save state: wrong idea for indices repartition

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

diff --git a/epclust/R/clustering.R b/epclust/R/clustering.R
index 14915ab..36b4769 100644 (file)
--- a/epclust/R/clustering.R
+++ b/epclust/R/clustering.R
@@ -11,8 +11,8 @@
 #'   and then WER distances computations, before applying the clustering algorithm.
 #'   \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)
+#'   first clustering algorithm on a contributions matrix, while the latter clusters
+#'   a set of series inside one task (~nb_items_clust)
 #'
 #' @param indices Range of series indices to cluster in parallel (initial data)
 #' @param getContribs Function to retrieve contributions from initial series indices:
@@ -30,20 +30,20 @@ NULL
 
 #' @rdname clustering
 #' @export
-clusteringTask1 = function(
-       indices, getContribs, K1, nb_items_per_chunk, ncores_clust=1, verbose=FALSE, parll=TRUE)
+clusteringTask1 = function(indices, getContribs, K1, nb_items_clust1,
+       ncores_clust=1, verbose=FALSE, parll=TRUE)
 {
        if (verbose)
                cat(paste("*** Clustering task 1 on ",length(indices)," lines\n", sep=""))
 
        if (parll)
        {
-               cl = parallel::makeCluster(ncores_clust)
+               cl = parallel::makeCluster(ncores_clust, outfile = "")
                parallel::clusterExport(cl, varlist=c("getContribs","K1","verbose"), envir=environment())
        }
        while (length(indices) > K1)
        {
-               indices_workers = .spreadIndices(indices, nb_series_per_chunk)
+               indices_workers = .spreadIndices(indices, nb_items_clust1, K1+1)
                indices <-
                        if (parll)
                        {
@@ -317,20 +317,31 @@ computeWerDists = function(synchrones, nbytes,endian,ncores_clust=1,verbose=FALS
 }
 
 # Helper function to divide indices into balanced sets
-.spreadIndices = function(indices, nb_per_chunk)
+.spreadIndices = function(indices, max_per_set, min_nb_per_set = 1)
 {
        L = length(indices)
-       nb_workers = floor( L / nb_per_chunk )
-       if (nb_workers == 0)
+       min_nb_workers = floor( L / max_per_set )
+       rem = L %% max_per_set
+       if (nb_workers == 0 || (nb_workers==1 && rem==0))
        {
-               # L < nb_series_per_chunk, simple case
+               # L <= max_nb_per_set, simple case
                indices_workers = list(indices)
        }
        else
        {
                indices_workers = lapply( seq_len(nb_workers), function(i)
-                       indices[(nb_per_chunk*(i-1)+1):(nb_per_chunk*i)] )
-               # Spread the remaining load among the workers
+                       indices[(max_nb_per_set*(i-1)+1):(max_per_set*i)] )
+               # Two cases: remainder is >= min_per_set (easy)...
+               if (rem >= min_nb_per_set)
+                       indices_workers = c( indices_workers, list(tail(indices,rem)) )
+               #...or < min_per_set: harder, need to remove indices from current sets to feed
+               # the too-small remainder. It may fail: then fallback to "slightly bigger sets"
+               else
+               {
+                       save_indices_workers = indices_workers
+                       small_set = tail(indices,rem)
+                       # Try feeding small_set until it reaches min_per_set, whle keeping the others big enough
+                       # Spread the remaining load among the workers
                rem = L %% nb_per_chunk
                while (rem > 0)
                {