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[epclust.git] / epclust / R / clustering.R
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1#' @name clustering
2#' @rdname clustering
3#' @aliases clusteringTask computeClusters1 computeClusters2
4#'
5#' @title Two-stages clustering, withing one task (see \code{claws()})
6#'
7#' @description \code{clusteringTask()} runs one full task, which consists in iterated stage 1
8#' clustering (on nb_curves / ntasks energy contributions, computed through discrete
9#' wavelets coefficients). \code{computeClusters1()} and \code{computeClusters2()}
10#' correspond to the atomic clustering procedures respectively for stage 1 and 2.
11#' The former applies the clustering algorithm (PAM) on a contributions matrix, while
12#' the latter clusters a chunk of series inside one task (~max nb_series_per_chunk)
13#'
14#' @param indices Range of series indices to cluster in parallel (initial data)
15#' @param getContribs Function to retrieve contributions from initial series indices:
16#' \code{getContribs(indices)} outpus a contributions matrix
17#' @param contribs matrix of contributions (e.g. output of \code{curvesToContribs()})
18#' @inheritParams computeSynchrones
19#' @inheritParams claws
20#'
21#' @return For \code{clusteringTask()} and \code{computeClusters1()}, the indices of the
22#' computed (K1) medoids. Indices are irrelevant for stage 2 clustering, thus
23#' \code{computeClusters2()} outputs a matrix of medoids
24#' (of size limited by nb_series_per_chunk)
25NULL
26
27#' @rdname clustering
28#' @export
29clusteringTask = function(indices, getContribs, K1, nb_series_per_chunk, ncores_clust)
30{
31
32#NOTE: comment out parallel sections for debugging
33#propagate verbose arg ?!
34
35# cl = parallel::makeCluster(ncores_clust)
36# parallel::clusterExport(cl, varlist=c("getContribs","K1"), envir=environment())
37 repeat
38 {
39
40print(length(indices))
41
42 nb_workers = max( 1, floor( length(indices) / nb_series_per_chunk ) )
43 indices_workers = lapply( seq_len(nb_workers), function(i)
44 indices[(nb_series_per_chunk*(i-1)+1):(nb_series_per_chunk*i)] )
45 # Spread the remaining load among the workers
46 rem = length(indices) %% nb_series_per_chunk
47 while (rem > 0)
48 {
49 index = rem%%nb_workers + 1
50 indices_workers[[index]] = c(indices_workers[[index]], tail(indices,rem))
51 rem = rem - 1
52 }
53# indices = unlist( parallel::parLapply( cl, indices_workers, function(inds) {
54 indices = unlist( lapply( indices_workers, function(inds) {
55# require("epclust", quietly=TRUE)
56
57print(paste(" ",length(inds))) ## PROBLEME ICI : 21104 ??!
58
59 inds[ computeClusters1(getContribs(inds), K1) ]
60 } ) )
61 if (length(indices) == K1)
62 break
63 }
64# parallel::stopCluster(cl)
65 indices #medoids
66}
67
68#' @rdname clustering
69#' @export
70computeClusters1 = function(contribs, K1)
71 cluster::pam(contribs, K1, diss=FALSE)$id.med
72
73#' @rdname clustering
74#' @export
75computeClusters2 = function(medoids, K2, getRefSeries, nb_series_per_chunk)
76{
77 synchrones = computeSynchrones(medoids, getRefSeries, nb_series_per_chunk)
78 medoids[ cluster::pam(computeWerDists(synchrones), K2, diss=TRUE)$medoids , ]
79}
80
81#' computeSynchrones
82#'
83#' Compute the synchrones curves (sum of clusters elements) from a matrix of medoids,
84#' using L2 distances.
85#'
86#' @param medoids Matrix of medoids (curves of same legnth as initial series)
87#' @param getRefSeries Function to retrieve initial series (e.g. in stage 2 after series
88#' have been replaced by stage-1 medoids)
89#' @inheritParams claws
90#'
91#' @export
92computeSynchrones = function(medoids, getRefSeries, nb_series_per_chunk)
93{
94 K = nrow(medoids)
95 synchrones = matrix(0, nrow=K, ncol=ncol(medoids))
96 counts = rep(0,K)
97 index = 1
98 repeat
99 {
100 range = (index-1) + seq_len(nb_series_per_chunk)
101 ref_series = getRefSeries(range)
102 if (is.null(ref_series))
103 break
104 #get medoids indices for this chunk of series
105 for (i in seq_len(nrow(ref_series)))
106 {
107 j = which.min( rowSums( sweep(medoids, 2, ref_series[i,], '-')^2 ) )
108 synchrones[j,] = synchrones[j,] + ref_series[i,]
109 counts[j] = counts[j] + 1
110 }
111 index = index + nb_series_per_chunk
112 }
113 #NOTE: odds for some clusters to be empty? (when series already come from stage 2)
114 # ...maybe; but let's hope resulting K1' be still quite bigger than K2
115 synchrones = sweep(synchrones, 1, counts, '/')
116 synchrones[ sapply(seq_len(K), function(i) all(!is.nan(synchrones[i,]))) , ]
117}
118
119#' computeWerDists
120#'
121#' Compute the WER distances between the synchrones curves (in rows), which are
122#' returned (e.g.) by \code{computeSynchrones()}
123#'
124#' @param synchrones A matrix of synchrones, in rows. The series have same length as the
125#' series in the initial dataset
126#'
127#' @export
128computeWerDists = function(synchrones)
129{
130 n <- nrow(synchrones)
131 delta <- ncol(synchrones)
132 #TODO: automatic tune of all these parameters ? (for other users)
133 nvoice <- 4
134 # noctave = 2^13 = 8192 half hours ~ 180 days ; ~log2(ncol(synchrones))
135 noctave = 13
136 # 4 here represent 2^5 = 32 half-hours ~ 1 day
137 #NOTE: default scalevector == 2^(0:(noctave * nvoice) / nvoice) * s0 (?)
138 scalevector <- 2^(4:(noctave * nvoice) / nvoice) * 2
139 #condition: ( log2(s0*w0/(2*pi)) - 1 ) * nvoice + 1.5 >= 1
140 s0=2
141 w0=2*pi
142 scaled=FALSE
143 s0log = as.integer( (log2( s0*w0/(2*pi) ) - 1) * nvoice + 1.5 )
144 totnoct = noctave + as.integer(s0log/nvoice) + 1
145
146 # (normalized) observations node with CWT
147 Xcwt4 <- lapply(seq_len(n), function(i) {
148 ts <- scale(ts(synchrones[i,]), center=TRUE, scale=scaled)
149 totts.cwt = Rwave::cwt(ts,totnoct,nvoice,w0,plot=0)
150 ts.cwt = totts.cwt[,s0log:(s0log+noctave*nvoice)]
151 #Normalization
152 sqs <- sqrt(2^(0:(noctave*nvoice)/nvoice)*s0)
153 sqres <- sweep(ts.cwt,MARGIN=2,sqs,'*')
154 sqres / max(Mod(sqres))
155 })
156
157 Xwer_dist <- matrix(0., n, n)
158 fcoefs = rep(1/3, 3) #moving average on 3 values (TODO: very slow! correct?!)
159 for (i in 1:(n-1))
160 {
161 for (j in (i+1):n)
162 {
163 #TODO: later, compute CWT here (because not enough storage space for 200k series)
164 # 'circular=TRUE' is wrong, should just take values on the sides; to rewrite in C
165 num <- filter(Mod(Xcwt4[[i]] * Conj(Xcwt4[[j]])), fcoefs, circular=TRUE)
166 WX <- filter(Mod(Xcwt4[[i]] * Conj(Xcwt4[[i]])), fcoefs, circular=TRUE)
167 WY <- filter(Mod(Xcwt4[[j]] * Conj(Xcwt4[[j]])), fcoefs, circular=TRUE)
168 wer2 <- sum(colSums(num)^2) / sum( sum(colSums(WX) * colSums(WY)) )
169 Xwer_dist[i,j] <- sqrt(delta * ncol(Xcwt4[[1]]) * (1 - wer2))
170 Xwer_dist[j,i] <- Xwer_dist[i,j]
171 }
172 }
173 diag(Xwer_dist) <- numeric(n)
174 Xwer_dist
175}