| 1 | oneIteration = function(..........) |
| 2 | { |
| 3 | cl_clust = parallel::makeCluster(ncores_clust) |
| 4 | parallel::clusterExport(cl_clust, .............., envir=........) |
| 5 | indices_clust = indices_task[[i]] |
| 6 | repeat |
| 7 | { |
| 8 | nb_workers = max( 1, round( length(indices_clust) / nb_series_per_chunk ) ) |
| 9 | indices_workers = list() |
| 10 | #indices[[i]] == (start_index,number_of_elements) |
| 11 | for (i in 1:nb_workers) |
| 12 | { |
| 13 | upper_bound = ifelse( i<nb_workers, |
| 14 | min(nb_series_per_chunk*i,length(indices_clust)), length(indices_clust) ) |
| 15 | indices_workers[[i]] = indices_clust[(nb_series_per_chunk*(i-1)+1):upper_bound] |
| 16 | } |
| 17 | indices_clust = parallel::parSapply(cl, indices_workers, processChunk, K1, K2*(WER=="mix")) |
| 18 | if ( (WER=="end" && length(indices_clust) == K1) || |
| 19 | (WER=="mix" && length(indices_clust) == K2) ) |
| 20 | { |
| 21 | break |
| 22 | } |
| 23 | } |
| 24 | parallel::stopCluster(cl_clust) |
| 25 | res_clust |
| 26 | } |
| 27 | |
| 28 | processChunk = function(indices, K1, K2) |
| 29 | { |
| 30 | #1) retrieve data (coeffs) |
| 31 | coeffs = getCoeffs(indices) |
| 32 | #2) cluster |
| 33 | cl = computeClusters(as.matrix(coeffs[,2:ncol(coeffs)]), K1) |
| 34 | #3) WER (optional) |
| 35 | if (K2 > 0) |
| 36 | { |
| 37 | curves = computeSynchrones(cl) |
| 38 | dists = computeWerDists(curves) |
| 39 | cl = computeClusters(dists, K2) |
| 40 | } |
| 41 | cl |
| 42 | } |
| 43 | |
| 44 | computeClusters = function(data, K) |
| 45 | { |
| 46 | library(cluster) |
| 47 | pam_output = cluster::pam(data, K) |
| 48 | return ( list( clusts=pam_output$clustering, medoids=pam_output$medoids, |
| 49 | ranks=pam_output$id.med ) ) |
| 50 | } |
| 51 | |
| 52 | #TODO: appendCoeffs() en C --> serialize et append to file |
| 53 | |
| 54 | computeSynchrones = function(...) |
| 55 | { |
| 56 | |
| 57 | } |
| 58 | |
| 59 | #Entrée : courbes synchrones, soit après étape 1 itérée, soit après chaqure étape 1 |
| 60 | computeWerDist = function(conso) |
| 61 | { |
| 62 | if (!require("Rwave", quietly=TRUE)) |
| 63 | stop("Unable to load Rwave library") |
| 64 | n <- nrow(conso) |
| 65 | delta <- ncol(conso) |
| 66 | #TODO: automatic tune of all these parameters ? (for other users) |
| 67 | nvoice <- 4 |
| 68 | # noctave = 2^13 = 8192 half hours ~ 180 days ; ~log2(ncol(conso)) |
| 69 | noctave = 13 |
| 70 | # 4 here represent 2^5 = 32 half-hours ~ 1 day |
| 71 | #NOTE: default scalevector == 2^(0:(noctave * nvoice) / nvoice) * s0 (?) |
| 72 | scalevector <- 2^(4:(noctave * nvoice) / nvoice) * 2 |
| 73 | #condition: ( log2(s0*w0/(2*pi)) - 1 ) * nvoice + 1.5 >= 1 |
| 74 | s0=2 |
| 75 | w0=2*pi |
| 76 | scaled=FALSE |
| 77 | s0log = as.integer( (log2( s0*w0/(2*pi) ) - 1) * nvoice + 1.5 ) |
| 78 | totnoct = noctave + as.integer(s0log/nvoice) + 1 |
| 79 | |
| 80 | # (normalized) observations node with CWT |
| 81 | Xcwt4 <- lapply(seq_len(n), function(i) { |
| 82 | ts <- scale(ts(conso[i,]), center=TRUE, scale=scaled) |
| 83 | totts.cwt = Rwave::cwt(ts,totnoct,nvoice,w0,plot=0) |
| 84 | ts.cwt = totts.cwt[,s0log:(s0log+noctave*nvoice)] |
| 85 | #Normalization |
| 86 | sqs <- sqrt(2^(0:(noctave*nvoice)/nvoice)*s0) |
| 87 | sqres <- sweep(ts.cwt,MARGIN=2,sqs,'*') |
| 88 | sqres / max(Mod(sqres)) |
| 89 | }) |
| 90 | |
| 91 | Xwer_dist <- matrix(0., n, n) |
| 92 | fcoefs = rep(1/3, 3) #moving average on 3 values (TODO: very slow! correct?!) |
| 93 | for (i in 1:(n-1)) |
| 94 | { |
| 95 | for (j in (i+1):n) |
| 96 | { |
| 97 | #TODO: later, compute CWT here (because not enough storage space for 32M series) |
| 98 | # 'circular=TRUE' is wrong, should just take values on the sides; to rewrite in C |
| 99 | num <- filter(Mod(Xcwt4[[i]] * Conj(Xcwt4[[j]])), fcoefs, circular=TRUE) |
| 100 | WX <- filter(Mod(Xcwt4[[i]] * Conj(Xcwt4[[i]])), fcoefs, circular=TRUE) |
| 101 | WY <- filter(Mod(Xcwt4[[j]] * Conj(Xcwt4[[j]])), fcoefs, circular=TRUE) |
| 102 | wer2 <- sum(colSums(num)^2) / sum( sum(colSums(WX) * colSums(WY)) ) |
| 103 | Xwer_dist[i,j] <- sqrt(delta * ncol(Xcwt4[[1]]) * (1 - wer2)) |
| 104 | Xwer_dist[j,i] <- Xwer_dist[i,j] |
| 105 | } |
| 106 | } |
| 107 | diag(Xwer_dist) <- numeric(n) |
| 108 | Xwer_dist |
| 109 | } |