'update'
[epclust.git] / epclust / R / computeWerDists.R
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1#' computeWerDists
2#'
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3#' Compute the WER distances between the series at specified indices, which are
4#' obtaind by \code{getSeries(indices)}
40f12a2f 5#'
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6#' @param indices Indices of the series to consider
7#' @param getSeries Function to retrieve series (argument: 'inds', integer vector),
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8#' as columns of a matrix
9#' @param ncores Number of cores for parallel runs
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10#' @inheritParams claws
11#'
3c5a4b08 12#' @return A distances matrix of size K x K where K == length(indices)
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13#'
14#' @export
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15computeWerDists <- function(indices, getSeries, nb_series_per_chunk, smooth_lvl=3,
16 nvoice=4, nbytes=4, endian=.Platform$endian, ncores=3, verbose=FALSE)
40f12a2f 17{
3c5a4b08 18 n <- length(indices)
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19 L <- length(getSeries(1)) #TODO: not very neat way to get L
20 noctave <- ceiling(log2(L)) #min power of 2 to cover serie range
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21 # Since a CWT contains noctave*nvoice complex series, we deduce the number of CWT to
22 # retrieve/put in one chunk.
282342ba 23 nb_cwt_per_chunk <- max(1, floor(nb_series_per_chunk / (nvoice*noctave*2)))
40f12a2f 24
3c5a4b08 25 # Initialize result as a square big.matrix of size 'number of medoids'
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26 Xwer_dist <- bigmemory::big.matrix(nrow=n, ncol=n, type="double")
27
282342ba 28 cwt_file <- tempfile(pattern="epclust_cwt.bin_")
3c5a4b08 29 # Compute the getSeries(indices) CWT, and store the results in the binary file
e0154a59 30 computeSaveCWT <- function(inds)
40f12a2f 31 {
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32 if (verbose)
33 cat(" Compute save CWT on ",length(inds)," indices\n", sep="")
34
40f12a2f 35 # Obtain CWT as big vectors of real part + imaginary part (concatenate)
e0154a59 36 ts_cwt <- sapply(inds, function(i) {
3c5a4b08 37 ts <- scale(ts(getSeries(i)), center=TRUE, scale=FALSE)
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38 ts_cwt <- Rwave::cwt(ts, noctave, nvoice, w0=2*pi, twoD=TRUE, plot=FALSE)
39 c( as.double(Re(ts_cwt)),as.double(Im(ts_cwt)) )
40 })
41
42 # Serialization
3c5a4b08 43 binarize(ts_cwt, cwt_file, nb_cwt_per_chunk, ",", nbytes, endian)
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44 }
45
40f12a2f 46 # Function to retrieve a synchrone CWT from (binary) file
282342ba 47 getCWT <- function(index, L)
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48 {
49 flat_cwt <- getDataInFile(index, cwt_file, nbytes, endian)
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50 cwt_length <- length(flat_cwt) / 2
51 re_part <- as.matrix(flat_cwt[1:cwt_length], nrow=L)
52 im_part <- as.matrix(flat_cwt[(cwt_length+1):(2*cwt_length)], nrow=L)
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53 re_part + 1i * im_part
54 }
55
dc86eb0c 56 # Compute distances between columns i and j for j>i
282342ba 57 computeDistances <- function(i)
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58 {
59 if (parll)
60 {
61 # parallel workers start with an empty environment
40f12a2f 62 require("epclust", quietly=TRUE)
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63 Xwer_dist <- bigmemory::attach.big.matrix(Xwer_dist_desc)
64 }
65
e0154a59 66 if (verbose)
282342ba 67 cat(paste(" Distances from ",i," to ",i+1,"...",n,"\n", sep=""))
40f12a2f 68
282342ba 69 # Get CWT of column i, and run computations for columns j>i
3c5a4b08 70 cwt_i <- getCWT(i, L)
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71 WX <- filterMA(Mod(cwt_i * Conj(cwt_i)), smooth_lvl)
72
73 for (j in (i+1):n)
74 {
75 cwt_j <- getCWT(j, L)
40f12a2f 76
282342ba 77 # Compute the ratio of integrals formula 5.6 for WER^2
3fb6e823 78 # in https://arxiv.org/abs/1101.4744v2 paragraph 5.3
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79 num <- filterMA(Mod(cwt_i * Conj(cwt_j)), smooth_lvl)
80 WY <- filterMA(Mod(cwt_j * Conj(cwt_j)), smooth_lvl)
81 wer2 <- sum(colSums(num)^2) / sum(colSums(WX) * colSums(WY))
40f12a2f 82
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83 Xwer_dist[i,j] <- sqrt(L * ncol(cwt_i) * (1 - wer2))
84 Xwer_dist[j,i] <- Xwer_dist[i,j]
85 }
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86 Xwer_dist[i,i] <- 0.
87 }
88
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89 if (verbose)
90 cat(paste("--- Precompute and serialize synchrones CWT\n", sep=""))
91
92 # Split indices by packets of length at most nb_cwt_per_chunk
e0154a59 93 indices_cwt <- .splitIndices(indices, nb_cwt_per_chunk)
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94 # NOTE: next loop could potentially be run in //. Indices would be permuted (by
95 # serialization order), and synchronicity would be required because of concurrent
e0154a59 96 # writes. Probably not worth the effort - but possible.
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97 for (inds in indices_cwt)
98 computeSaveCWT(inds)
99
074a48c4 100 parll <- (ncores > 1)
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101 if (parll)
102 {
103 # outfile=="" to see stderr/stdout on terminal
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104 cl <-
105 if (verbose)
dc86eb0c 106 parallel::makeCluster(ncores, outfile="")
3fb6e823 107 else
dc86eb0c 108 parallel::makeCluster(ncores)
282342ba 109 Xwer_dist_desc <- bigmemory::describe(Xwer_dist)
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110 parallel::clusterExport(cl, envir=environment(),
111 varlist=c("parll","n","L","Xwer_dist_desc","getCWT","verbose"))
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112 }
113
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114 if (verbose)
115 cat(paste("--- Compute WER distances\n", sep=""))
116
117 ignored <-
118 if (parll)
57f337af 119 parallel::parLapplyLB(cl, seq_len(n-1), computeDistances)
40f12a2f 120 else
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121 lapply(seq_len(n-1), computeDistances)
122 Xwer_dist[n,n] <- 0.
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123
124 if (parll)
125 parallel::stopCluster(cl)
126
282342ba 127 unlink(cwt_file) #remove binary file
40f12a2f 128
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129 Xwer_dist[,] #~small matrix K1 x K1
130}