'update'

[epclust.git] / epclust / R / clustering.R

diff --git a/epclust/R/clustering.R b/epclust/R/clustering.R
index bea073a..b91d512 100644 (file)
--- a/epclust/R/clustering.R
+++ b/epclust/R/clustering.R
@@ -1,30 +1,28 @@
-#' @name clustering
-#' @rdname clustering
-#' @aliases clusteringTask1 clusteringTask2 computeClusters1 computeClusters2
+#' Two-stage clustering, within one task (see \code{claws()})
 #'
-#' @title Two-stage clustering, withing one task (see \code{claws()})
+#' \code{clusteringTask1()} runs one full stage-1 task, which consists in iterated
+#' stage 1 clustering on nb_curves / ntasks energy contributions, computed through
+#' discrete wavelets coefficients.
+#' \code{clusteringTask2()} runs a full stage-2 task, which consists in WER distances
+#' computations between medoids (indices) output from stage 1, before applying
+#' the second clustering algorithm on the distances matrix.
 #'
-#' @description \code{clusteringTask1()} runs one full stage-1 task, which consists in
-#'   iterated stage 1 clustering on nb_curves / ntasks energy contributions, computed
-#'   through discrete wavelets coefficients.
-#'   \code{clusteringTask2()} runs a full stage-2 task, which consists in
-#'   WER distances computations between medoids indices output from stage 1,
-#'   before applying the second clustering algorithm, on the distances matrix.
-#'
-#' @param indices Range of series indices to cluster
 #' @param getContribs Function to retrieve contributions from initial series indices:
 #'   \code{getContribs(indices)} outputs a contributions matrix
 #' @inheritParams claws
 #' @inheritParams computeSynchrones
+#' @inheritParams computeWerDists
+#'
+#' @return The indices of the computed (resp. K1 and K2) medoids.
 #'
-#' @return For \code{clusteringTask1()}, the indices of the computed (K1) medoids.
-#'   Indices are irrelevant for stage 2 clustering, thus \code{clusteringTask2()}
-#'   outputs a big.matrix of medoids (of size LxK2, K2 = final number of clusters)
+#' @name clustering
+#' @rdname clustering
+#' @aliases clusteringTask1 clusteringTask2
 NULL
 
 #' @rdname clustering
 #' @export
-clusteringTask1 = function(indices, getContribs, K1, algoClust1, nb_series_per_chunk,
+clusteringTask1 = function(indices, getContribs, K1, algoClust1, nb_items_clust,
        ncores_clust=1, verbose=FALSE, parll=TRUE)
 {
        if (parll)
@@ -36,7 +34,7 @@ clusteringTask1 = function(indices, getContribs, K1, algoClust1, nb_series_per_c
        while (length(indices) > K1)
        {
                # Balance tasks by splitting the indices set - as evenly as possible
-               indices_workers = .splitIndices(indices, nb_items_clust1)
+               indices_workers = .splitIndices(indices, nb_items_clust, min_size=K1+1)
                if (verbose)
                        cat(paste("*** [iterated] Clustering task 1 on ",length(indices)," series\n", sep=""))
                indices <-
@@ -66,22 +64,17 @@ clusteringTask2 = function(indices, getSeries, K2, algoClust2, nb_series_per_chu
        nvoice, nbytes, endian, ncores_clust=1, verbose=FALSE, parll=TRUE)
 {
        if (verbose)
-               cat(paste("*** Clustering task 2 on ",ncol(medoids)," synchrones\n", sep=""))
-
-       if (ncol(medoids) <= K2)
-               return (medoids)
+               cat(paste("*** Clustering task 2 on ",length(indices)," medoids\n", sep=""))
 
-       # A) Obtain synchrones, that is to say the cumulated power consumptions
-       #    for each of the K1 initial groups
-       synchrones = computeSynchrones(medoids, getRefSeries, nb_ref_curves,
-               nb_series_per_chunk, ncores_clust, verbose, parll)
+       if (length(indices) <= K2)
+               return (indices)
 
-       # B) Compute the WER distances (Wavelets Extended coefficient of deteRmination)
-       distances = computeWerDists(
-               synchrones, nvoice, nbytes, endian, ncores_clust, verbose, parll)
+       # A) Compute the WER distances (Wavelets Extended coefficient of deteRmination)
+       distances = computeWerDists(indices, getSeries, nb_series_per_chunk,
+               nvoice, nbytes, endian, ncores_clust, verbose, parll)
 
-       # C) Apply clustering algorithm 2 on the WER distances matrix
+       # B) Apply clustering algorithm 2 on the WER distances matrix
        if (verbose)
                cat(paste("*** algoClust2() on ",nrow(distances)," items\n", sep=""))
-       medoids[ ,algoClust2(distances,K2) ]
+       indices[ algoClust2(distances,K2) ]
 }