--- /dev/null
+#' computeWerDists
+#'
+#' Compute the WER distances between the series at specified indices, which are
+#' obtaind by \code{getSeries(indices)}
+#'
+#' @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
+#'
+#' @return A distances matrix of size K x K where K == length(indices)
+#'
+#' @export
+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
+ noctave <- ceiling(log2(L)) #min power of 2 to cover serie range
+ # Since a CWT contains noctave*nvoice complex series, we deduce the number of CWT to
+ # retrieve/put in one chunk.
+ nb_cwt_per_chunk <- max(1, floor(nb_series_per_chunk / (nvoice*noctave*2)))
+
+ # Initialize result as a square big.matrix of size 'number of medoids'
+ Xwer_dist <- bigmemory::big.matrix(nrow=n, ncol=n, type="double")
+
+ shift <- 1 #roughly equivalent to s0 in biwavelets & cie. TODO: as arg?
+
+ cwt_file <- tempfile(pattern="epclust_cwt.bin_")
+ # Compute the getSeries(indices) CWT, and store the results in the binary file
+ 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(inds, function(i) {
+ ts <- scale(ts(getSeries(i)), center=TRUE, scale=FALSE)
+ ts_cwt <- Rwave::cwt(ts, noctave+ceiling(shift/nvoice), nvoice,
+ w0=2*pi, twoD=TRUE, plot=FALSE)
+ ts_cwt <- ts_cwt[,(1+shift):(noctave*nvoice+shift)]
+ c( as.double(Re(ts_cwt)),as.double(Im(ts_cwt)) )
+ })
+
+ # Serialization
+ binarize(ts_cwt, cwt_file, nb_cwt_per_chunk, ",", nbytes, endian)
+ }
+
+ # Function to retrieve a synchrone CWT from (binary) file
+ getCWT <- function(index, L)
+ {
+ flat_cwt <- getDataInFile(index, cwt_file, nbytes, endian)
+ cwt_length <- length(flat_cwt) / 2
+ re_part <- as.matrix(flat_cwt[1:cwt_length], nrow=L)
+ im_part <- as.matrix(flat_cwt[(cwt_length+1):(2*cwt_length)], nrow=L)
+ re_part + 1i * im_part
+ }
+
+ # Compute distances between columns i and j for j>i
+ computeDistances <- function(i)
+ {
+ if (parll)
+ {
+ # parallel workers start with an empty environment
+ require("epclust", quietly=TRUE)
+ Xwer_dist <- bigmemory::attach.big.matrix(Xwer_dist_desc)
+ }
+
+ 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
+ cwt_i <- getCWT(i, L)
+ WX <- filterMA(Mod(cwt_i * Conj(cwt_i)), smooth_lvl)
+
+ for (j in (i+1):n)
+ {
+ cwt_j <- getCWT(j, L)
+
+ # Compute the ratio of integrals formula 5.6 for WER^2
+ # 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))
+
+ Xwer_dist[i,j] <- sqrt(L * ncol(cwt_i) * (1 - wer2))
+ Xwer_dist[j,i] <- Xwer_dist[i,j]
+ }
+ 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 <-
+ if (verbose)
+ parallel::makeCluster(ncores, outfile="")
+ else
+ parallel::makeCluster(ncores)
+ Xwer_dist_desc <- bigmemory::describe(Xwer_dist)
+ parallel::clusterExport(cl, envir=environment(),
+ varlist=c("parll","n","L","Xwer_dist_desc","getCWT","verbose"))
+ }
+
+ if (verbose)
+ cat(paste("--- Compute WER distances\n", sep=""))
+
+ ignored <-
+ if (parll)
+ parallel::clusterApplyLB(cl, seq_len(n-1), computeDistances)
+ else
+ lapply(seq_len(n-1), computeDistances)
+ Xwer_dist[n,n] <- 0.
+
+ if (parll)
+ parallel::stopCluster(cl)
+
+ unlink(cwt_file) #remove binary file
+
+ Xwer_dist[,] #~small matrix K1 x K1
+}