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)) )
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
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)