seq_len(K), function(i) all(!is.nan(synchrones[i,]))) , ]
}
-# Compute the WER distance between the synchrones curves (in rows)
-computeWerDists = function(curves)
+#' computeWerDists
+#'
+#' Compute the WER distances between the synchrones curves (in rows), which are
+#' returned (e.g.) by \code{computeSynchrones()}
+#'
+#' @param synchrones A matrix of synchrones, in rows. The series have same length as the
+#' series in the initial dataset
+#'
+#' @export
+computeWerDists = function(synchrones)
{
- if (!require("Rwave", quietly=TRUE))
- stop("Unable to load Rwave library")
- n <- nrow(curves)
- delta <- ncol(curves)
+ n <- nrow(synchrones)
+ delta <- ncol(synchrones)
#TODO: automatic tune of all these parameters ? (for other users)
nvoice <- 4
- # noctave = 2^13 = 8192 half hours ~ 180 days ; ~log2(ncol(curves))
+ # noctave = 2^13 = 8192 half hours ~ 180 days ; ~log2(ncol(synchrones))
noctave = 13
# 4 here represent 2^5 = 32 half-hours ~ 1 day
#NOTE: default scalevector == 2^(0:(noctave * nvoice) / nvoice) * s0 (?)
# (normalized) observations node with CWT
Xcwt4 <- lapply(seq_len(n), function(i) {
- ts <- scale(ts(curves[i,]), center=TRUE, scale=scaled)
+ ts <- scale(ts(synchrones[i,]), center=TRUE, scale=scaled)
totts.cwt = Rwave::cwt(ts,totnoct,nvoice,w0,plot=0)
ts.cwt = totts.cwt[,s0log:(s0log+noctave*nvoice)]
#Normalization
projects / epclust.git / blobdiff