computeSynchronesChunk = function(indices)
{
- ref_series = getRefSeries(indices)
- nb_series = nrow(ref_series)
-
if (parll)
{
require("bigmemory", quietly=TRUE)
- require("synchronicity", quietly=TRUE)
+ requireNamespace("synchronicity", quietly=TRUE)
require("epclust", quietly=TRUE)
synchrones <- bigmemory::attach.big.matrix(synchrones_desc)
+ counts <- bigmemory::attach.big.matrix(counts_desc)
medoids <- bigmemory::attach.big.matrix(medoids_desc)
m <- synchronicity::attach.mutex(m_desc)
}
+ ref_series = getRefSeries(indices)
+ nb_series = nrow(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()
for (i in seq_len(nb_series))
{
if (parll)
synchronicity::lock(m)
- 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?
if (parll)
synchronicity::unlock(m)
}
m <- synchronicity::boost.mutex()
m_desc <- synchronicity::describe(m)
synchrones_desc = bigmemory::describe(synchrones)
+ counts_desc = bigmemory::describe(counts)
medoids_desc = bigmemory::describe(medoids)
-
cl = parallel::makeCluster(ncores_clust)
- 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())
}
indices_workers = .spreadIndices(seq_len(nb_ref_curves), nb_series_per_chunk)
if (verbose)
cat(paste("--- Compute WER dists\n", sep=""))
+
+
+
+#TODO: serializer les CWT, les récupérer via getDataInFile
+#--> OK, faut juste stocker comme séries simples de taille delta*ncol (53*17519)
+
+
+
+
n <- nrow(synchrones)
delta <- ncol(synchrones)
#TODO: automatic tune of all these parameters ? (for other users)
totnoct = noctave + as.integer(s0log/nvoice) + 1
Xwer_dist <- bigmemory::big.matrix(nrow=n, ncol=n, type="double")
- fcoefs = rep(1/3, 3) #moving average on 3 values
# Generate n(n-1)/2 pairs for WER distances computations
+# pairs = list()
+# V = seq_len(n)
+# for (i in 1:n)
+# {
+# V = V[-1]
+# pairs = c(pairs, lapply(V, function(v) c(i,v)))
+# }
+ # Generate "smart" pairs for WER distances computations
pairs = list()
- V = seq_len(n)
- for (i in 1:n)
+ F = floor(2*n/3)
+ for (i in 1:F)
+ pairs = c(pairs, lapply((i+1):n, function(v) c(i,v)))
+ V = (F+1):n
+ for (i in (F+1):(n-1))
{
V = V[-1]
- pairs = c(pairs, lapply(V, function(v) c(i,v)))
- }
+ pairs = c(pairs,
# Distance between rows i and j
computeDistancesIJ = function(pair)
sqres <- sweep(ts.cwt,2,sqs,'*')
sqres / max(Mod(sqres))
}
-#browser()
+
i = pair[1] ; j = pair[2]
if (verbose && j==i+1)
cat(paste(" Distances (",i,",",j,"), (",i,",",j+1,") ...\n", sep=""))
-print(system.time( { cwt_i <- computeCWT(i)
- cwt_j <- computeCWT(j) } ))
+ cwt_i <- computeCWT(i)
+ cwt_j <- computeCWT(j)
-print(system.time( {
+#print(system.time( {
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]
-} ) )
+#} ) )
Xwer_dist[i,i] = 0.
}
parallel::clusterExport(cl, varlist=c("synchrones_desc","Xwer_dist_desc","totnoct",
"nvoice","w0","s0log","noctave","s0","verbose"), envir=environment())
}
-browser()
+
ignored <-
if (parll)
parallel::parLapply(cl, pairs, computeDistancesIJ)
if (parll)
parallel::stopCluster(cl)
-#browser()
+
Xwer_dist[n,n] = 0.
distances <- Xwer_dist[,]
rm(Xwer_dist) ; gc()
--- /dev/null
+context("computeMedoidsIndices")
+
+test_that("serialization + getDataInFile retrieve original data / from matrix",
+{
+ data_bin_file = "/tmp/epclust_test_m.bin"
+ unlink(data_bin_file)
+
+ #dataset 200 lignes / 30 columns
+ data_ascii = matrix(runif(200*30,-10,10),ncol=30)
+ nbytes = 4 #lead to a precision of 1e-7 / 1e-8
+ endian = "little"
+
+ #Simulate serialization in one single call
+ binarize(data_ascii, data_bin_file, 500, ",", nbytes, endian)
+ expect_equal(file.info(data_bin_file)$size, length(data_ascii)*nbytes+8)
+ for (indices in list(c(1,3,5), 3:13, c(5,20,50), c(75,130:135), 196:200))
+ {
+ data_lines = getDataInFile(indices, data_bin_file, nbytes, endian)
+ expect_equal(data_lines, data_ascii[indices,], tolerance=1e-6)
+ }
+ unlink(data_bin_file)
+
+ #...in several calls (last call complete, next call NULL)
+ for (i in 1:20)
+ binarize(data_ascii[((i-1)*10+1):(i*10),], data_bin_file, 20, ",", nbytes, endian)
+ expect_equal(file.info(data_bin_file)$size, length(data_ascii)*nbytes+8)
+ for (indices in list(c(1,3,5), 3:13, c(5,20,50), c(75,130:135), 196:200))
+ {
+ data_lines = getDataInFile(indices, data_bin_file, nbytes, endian)
+ expect_equal(data_lines, data_ascii[indices,], tolerance=1e-6)
+ }
+ unlink(data_bin_file)
+})
+
+TODO: test computeMedoids + filter
+# #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()
+
projects / epclust.git / commitdiff