save intermediate

[epclust.git] / epclust / R / clustering.R

diff --git a/epclust/R/clustering.R b/epclust/R/clustering.R
index 7e06c43..c226786 100644 (file)
--- a/epclust/R/clustering.R
+++ b/epclust/R/clustering.R
@@ -121,35 +121,29 @@ computeSynchrones = function(medoids, getRefSeries,
 
        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)
                }
@@ -168,12 +162,11 @@ computeSynchrones = function(medoids, getRefSeries,
                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)
@@ -215,6 +208,15 @@ computeWerDists = function(synchrones, ncores_clust=1,verbose=FALSE,parll=TRUE)
        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)
@@ -232,16 +234,25 @@ computeWerDists = function(synchrones, ncores_clust=1,verbose=FALSE,parll=TRUE)
        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)
@@ -264,21 +275,21 @@ computeWerDists = function(synchrones, ncores_clust=1,verbose=FALSE,parll=TRUE)
                        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.
        }
 
@@ -291,7 +302,7 @@ print(system.time( {
                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)
@@ -300,7 +311,7 @@ browser()
 
        if (parll)
                parallel::stopCluster(cl)
-#browser()
+
        Xwer_dist[n,n] = 0.
        distances <- Xwer_dist[,]
        rm(Xwer_dist) ; gc()