[cosmetics] Slight improvements in doc

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

diff --git a/epclust/R/computeWerDists.R b/epclust/R/computeWerDists.R
index 061c360..0ad5404 100644 (file)
--- a/epclust/R/computeWerDists.R
+++ b/epclust/R/computeWerDists.R
@@ -1,17 +1,19 @@
 #' computeWerDists
 #'
-#' Compute the WER distances between the synchrones curves (in columns), which are
-#' returned (e.g.) by \code{computeSynchrones()}
+#' 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 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
-#' @inheritParams computeSynchrones
 #'
 #' @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_clust=1, 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
@@ -25,17 +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 (parll)
-               {
-                       require("bigmemory", quietly=TRUE)
-                       require("Rwave", quietly=TRUE)
-                       require("epclust", quietly=TRUE)
-               }
+               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)) )
@@ -55,18 +53,17 @@ computeWerDists <- function(indices, getSeries, nb_series_per_chunk, smooth_lvl,
                re_part + 1i * im_part
        }
 
-       # Compute distance between columns i and j for j>i
+       # Compute distances between columns i and j for j>i
        computeDistances <- function(i)
        {
                if (parll)
                {
                        # parallel workers start with an empty environment
-                       require("bigmemory", quietly=TRUE)
                        require("epclust", quietly=TRUE)
                        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
@@ -78,7 +75,7 @@ computeWerDists <- function(indices, getSeries, nb_series_per_chunk, smooth_lvl,
                        cwt_j <- getCWT(j, L)
 
                        # Compute the ratio of integrals formula 5.6 for WER^2
-                       # in https://arxiv.org/abs/1101.4744v2 §5.3
+                       # in https://arxiv.org/abs/1101.4744v2 paragraph 5.3
                        num <- filterMA(Mod(cwt_i * Conj(cwt_j)), smooth_lvl)
                        WY <- filterMA(Mod(cwt_j * Conj(cwt_j)), smooth_lvl)
                        wer2 <- sum(colSums(num)^2) / sum(colSums(WX) * colSums(WY))
@@ -89,26 +86,31 @@ computeWerDists <- function(indices, getSeries, nb_series_per_chunk, smooth_lvl,
                Xwer_dist[i,i] <- 0.
        }
 
+       if (verbose)
+               cat(paste("--- Precompute and serialize synchrones CWT\n", sep=""))
+
+       # Split indices by packets of length at most 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.
+       for (inds in indices_cwt)
+               computeSaveCWT(inds)
+
+       parll <- (ncores > 1)
        if (parll)
        {
                # outfile=="" to see stderr/stdout on terminal
-               cl <- parallel::makeCluster(ncores_clust, outfile="")
+               cl <-
+                       if (verbose)
+                               parallel::makeCluster(ncores, outfile="")
+                       else
+                               parallel::makeCluster(ncores)
                Xwer_dist_desc <- bigmemory::describe(Xwer_dist)
-               parallel::clusterExport(cl, varlist=c("parll","nb_cwt_per_chunk","n","L",
-                       "Xwer_dist_desc","noctave","nvoice","getCWT"), envir=environment())
+               parallel::clusterExport(cl, envir=environment(),
+                       varlist=c("parll","n","L","Xwer_dist_desc","getCWT","verbose"))
        }
 
-       if (verbose)
-               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)
-       ignored <-
-               if (parll)
-                       parallel::parLapply(cl, indices_cwt, computeSaveCWT)
-               else
-                       lapply(indices_cwt, computeSaveCWT)
-
        if (verbose)
                cat(paste("--- Compute WER distances\n", sep=""))