require("epclust", quietly=TRUE)
synchrones <- bigmemory::attach.big.matrix(synchrones_desc)
require("epclust", quietly=TRUE)
synchrones <- bigmemory::attach.big.matrix(synchrones_desc)
#get medoids indices for this chunk of series
mi = computeMedoidsIndices(medoids@address, ref_series)
#get medoids indices for this chunk of series
mi = computeMedoidsIndices(medoids@address, ref_series)
-# #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()
- synchrones[mi[i],] = synchrones[mi[i],] + ref_series[i,]
-#TODO: remove counts
- counts[mi[i],1] = counts[mi[i],1] + 1
+ synchrones[ mi[i], ] = synchrones[ mi[i], ] + ref_series[i,]
+ counts[ mi[i] ] = counts[ mi[i] ] + 1 #TODO: remove counts?
m <- synchronicity::boost.mutex()
m_desc <- synchronicity::describe(m)
synchrones_desc = bigmemory::describe(synchrones)
m <- synchronicity::boost.mutex()
m_desc <- synchronicity::describe(m)
synchrones_desc = bigmemory::describe(synchrones)
- parallel::clusterExport(cl,
- varlist=c("synchrones_desc","counts","verbose","m_desc","medoids_desc","getRefSeries"),
- envir=environment())
+ parallel::clusterExport(cl, varlist=c("synchrones_desc","counts_desc","counts",
+ "verbose","m_desc","medoids_desc","getRefSeries"), envir=environment())
totnoct = noctave + as.integer(s0log/nvoice) + 1
Xwer_dist <- bigmemory::big.matrix(nrow=n, ncol=n, type="double")
totnoct = noctave + as.integer(s0log/nvoice) + 1
Xwer_dist <- bigmemory::big.matrix(nrow=n, ncol=n, type="double")
sqres <- sweep(ts.cwt,2,sqs,'*')
sqres / max(Mod(sqres))
}
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=""))
i = pair[1] ; j = pair[2]
if (verbose && j==i+1)
cat(paste(" Distances (",i,",",j,"), (",i,",",j+1,") ...\n", sep=""))
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) * max(1 - wer2, 0.)) #FIXME: wer2 should be < 1
Xwer_dist[j,i] <- Xwer_dist[i,j]
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) * max(1 - wer2, 0.)) #FIXME: wer2 should be < 1
Xwer_dist[j,i] <- Xwer_dist[i,j]
parallel::clusterExport(cl, varlist=c("synchrones_desc","Xwer_dist_desc","totnoct",
"nvoice","w0","s0log","noctave","s0","verbose"), envir=environment())
}
parallel::clusterExport(cl, varlist=c("synchrones_desc","Xwer_dist_desc","totnoct",
"nvoice","w0","s0log","noctave","s0","verbose"), envir=environment())
}