{
ref_series = getRefSeries(indices)
nb_series = nrow(ref_series)
- #get medoids indices for this chunk of series
- #TODO: debug this (address is OK but values are garbage: why?)
-# mi = .Call("computeMedoidsIndices", medoids@address, ref_series, PACKAGE="epclust")
+ if (parll)
+ {
+ require("bigmemory", quietly=TRUE)
+ require("synchronicity", quietly=TRUE)
+ require("epclust", quietly=TRUE)
+ synchrones <- bigmemory::attach.big.matrix(synchrones_desc)
+ medoids <- bigmemory::attach.big.matrix(medoids_desc)
+ m <- synchronicity::attach.mutex(m_desc)
+ }
+
+
- #R-equivalent, requiring a matrix (thus potentially breaking "fit-in-memory" hope)
- mat_meds = medoids[,]
- mi = rep(NA,nb_series)
- for (i in 1:nb_series)
- mi[i] <- which.min( rowSums( sweep(mat_meds, 2, ref_series[i,], '-')^2 ) )
- rm(mat_meds); gc()
+#TODO: use dbs(),
+ #https://www.r-bloggers.com/debugging-parallel-code-with-dbs/
+ #http://gforge.se/2015/02/how-to-go-parallel-in-r-basics-tips/
+
+#OK ::
+#write(length(indices), file="TOTO")
+#write( computeMedoidsIndices(medoids@address, getRefSeries(indices[1:600])), file="TOTO")
+#stop()
+
+# write(indices, file="TOTO", ncolumns=10, append=TRUE)
+#write("medoids", file = "TOTO", ncolumns=1, append=TRUE)
+#write(medoids[1,1:3], file = "TOTO", ncolumns=1, append=TRUE)
+#write("synchrones", file = "TOTO", ncolumns=1, append=TRUE)
+#write(synchrones[1,1:3], file = "TOTO", ncolumns=1, append=TRUE)
+
+#NOT OK :: (should just be "ref_series") ...or yes ? race problems mutex then ? ?!
+ #get medoids indices for this chunk of series
+ mi = computeMedoidsIndices(medoids@address, getRefSeries(indices[1:600])) #ref_series)
+write("MI ::::", file = "TOTO", ncolumns=1, append=TRUE)
+write(mi[1:3], file = "TOTO", ncolumns=1, append=TRUE)
+
+# #R-equivalent, requiring a matrix (thus potentially breaking "fit-in-memory" hope)
+# mat_meds = medoids[,]
+# mi = rep(NA,nb_series)
+# for (i in 1:nb_series)
+# mi[i] <- which.min( rowSums( sweep(mat_meds, 2, ref_series[i,], '-')^2 ) )
+# rm(mat_meds); gc()
for (i in seq_len(nb_series))
{
parll = (requireNamespace("synchronicity",quietly=TRUE)
&& parll && Sys.info()['sysname'] != "Windows")
if (parll)
+ {
m <- synchronicity::boost.mutex()
+ m_desc <- synchronicity::describe(m)
+ synchrones_desc = bigmemory::describe(synchrones)
+ medoids_desc = bigmemory::describe(medoids)
- if (parll)
- {
cl = parallel::makeCluster(ncores_clust)
parallel::clusterExport(cl,
- varlist=c("synchrones","counts","verbose","medoids","getRefSeries"),
+ varlist=c("synchrones_desc","counts","verbose","m_desc","medoids_desc","getRefSeries"),
envir=environment())
}
indices_workers = .spreadIndices(seq_len(nb_ref_curves), nb_series_per_chunk)
-#browser()
ignored <-
if (parll)
parallel::parLapply(cl, indices_workers, computeSynchronesChunk)
pairs = c(pairs, lapply(V, function(v) c(i,v)))
}
- computeCWT = function(i)
- {
- ts <- scale(ts(synchrones[i,]), center=TRUE, scale=scaled)
- totts.cwt = Rwave::cwt(ts, totnoct, nvoice, w0, plot=FALSE)
- ts.cwt = totts.cwt[,s0log:(s0log+noctave*nvoice)]
- #Normalization
- sqs <- sqrt(2^(0:(noctave*nvoice)/nvoice)*s0)
- sqres <- sweep(ts.cwt,2,sqs,'*')
- sqres / max(Mod(sqres))
- }
-
# Distance between rows i and j
computeDistancesIJ = function(pair)
{
+ require("bigmemory", quietly=TRUE)
+ require("epclust", quietly=TRUE)
+ synchrones <- bigmemory::attach.big.matrix(synchrones_desc)
+ Xwer_dist <- bigmemory::attach.big.matrix(Xwer_dist_desc)
+
+ computeCWT = function(i)
+ {
+ ts <- scale(ts(synchrones[i,]), center=TRUE, scale=scaled)
+ totts.cwt = Rwave::cwt(ts, totnoct, nvoice, w0, plot=FALSE)
+ ts.cwt = totts.cwt[,s0log:(s0log+noctave*nvoice)]
+ #Normalization
+ sqs <- sqrt(2^(0:(noctave*nvoice)/nvoice)*s0)
+ sqres <- sweep(ts.cwt,2,sqs,'*')
+ sqres / max(Mod(sqres))
+ }
+
i = pair[1] ; j = pair[2]
if (verbose && j==i+1)
cat(paste(" Distances (",i,",",j,"), (",i,",",j+1,") ...\n", sep=""))
cwt_i = computeCWT(i)
cwt_j = computeCWT(j)
- num <- .Call("filter", Mod(cwt_i * Conj(cwt_j)), PACKAGE="epclust")
- WX <- .Call("filter", Mod(cwt_i * Conj(cwt_i)), PACKAGE="epclust")
- WY <- .Call("filter", Mod(cwt_j * Conj(cwt_j)), PACKAGE="epclust")
+ num <- epclustFilter(Mod(cwt_i * Conj(cwt_j)))
+ WX <- epclustFilter(Mod(cwt_i * Conj(cwt_i)))
+ WY <- epclustFilter(Mod(cwt_j * Conj(cwt_j)))
wer2 <- sum(colSums(num)^2) / sum(colSums(WX) * colSums(WY))
Xwer_dist[i,j] <- sqrt(delta * ncol(cwt_i) * (1 - wer2))
Xwer_dist[j,i] <- Xwer_dist[i,j]
if (parll)
{
cl = parallel::makeCluster(ncores_clust)
- parallel::clusterExport(cl,
- varlist=c("synchrones","totnoct","nvoice","w0","s0log","noctave","s0","verbose"),
- envir=environment())
+ synchrones_desc <- bigmemory::describe(synchrones)
+ Xwer_dist_desc_desc <- bigmemory::describe(Xwer_dist)
+
+ parallel::clusterExport(cl, varlist=c("synchrones_desc","Xwer_dist_desc","totnoct",
+ "nvoice","w0","s0log","noctave","s0","verbose"), envir=environment())
}
ignored <-
projects / epclust.git / blobdiff