#' Compute the WER distances between the synchrones curves (in columns), which are
#' returned (e.g.) by \code{computeSynchrones()}
#'
-#' @param synchrones A big.matrix of synchrones, in columns. The series have same
-#' length as the series in the initial dataset
+#' @param indices Range of series indices to cluster
#' @inheritParams claws
+#' @inheritParams computeSynchrones
#'
-#' @return A distances matrix of size K1 x K1
+#' @return A distances matrix of size K x K where K == length(indices)
#'
#' @export
-computeWerDists = function(synchrones, nvoice, nbytes,endian,ncores_clust=1,
- verbose=FALSE,parll=TRUE)
+computeWerDists = function(indices, getSeries, nb_series_per_chunk, nvoice, nbytes, endian,
+ ncores_clust=1, verbose=FALSE, parll=TRUE)
{
- n <- ncol(synchrones)
- L <- nrow(synchrones)
+ n <- length(indices)
+ L <- length(getSeries(1)) #TODO: not very nice 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 synchrones'
+ # Initialize result as a square big.matrix of size 'number of medoids'
Xwer_dist <- bigmemory::big.matrix(nrow=n, ncol=n, type="double")
# Generate n(n-1)/2 pairs for WER distances computations
}
cwt_file = ".cwt.bin"
- # Compute the synchrones[,indices] CWT, and store the results in the binary file
+ # Compute the getSeries(indices) CWT, and store the results in the binary file
computeSaveCWT = function(indices)
{
if (parll)
require("bigmemory", quietly=TRUE)
require("Rwave", quietly=TRUE)
require("epclust", quietly=TRUE)
- synchrones <- bigmemory::attach.big.matrix(synchrones_desc)
}
# Obtain CWT as big vectors of real part + imaginary part (concatenate)
ts_cwt <- sapply(indices, function(i) {
- ts <- scale(ts(synchrones[,i]), center=TRUE, scale=FALSE)
+ 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)) )
})
# Serialization
- binarize(ts_cwt, cwt_file, 1, ",", nbytes, endian)
+ binarize(ts_cwt, cwt_file, nb_cwt_per_chunk, ",", nbytes, endian)
}
if (parll)
{
cl = parallel::makeCluster(ncores_clust)
- synchrones_desc <- bigmemory::describe(synchrones)
Xwer_dist_desc <- bigmemory::describe(Xwer_dist)
- parallel::clusterExport(cl, varlist=c("parll","synchrones_desc","Xwer_dist_desc",
- "noctave","nvoice","verbose","getCWT"), envir=environment())
+ parallel::clusterExport(cl, varlist=c("parll","nb_cwt_per_chunk","L",
+ "Xwer_dist_desc","noctave","nvoice","getCWT"), envir=environment())
}
if (verbose)
lapply(1:n, computeSaveCWT)
# Function to retrieve a synchrone CWT from (binary) file
- getSynchroneCWT = function(index, L)
+ getCWT = function(index, L)
{
flat_cwt <- getDataInFile(index, cwt_file, nbytes, endian)
cwt_length = length(flat_cwt) / 2
#TODO: better repartition here,
- #better code in .splitIndices :: never exceed nb_per_chunk; arg: min_per_chunk (default: 1)
-###TODO: reintroduire nb_items_clust ======> l'autre est typiquement + grand !!! (pas de relation !)
# parallel workers start with an empty environment
require("bigmemory", quietly=TRUE)
require("epclust", quietly=TRUE)
- synchrones <- bigmemory::attach.big.matrix(synchrones_desc)
Xwer_dist <- bigmemory::attach.big.matrix(Xwer_dist_desc)
}
cat(paste(" Distances (",i,",",j,"), (",i,",",j+1,") ...\n", sep=""))
# Compute CWT of columns i and j in synchrones
- L = nrow(synchrones)
- cwt_i <- getSynchroneCWT(i, L)
- cwt_j <- getSynchroneCWT(j, L)
+ cwt_i <- getCWT(i, L)
+ cwt_j <- getCWT(j, L)
# Compute the ratio of integrals formula 5.6 for WER^2
# in https://arxiv.org/abs/1101.4744v2 ยง5.3