| 1 | # Cluster one full task (nb_curves / ntasks series) |
| 2 | clusteringTask = function(indices, ncores) |
| 3 | { |
| 4 | cl = parallel::makeCluster(ncores) |
| 5 | parallel::clusterExport(cl, |
| 6 | varlist=c("K1","getCoefs"), |
| 7 | envir=environment()) |
| 8 | repeat |
| 9 | { |
| 10 | nb_workers = max( 1, round( length(indices_clust) / nb_series_per_chunk ) ) |
| 11 | indices_workers = lapply(seq_len(nb_workers), function(i) { |
| 12 | upper_bound = ifelse( i<nb_workers, |
| 13 | min(nb_series_per_chunk*i,length(indices_clust)), length(indices_clust) ) |
| 14 | indices_clust[(nb_series_per_chunk*(i-1)+1):upper_bound] |
| 15 | }) |
| 16 | indices_clust = unlist( parallel::parLapply(cl, indices_workers, function(indices) |
| 17 | computeClusters1(indices, getCoefs, K1)) ) |
| 18 | if (length(indices_clust) == K1) |
| 19 | break |
| 20 | } |
| 21 | parallel::stopCluster(cl_clust) |
| 22 | if (WER == "end") |
| 23 | return (indices_clust) |
| 24 | #WER=="mix" |
| 25 | computeClusters2(indices_clust, K2, getSeries, to_file=TRUE) |
| 26 | } |
| 27 | |
| 28 | # Apply the clustering algorithm (PAM) on a coeffs or distances matrix |
| 29 | computeClusters1 = function(indices, getCoefs, K1) |
| 30 | indices[ cluster::pam(getCoefs(indices), K1, diss=FALSE)$id.med ] |
| 31 | |
| 32 | # Cluster a chunk of series inside one task (~max nb_series_per_chunk) |
| 33 | computeClusters2 = function(indices, K2, getSeries, to_file) |
| 34 | { |
| 35 | if (is.null(indices)) |
| 36 | { |
| 37 | #get series from file |
| 38 | } |
| 39 | #Puis K-means après WER... |
| 40 | if (WER=="mix" > 0) |
| 41 | { |
| 42 | curves = computeSynchrones(indices) |
| 43 | dists = computeWerDists(curves) |
| 44 | indices = computeClusters(dists, K2, diss=TRUE) |
| 45 | } |
| 46 | if (to_file) |
| 47 | #write results to file (JUST series ; no possible ID here) |
| 48 | } |
| 49 | |
| 50 | # Compute the synchrones curves (sum of clusters elements) from a clustering result |
| 51 | computeSynchrones = function(inds) |
| 52 | sapply(seq_along(inds), colMeans(getSeries(inds[[i]]$indices,inds[[i]]$ids))) |
| 53 | |
| 54 | # Compute the WER distance between the synchrones curves (in columns) |
| 55 | computeWerDist = function(curves) |
| 56 | { |
| 57 | if (!require("Rwave", quietly=TRUE)) |
| 58 | stop("Unable to load Rwave library") |
| 59 | n <- nrow(curves) |
| 60 | delta <- ncol(curves) |
| 61 | #TODO: automatic tune of all these parameters ? (for other users) |
| 62 | nvoice <- 4 |
| 63 | # noctave = 2^13 = 8192 half hours ~ 180 days ; ~log2(ncol(curves)) |
| 64 | noctave = 13 |
| 65 | # 4 here represent 2^5 = 32 half-hours ~ 1 day |
| 66 | #NOTE: default scalevector == 2^(0:(noctave * nvoice) / nvoice) * s0 (?) |
| 67 | scalevector <- 2^(4:(noctave * nvoice) / nvoice) * 2 |
| 68 | #condition: ( log2(s0*w0/(2*pi)) - 1 ) * nvoice + 1.5 >= 1 |
| 69 | s0=2 |
| 70 | w0=2*pi |
| 71 | scaled=FALSE |
| 72 | s0log = as.integer( (log2( s0*w0/(2*pi) ) - 1) * nvoice + 1.5 ) |
| 73 | totnoct = noctave + as.integer(s0log/nvoice) + 1 |
| 74 | |
| 75 | # (normalized) observations node with CWT |
| 76 | Xcwt4 <- lapply(seq_len(n), function(i) { |
| 77 | ts <- scale(ts(curves[,i]), center=TRUE, scale=scaled) |
| 78 | totts.cwt = Rwave::cwt(ts,totnoct,nvoice,w0,plot=0) |
| 79 | ts.cwt = totts.cwt[,s0log:(s0log+noctave*nvoice)] |
| 80 | #Normalization |
| 81 | sqs <- sqrt(2^(0:(noctave*nvoice)/nvoice)*s0) |
| 82 | sqres <- sweep(ts.cwt,MARGIN=2,sqs,'*') |
| 83 | sqres / max(Mod(sqres)) |
| 84 | }) |
| 85 | |
| 86 | Xwer_dist <- matrix(0., n, n) |
| 87 | fcoefs = rep(1/3, 3) #moving average on 3 values (TODO: very slow! correct?!) |
| 88 | for (i in 1:(n-1)) |
| 89 | { |
| 90 | for (j in (i+1):n) |
| 91 | { |
| 92 | #TODO: later, compute CWT here (because not enough storage space for 200k series) |
| 93 | # 'circular=TRUE' is wrong, should just take values on the sides; to rewrite in C |
| 94 | num <- filter(Mod(Xcwt4[[i]] * Conj(Xcwt4[[j]])), fcoefs, circular=TRUE) |
| 95 | WX <- filter(Mod(Xcwt4[[i]] * Conj(Xcwt4[[i]])), fcoefs, circular=TRUE) |
| 96 | WY <- filter(Mod(Xcwt4[[j]] * Conj(Xcwt4[[j]])), fcoefs, circular=TRUE) |
| 97 | wer2 <- sum(colSums(num)^2) / sum( sum(colSums(WX) * colSums(WY)) ) |
| 98 | Xwer_dist[i,j] <- sqrt(delta * ncol(Xcwt4[[1]]) * (1 - wer2)) |
| 99 | Xwer_dist[j,i] <- Xwer_dist[i,j] |
| 100 | } |
| 101 | } |
| 102 | diag(Xwer_dist) <- numeric(n) |
| 103 | Xwer_dist |
| 104 | } |