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

[epclust.git] / epclust / R / computeSynchrones.R

diff --git a/epclust/R/computeSynchrones.R b/epclust/R/computeSynchrones.R
index 09ff3a0..16bf0b4 100644 (file)
--- a/epclust/R/computeSynchrones.R
+++ b/epclust/R/computeSynchrones.R
@@ -11,11 +11,11 @@
 #' @return A matrix of K synchrones in columns (same length as the series)
 #'
 #' @export
-computeSynchrones = function(medoids, getSeries, nb_curves,
+computeSynchrones <- function(medoids, getSeries, nb_curves,
        nb_series_per_chunk, ncores_clust=1,verbose=FALSE,parll=TRUE)
 {
        # Synchrones computation is embarassingly parallel: compute it by chunks of series
-       computeSynchronesChunk = function(indices)
+       computeSynchronesChunk <- function(indices)
        {
                if (parll)
                {
@@ -29,12 +29,11 @@ computeSynchrones = function(medoids, getSeries, nb_curves,
                }
 
                # Obtain a chunk of reference series
-               series_chunk = getSeries(indices)
-               nb_series_chunk = ncol(series_chunk)
+               series_chunk <- getSeries(indices)
+               nb_series_chunk <- ncol(series_chunk)
 
                # Get medoids indices for this chunk of series
-               for (i in seq_len(nb_series_chunk))
-                       mi[i] <- which.min( colSums( sweep(medoids, 1, series_chunk[,i], '-')^2 ) )
+               mi <- assignMedoids(series_chunk, medoids[,])
 
                # Update synchrones using mi above, grouping it by values of mi (in 1...K)
                # to avoid too many lock/unlock
@@ -43,19 +42,19 @@ computeSynchrones = function(medoids, getSeries, nb_curves,
                        # lock / unlock required because several writes at the same time
                        if (parll)
                                synchronicity::lock(m)
-                       synchrones[,i] = synchrones[,i] + rowSums(series_chunk[,mi==i])
+                       synchrones[,i] <- synchrones[,i] + rowSums(as.matrix(series_chunk[,mi==i]))
                        if (parll)
                                synchronicity::unlock(m)
                }
                NULL
        }
 
-       K = ncol(medoids)
-       L = nrow(medoids)
+       K <- ncol(medoids)
+       L <- nrow(medoids)
        # Use bigmemory (shared==TRUE by default) + synchronicity to fill synchrones in //
-       synchrones = bigmemory::big.matrix(nrow=L, ncol=K, type="double", init=0.)
+       synchrones <- bigmemory::big.matrix(nrow=L, ncol=K, type="double", init=0.)
        # NOTE: synchronicity is only for Linux & MacOS; on Windows: run sequentially
-       parll = (parll && requireNamespace("synchronicity",quietly=TRUE)
+       parll <- (parll && requireNamespace("synchronicity",quietly=TRUE)
                && Sys.info()['sysname'] != "Windows")
        if (parll)
        {
@@ -63,10 +62,11 @@ computeSynchrones = function(medoids, getSeries, nb_curves,
                # mutex and big.matrix objects cannot be passed directly:
                # they will be accessed from their description
                m_desc <- synchronicity::describe(m)
-               synchrones_desc = bigmemory::describe(synchrones)
+               synchrones_desc <- bigmemory::describe(synchrones)
                medoids <- bigmemory::as.big.matrix(medoids)
                medoids_desc <- bigmemory::describe(medoids)
-               cl = parallel::makeCluster(ncores_clust)
+               # outfile=="" to see stderr/stdout on terminal
+               cl <- parallel::makeCluster(ncores_clust, outfile="")
                parallel::clusterExport(cl, envir=environment(),
                        varlist=c("synchrones_desc","m_desc","medoids_desc","getSeries"))
        }
@@ -75,7 +75,7 @@ computeSynchrones = function(medoids, getSeries, nb_curves,
                cat(paste("--- Compute ",K," synchrones with ",nb_curves," series\n", sep=""))
 
        # Balance tasks by splitting 1:nb_curves into groups of size <= nb_series_per_chunk
-       indices_workers = .splitIndices(seq_len(nb_curves), nb_series_per_chunk)
+       indices_workers <- .splitIndices(seq_len(nb_curves), nb_series_per_chunk)
        ignored <-
                if (parll)
                        parallel::parLapply(cl, indices_workers, computeSynchronesChunk)