Commit | Line | Data |
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3c5a4b08 | 1 | #' Two-stage clustering, within one task (see \code{claws()}) |
4bcfdbee | 2 | #' |
3c5a4b08 BA |
3 | #' \code{clusteringTask1()} runs one full stage-1 task, which consists in iterated |
4 | #' stage 1 clustering on nb_curves / ntasks energy contributions, computed through | |
5 | #' discrete wavelets coefficients. | |
6 | #' \code{clusteringTask2()} runs a full stage-2 task, which consists in WER distances | |
7 | #' computations between medoids (indices) output from stage 1, before applying | |
8 | #' the second clustering algorithm on the distances matrix. | |
4bcfdbee | 9 | #' |
4bcfdbee | 10 | #' @param getContribs Function to retrieve contributions from initial series indices: |
40f12a2f | 11 | #' \code{getContribs(indices)} outputs a contributions matrix |
4bcfdbee | 12 | #' @inheritParams claws |
40f12a2f | 13 | #' @inheritParams computeSynchrones |
3c5a4b08 BA |
14 | #' @inheritParams computeWerDists |
15 | #' | |
16 | #' @return The indices of the computed (resp. K1 and K2) medoids. | |
4bcfdbee | 17 | #' |
3c5a4b08 BA |
18 | #' @name clustering |
19 | #' @rdname clustering | |
20 | #' @aliases clusteringTask1 clusteringTask2 | |
4bcfdbee BA |
21 | NULL |
22 | ||
23 | #' @rdname clustering | |
24 | #' @export | |
282342ba | 25 | clusteringTask1 <- function(indices, getContribs, K1, algoClust1, nb_items_clust, |
37c82bba | 26 | ncores_clust=1, verbose=FALSE, parll=TRUE) |
5c652979 | 27 | { |
492cd9e7 | 28 | if (parll) |
7b13d0c2 | 29 | { |
282342ba BA |
30 | # outfile=="" to see stderr/stdout on terminal |
31 | cl <- parallel::makeCluster(ncores_clust, outfile = "") | |
d9bb53c5 | 32 | parallel::clusterExport(cl, c("getContribs","K1","verbose"), envir=environment()) |
7b13d0c2 | 33 | } |
d9bb53c5 | 34 | # Iterate clustering algorithm 1 until K1 medoids are found |
492cd9e7 BA |
35 | while (length(indices) > K1) |
36 | { | |
d9bb53c5 | 37 | # Balance tasks by splitting the indices set - as evenly as possible |
282342ba | 38 | indices_workers <- .splitIndices(indices, nb_items_clust, min_size=K1+1) |
0486fbad BA |
39 | if (verbose) |
40 | cat(paste("*** [iterated] Clustering task 1 on ",length(indices)," series\n", sep="")) | |
e161499b BA |
41 | indices <- |
42 | if (parll) | |
43 | { | |
44 | unlist( parallel::parLapply(cl, indices_workers, function(inds) { | |
45 | require("epclust", quietly=TRUE) | |
0486fbad | 46 | inds[ algoClust1(getContribs(inds), K1) ] |
e161499b BA |
47 | }) ) |
48 | } | |
49 | else | |
50 | { | |
51 | unlist( lapply(indices_workers, function(inds) | |
0486fbad | 52 | inds[ algoClust1(getContribs(inds), K1) ] |
e161499b BA |
53 | ) ) |
54 | } | |
492cd9e7 BA |
55 | } |
56 | if (parll) | |
57 | parallel::stopCluster(cl) | |
58 | ||
56857861 | 59 | indices #medoids |
5c652979 BA |
60 | } |
61 | ||
4bcfdbee BA |
62 | #' @rdname clustering |
63 | #' @export | |
282342ba BA |
64 | clusteringTask2 <- function(indices, getSeries, K2, algoClust2, nb_series_per_chunk, |
65 | smooth_lvl, nvoice, nbytes, endian, ncores_clust=1, verbose=FALSE, parll=TRUE) | |
5c652979 | 66 | { |
e161499b | 67 | if (verbose) |
3c5a4b08 | 68 | cat(paste("*** Clustering task 2 on ",length(indices)," medoids\n", sep="")) |
d9bb53c5 | 69 | |
3c5a4b08 BA |
70 | if (length(indices) <= K2) |
71 | return (indices) | |
d9bb53c5 | 72 | |
3c5a4b08 | 73 | # A) Compute the WER distances (Wavelets Extended coefficient of deteRmination) |
282342ba BA |
74 | distances <- computeWerDists(indices, getSeries, nb_series_per_chunk, |
75 | smooth_lvl, nvoice, nbytes, endian, ncores_clust, verbose, parll) | |
d9bb53c5 | 76 | |
3c5a4b08 | 77 | # B) Apply clustering algorithm 2 on the WER distances matrix |
e161499b | 78 | if (verbose) |
a52836b2 | 79 | cat(paste("*** algoClust2() on ",nrow(distances)," items\n", sep="")) |
3c5a4b08 | 80 | indices[ algoClust2(distances,K2) ] |
e161499b | 81 | } |