#' @rdname clustering
#' @export
clusteringTask2 = function(medoids, K2, algoClust2, getRefSeries, nb_ref_curves,
- nb_series_per_chunk, nbytes,endian,ncores_clust=1,verbose=FALSE,parll=TRUE)
+ nb_series_per_chunk, nvoice, nbytes,endian,ncores_clust=1,verbose=FALSE,parll=TRUE)
{
if (verbose)
cat(paste("*** Clustering task 2 on ",ncol(medoids)," synchrones\n", sep=""))
nb_series_per_chunk, ncores_clust, verbose, parll)
# B) Compute the WER distances (Wavelets Extended coefficient of deteRmination)
- distances = computeWerDists(synchrones, nbytes, endian, ncores_clust, verbose, parll)
+ distances = computeWerDists(
+ synchrones, nvoice, nbytes, endian, ncores_clust, verbose, parll)
# C) Apply clustering algorithm 2 on the WER distances matrix
if (verbose)
- cat(paste(" algoClust2() on ",nrow(distances)," items\n", sep=""))
+ cat(paste("*** algoClust2() on ",nrow(distances)," items\n", sep=""))
medoids[ ,algoClust2(distances,K2) ]
}
if (parll)
synchronicity::unlock(m)
}
+ NULL
}
K = ncol(medoids) ; L = nrow(medoids)
# Use bigmemory (shared==TRUE by default) + synchronicity to fill synchrones in //
synchrones = bigmemory::big.matrix(nrow=L, ncol=K, type="double", init=0.)
# NOTE: synchronicity is only for Linux & MacOS; on Windows: run sequentially
- parll = (requireNamespace("synchronicity",quietly=TRUE)
- && parll && Sys.info()['sysname'] != "Windows")
+ parll = (parll && requireNamespace("synchronicity",quietly=TRUE)
+ && Sys.info()['sysname'] != "Windows")
if (parll)
{
m <- synchronicity::boost.mutex() #for lock/unlock, see computeSynchronesChunk
#' computeWerDists
#'
-#' Compute the WER distances between the synchrones curves (in rows), which are
+#' 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 rows. The series have same length
-#' as the series in the initial dataset
+#' @param synchrones A big.matrix of synchrones, in columns. The series have same
+#' length as the series in the initial dataset
#' @inheritParams claws
#'
-#' @return A matrix of size K1 x K1
+#' @return A distances matrix of size K1 x K1
#'
#' @export
-computeWerDists = function(synchrones, nbytes,endian,ncores_clust=1,verbose=FALSE,parll=TRUE)
+computeWerDists = function(synchrones, nvoice, nbytes,endian,ncores_clust=1,
+ verbose=FALSE,parll=TRUE)
{
n <- ncol(synchrones)
L <- nrow(synchrones)
- #TODO: automatic tune of all these parameters ? (for other users)
- # 4 here represent 2^5 = 32 half-hours ~ 1 day
- nvoice <- 4
- # noctave = 2^13 = 8192 half hours ~ 180 days ; ~log2(ncol(synchrones))
- noctave = 13
+ noctave = ceiling(log2(L)) #min power of 2 to cover serie range
+ # Initialize result as a square big.matrix of size 'number of synchrones'
Xwer_dist <- bigmemory::big.matrix(nrow=n, ncol=n, type="double")
- cwt_file = ".epclust_bin/cwt"
- #TODO: args, nb_per_chunk, nbytes, endian
-
# Generate n(n-1)/2 pairs for WER distances computations
pairs = list()
V = seq_len(n)
pairs = c(pairs, lapply(V, function(v) c(i,v)))
}
+ cwt_file = ".cwt.bin"
+ # Compute the synchrones[,index] CWT, and store it in the binary file above
computeSaveCWT = function(index)
{
+ if (parll && !exists(synchrones)) #avoid going here after first call on a worker
+ {
+ require("bigmemory", quietly=TRUE)
+ require("Rwave", quietly=TRUE)
+ require("epclust", quietly=TRUE)
+ synchrones <- bigmemory::attach.big.matrix(synchrones_desc)
+ }
ts <- scale(ts(synchrones[,index]), center=TRUE, scale=FALSE)
- totts.cwt = Rwave::cwt(ts, totnoct, nvoice, w0=2*pi, twoD=TRUE, 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,'*')
- res <- sqres / max(Mod(sqres))
- #TODO: serializer les CWT, les récupérer via getDataInFile ;
- #--> OK, faut juste stocker comme séries simples de taille L*n' (53*17519)
- binarize(c(as.double(Re(res)),as.double(Im(res))), cwt_file, ncol(res), ",", nbytes, endian)
+ ts_cwt = Rwave::cwt(ts, noctave, nvoice, w0=2*pi, twoD=TRUE, plot=FALSE)
+
+ # Serialization
+ binarize(as.matrix(c(as.double(Re(ts_cwt)),as.double(Im(ts_cwt)))), cwt_file, 1,
+ ",", nbytes, endian)
}
if (parll)
cl = parallel::makeCluster(ncores_clust)
synchrones_desc <- bigmemory::describe(synchrones)
Xwer_dist_desc <- bigmemory::describe(Xwer_dist)
- parallel::clusterExport(cl, envir=environment(),
- varlist=c("synchrones_desc","Xwer_dist_desc","totnoct","nvoice","w0","s0log",
- "noctave","s0","verbose","getCWT"))
+ parallel::clusterExport(cl, varlist=c("parll","synchrones_desc","Xwer_dist_desc",
+ "noctave","nvoice","verbose","getCWT"), envir=environment())
}
-
+
if (verbose)
- {
- cat(paste("--- Compute WER dists\n", sep=""))
- # precompute save all CWT........
- }
- #precompute and serialize all CWT
+ cat(paste("--- Precompute and serialize synchrones CWT\n", sep=""))
+
ignored <-
if (parll)
parallel::parLapply(cl, 1:n, computeSaveCWT)
else
lapply(1:n, computeSaveCWT)
- getCWT = function(index)
+ # Function to retrieve a synchrone CWT from (binary) file
+ getSynchroneCWT = function(index, L)
{
- #from cwt_file ...
- res <- getDataInFile(c(2*index-1,2*index), cwt_file, nbytes, endian)
- ###############TODO:
+ 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
}
- # Distance between rows i and j
- computeDistancesIJ = function(pair)
+ # Compute distance between columns i and j in synchrones
+ computeDistanceIJ = function(pair)
{
if (parll)
{
+ # parallel workers start with an empty environment
require("bigmemory", quietly=TRUE)
require("epclust", quietly=TRUE)
synchrones <- bigmemory::attach.big.matrix(synchrones_desc)
}
i = pair[1] ; j = pair[2]
- if (verbose && j==i+1)
+ if (verbose && j==i+1 && !parll)
cat(paste(" Distances (",i,",",j,"), (",i,",",j+1,") ...\n", sep=""))
- cwt_i <- getCWT(i)
- cwt_j <- getCWT(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)))
+ # Compute CWT of columns i and j in synchrones
+ L = nrow(synchrones)
+ cwt_i <- getSynchroneCWT(i, L)
+ cwt_j <- getSynchroneCWT(j, L)
+
+ # Compute the ratio of integrals formula 5.6 for WER^2
+ # in https://arxiv.org/abs/1101.4744v2 §5.3
+ num <- filterMA(Mod(cwt_i * Conj(cwt_j)))
+ WX <- filterMA(Mod(cwt_i * Conj(cwt_i)))
+ WY <- filterMA(Mod(cwt_j * Conj(cwt_j)))
wer2 <- sum(colSums(num)^2) / sum(colSums(WX) * colSums(WY))
- Xwer_dist[i,j] <- sqrt(L * ncol(cwt_i) * max(1 - wer2, 0.))
+
+ Xwer_dist[i,j] <- sqrt(L * ncol(cwt_i) * (1 - wer2))
Xwer_dist[j,i] <- Xwer_dist[i,j]
- Xwer_dist[i,i] = 0.
+ Xwer_dist[i,i] <- 0.
}
if (verbose)
- {
- cat(paste("--- Compute WER dists\n", sep=""))
- }
+ cat(paste("--- Compute WER distances\n", sep=""))
+
ignored <-
if (parll)
- parallel::parLapply(cl, pairs, computeDistancesIJ)
+ parallel::parLapply(cl, pairs, computeDistanceIJ)
else
- lapply(pairs, computeDistancesIJ)
+ lapply(pairs, computeDistanceIJ)
if (parll)
parallel::stopCluster(cl)
+ unlink(cwt_file)
+
Xwer_dist[n,n] = 0.
- distances <- Xwer_dist[,]
- rm(Xwer_dist) ; gc()
- distances #~small matrix K1 x K1
+ Xwer_dist[,] #~small matrix K1 x K1
}
# Helper function to divide indices into balanced sets
if (max)
{
# Sets are not so well balanced, but size is supposed to be critical
- return ( c( indices_workers, (L-rem+1):L ) )
+ return ( c( indices_workers, if (rem>0) list((L-rem+1):L) else NULL ) )
}
# Spread the remaining load among the workers
projects / epclust.git / blobdiff