drop enercast submodule; drop Rcpp requirement; fix doc, complete code, fix fix fix

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

diff --git a/epclust/R/clustering.R b/epclust/R/clustering.R
index b91d512..a8f1d3e 100644 (file)
--- a/epclust/R/clustering.R
+++ b/epclust/R/clustering.R
@@ -22,19 +22,20 @@ NULL
 
 #' @rdname clustering
 #' @export
-clusteringTask1 = function(indices, getContribs, K1, algoClust1, nb_items_clust,
+clusteringTask1 <- function(indices, getContribs, K1, algoClust1, nb_items_clust,
        ncores_clust=1, verbose=FALSE, parll=TRUE)
 {
        if (parll)
        {
-               cl = parallel::makeCluster(ncores_clust, outfile = "")
+               # outfile=="" to see stderr/stdout on terminal
+               cl <- parallel::makeCluster(ncores_clust, outfile = "")
                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_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 <-
@@ -60,8 +61,8 @@ clusteringTask1 = function(indices, getContribs, K1, algoClust1, nb_items_clust,
 
 #' @rdname clustering
 #' @export
-clusteringTask2 = function(indices, getSeries, K2, algoClust2, nb_series_per_chunk,
-       nvoice, nbytes, endian, ncores_clust=1, verbose=FALSE, parll=TRUE)
+clusteringTask2 <- function(indices, getSeries, K2, algoClust2, nb_series_per_chunk,
+       smooth_lvl, nvoice, nbytes, endian, ncores_clust=1, verbose=FALSE, parll=TRUE)
 {
        if (verbose)
                cat(paste("*** Clustering task 2 on ",length(indices)," medoids\n", sep=""))
@@ -70,8 +71,8 @@ clusteringTask2 = function(indices, getSeries, K2, algoClust2, nb_series_per_chu
                return (indices)
 
        # 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)
+       distances <- computeWerDists(indices, getSeries, nb_series_per_chunk,
+               smooth_lvl, nvoice, nbytes, endian, ncores_clust, verbose, parll)
 
        # B) Apply clustering algorithm 2 on the WER distances matrix
        if (verbose)