merge with remote

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

diff --git a/pkg/R/clustering.R b/pkg/R/clustering.R
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+#' Two-stage clustering, within one task (see \code{claws()})
+#'
+#' \code{clusteringTask1()} runs one full stage-1 task, which consists in iterated
+#' 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 getContribs Function to retrieve contributions from initial series indices:
+#'   \code{getContribs(indices)} outputs a contributions matrix, in columns
+#' @inheritParams claws
+#' @inheritParams computeSynchrones
+#' @inheritParams computeWerDists
+#'
+#' @return The indices of the computed (resp. K1 and K2) medoids.
+#'
+#' @name clustering
+#' @rdname clustering
+#' @aliases clusteringTask1 clusteringTask2
+NULL
+
+#' @rdname clustering
+#' @export
+clusteringTask1 <- function(indices, getContribs, K1, algoClust1, nb_items_clust,
+       ncores_clust=3, verbose=FALSE)
+{
+       if (verbose)
+               cat(paste("*** Clustering task 1 on ",length(indices)," series [start]\n", sep=""))
+
+       if (length(indices) <= K1)
+               return (indices)
+
+       parll <- (ncores_clust > 1)
+       if (parll)
+       {
+               # outfile=="" to see stderr/stdout on terminal
+               cl <-
+                       if (verbose)
+                               parallel::makeCluster(ncores_clust, outfile = "")
+                       else
+                               parallel::makeCluster(ncores_clust)
+               parallel::clusterExport(cl, c("getContribs","K1","verbose"), envir=environment())
+       }
+       # Iterate clustering algorithm 1 until K1 medoids are found
+       while (length(indices) > K1)
+       {
+               # Balance tasks by splitting the indices set - as evenly as possible
+               indices_workers <- .splitIndices(indices, nb_items_clust, min_size=K1+1)
+               indices <-
+                       if (parll)
+                       {
+                               unlist( parallel::parLapply(cl, indices_workers, function(inds) {
+                                       require("epclust", quietly=TRUE)
+                                       inds[ algoClust1(getContribs(inds), K1) ]
+                               }) )
+                       }
+                       else
+                       {
+                               unlist( lapply(indices_workers, function(inds)
+                                       inds[ algoClust1(getContribs(inds), K1) ]
+                               ) )
+                       }
+               if (verbose)
+               {
+                       cat(paste("*** Clustering task 1 on ",length(indices)," medoids [iter]\n", sep=""))
+               }
+       }
+       if (parll)
+               parallel::stopCluster(cl)
+
+       indices #medoids
+}
+
+#' @rdname clustering
+#' @export
+clusteringTask2 <- function(indices, getSeries, K2, algoClust2, nb_series_per_chunk,
+       smooth_lvl, nvoice, nbytes, endian, ncores_clust=3, verbose=FALSE)
+{
+       if (verbose)
+               cat(paste("*** Clustering task 2 on ",length(indices)," medoids\n", sep=""))
+
+       if (length(indices) <= K2)
+               return (indices)
+
+       # A) Compute the WER distances (Wavelets Extended coefficient of deteRmination)
+       distances <- computeWerDists(indices, getSeries, nb_series_per_chunk,
+               smooth_lvl, nvoice, nbytes, endian, ncores_clust, verbose)
+
+       # B) Apply clustering algorithm 2 on the WER distances matrix
+       if (verbose)
+               cat(paste("*** algoClust2() on ",nrow(distances)," items\n", sep=""))
+       indices[ algoClust2(distances,K2) ]
+}