[cosmetics] Slight improvements in doc

[epclust.git] / epclust / R / computeWerDists.R

diff --git a/epclust/R/computeWerDists.R b/epclust/R/computeWerDists.R
index 8eb755c..0ad5404 100644 (file)
--- a/epclust/R/computeWerDists.R
+++ b/epclust/R/computeWerDists.R
@@ -3,8 +3,8 @@
 #' Compute the WER distances between the series at specified indices, which are
 #' obtaind by \code{getSeries(indices)}
 #'
-#' @param indices Range of series indices to cluster
-#' @param getSeries Function to retrieve series (argument: 'indices', integer vector),
+#' @param indices Indices of the series to consider
+#' @param getSeries Function to retrieve series (argument: 'inds', integer vector),
 #'   as columns of a matrix
 #' @param ncores Number of cores for parallel runs
 #' @inheritParams claws
@@ -12,8 +12,8 @@
 #' @return A distances matrix of size K x K where K == length(indices)
 #'
 #' @export
-computeWerDists <- function(indices, getSeries, nb_series_per_chunk, smooth_lvl, nvoice,
-       nbytes, endian, ncores=3, verbose=FALSE, parll=TRUE)
+computeWerDists <- function(indices, getSeries, nb_series_per_chunk, smooth_lvl=3, nvoice=4,
+       nbytes=4, endian=.Platform$endian, ncores=3, verbose=FALSE)
 {
        n <- length(indices)
        L <- length(getSeries(1)) #TODO: not very neat way to get L
@@ -27,10 +27,13 @@ computeWerDists <- function(indices, getSeries, nb_series_per_chunk, smooth_lvl,
 
        cwt_file <- tempfile(pattern="epclust_cwt.bin_")
        # Compute the getSeries(indices) CWT, and store the results in the binary file
-       computeSaveCWT <- function(indices)
+       computeSaveCWT <- function(inds)
        {
+               if (verbose)
+                       cat("   Compute save CWT on ",length(inds)," indices\n", sep="")
+
                # Obtain CWT as big vectors of real part + imaginary part (concatenate)
-               ts_cwt <- sapply(indices, function(i) {
+               ts_cwt <- sapply(inds, function(i) {
                        ts <- scale(ts(getSeries(i)), center=TRUE, scale=FALSE)
                        ts_cwt <- Rwave::cwt(ts, noctave, nvoice, w0=2*pi, twoD=TRUE, plot=FALSE)
                        c( as.double(Re(ts_cwt)),as.double(Im(ts_cwt)) )
@@ -60,7 +63,7 @@ computeWerDists <- function(indices, getSeries, nb_series_per_chunk, smooth_lvl,
                        Xwer_dist <- bigmemory::attach.big.matrix(Xwer_dist_desc)
                }
 
-               if (verbose && !parll)
+               if (verbose)
                        cat(paste("   Distances from ",i," to ",i+1,"...",n,"\n", sep=""))
 
                # Get CWT of column i, and run computations for columns j>i
@@ -87,13 +90,14 @@ computeWerDists <- function(indices, getSeries, nb_series_per_chunk, smooth_lvl,
                cat(paste("--- Precompute and serialize synchrones CWT\n", sep=""))
 
        # Split indices by packets of length at most nb_cwt_per_chunk
-       indices_cwt <- .splitIndices(seq_len(n), nb_cwt_per_chunk)
+       indices_cwt <- .splitIndices(indices, nb_cwt_per_chunk)
        # NOTE: next loop could potentially be run in //. Indices would be permuted (by
        # serialization order), and synchronicity would be required because of concurrent
-       # writes. Probably not worth the effort - but possible to gain some bits of speed.
+       # writes. Probably not worth the effort - but possible.
        for (inds in indices_cwt)
                computeSaveCWT(inds)
 
+       parll <- (ncores > 1)
        if (parll)
        {
                # outfile=="" to see stderr/stdout on terminal