merge with remote

[epclust.git] / pkg / R / computeWerDists.R

diff --git a/pkg/R/computeWerDists.R b/pkg/R/computeWerDists.R
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+#' computeWerDists
+#'
+#' Compute the WER distances between the series at specified indices, which are
+#' obtaind by \code{getSeries(indices)}
+#'
+#' @param indices Indices of the series to consider
+#' @param getSeries Function to retrieve series (argument: 'inds', integer vector),
+#'   as columns of a matrix
+#' @param ncores Number of cores for parallel runs
+#' @inheritParams claws
+#'
+#' @return A distances matrix of size K x K where K == length(indices)
+#'
+#' @export
+computeWerDists <- function(indices, getSeries, nb_series_per_chunk, smooth_lvl=3,
+       nvoice=4, nbytes=4, endian=.Platform$endian, ncores=3, verbose=FALSE)
+{
+       n <- length(indices)
+       L <- length(getSeries(1)) #TODO: not very neat way to get L
+       noctave <- ceiling(log2(L)) #min power of 2 to cover serie range
+       # Since a CWT contains noctave*nvoice complex series, we deduce the number of CWT to
+       # retrieve/put in one chunk.
+       nb_cwt_per_chunk <- max(1, floor(nb_series_per_chunk / (nvoice*noctave*2)))
+
+       # Initialize result as a square big.matrix of size 'number of medoids'
+       Xwer_dist <- bigmemory::big.matrix(nrow=n, ncol=n, type="double")
+
+       shift <- 1 #roughly equivalent to s0 in biwavelets & cie. TODO: as arg?
+
+       cwt_file <- tempfile(pattern="epclust_cwt.bin_")
+       # Compute the getSeries(indices) CWT, and store the results in the binary file
+       computeSaveCWT <- function(inds)
+       {
+               if (verbose)
+                       cat("   Compute save CWT on ",length(inds)," indices\n", sep="")
+
+               # Obtain CWT as big vectors of real part + imaginary part (concatenate)
+               ts_cwt <- sapply(inds, function(i) {
+                       ts <- scale(ts(getSeries(i)), center=TRUE, scale=FALSE)
+                       ts_cwt <- Rwave::cwt(ts, noctave+ceiling(shift/nvoice), nvoice,
+                               w0=2*pi, twoD=TRUE, plot=FALSE)
+                       ts_cwt <- ts_cwt[,(1+shift):(noctave*nvoice+shift)]
+                       c( as.double(Re(ts_cwt)),as.double(Im(ts_cwt)) )
+               })
+
+               # Serialization
+               binarize(ts_cwt, cwt_file, nb_cwt_per_chunk, ",", nbytes, endian)
+       }
+
+       # Function to retrieve a synchrone CWT from (binary) file
+       getCWT <- function(index, L)
+       {
+               flat_cwt <- getDataInFile(index, cwt_file, nbytes, endian)
+               cwt_length <- length(flat_cwt) / 2
+               re_part <- as.matrix(flat_cwt[1:cwt_length], nrow=L)
+               im_part <- as.matrix(flat_cwt[(cwt_length+1):(2*cwt_length)], nrow=L)
+               re_part + 1i * im_part
+       }
+
+       # Compute distances between columns i and j for j>i
+       computeDistances <- function(i)
+       {
+               if (parll)
+               {
+                       # parallel workers start with an empty environment
+                       require("epclust", quietly=TRUE)
+                       Xwer_dist <- bigmemory::attach.big.matrix(Xwer_dist_desc)
+               }
+
+               if (verbose)
+                       cat(paste("   Distances from ",i," to ",i+1,"...",n,"\n", sep=""))
+
+               # Get CWT of column i, and run computations for columns j>i
+               cwt_i <- getCWT(i, L)
+               WX  <- filterMA(Mod(cwt_i * Conj(cwt_i)), smooth_lvl)
+
+               for (j in (i+1):n)
+               {
+                       cwt_j <- getCWT(j, L)
+
+                       # Compute the ratio of integrals formula 5.6 for WER^2
+                       # in https://arxiv.org/abs/1101.4744v2 paragraph 5.3
+                       num <- filterMA(Mod(cwt_i * Conj(cwt_j)), smooth_lvl)
+                       WY <- filterMA(Mod(cwt_j * Conj(cwt_j)), smooth_lvl)
+                       wer2 <- sum(colSums(num)^2) / sum(colSums(WX) * colSums(WY))
+
+                       Xwer_dist[i,j] <- sqrt(L * ncol(cwt_i) * (1 - wer2))
+                       Xwer_dist[j,i] <- Xwer_dist[i,j]
+               }
+               Xwer_dist[i,i] <- 0.
+       }
+
+       if (verbose)
+               cat(paste("--- Precompute and serialize synchrones CWT\n", sep=""))
+
+       # Split indices by packets of length at most nb_cwt_per_chunk
+       indices_cwt <- .splitIndices(indices, nb_cwt_per_chunk)
+       # NOTE: next loop could potentially be run in //. Indices would be permuted (by
+       # serialization order), and synchronicity would be required because of concurrent
+       # writes. Probably not worth the effort - but possible.
+       for (inds in indices_cwt)
+               computeSaveCWT(inds)
+
+       parll <- (ncores > 1)
+       if (parll)
+       {
+               # outfile=="" to see stderr/stdout on terminal
+               cl <-
+                       if (verbose)
+                               parallel::makeCluster(ncores, outfile="")
+                       else
+                               parallel::makeCluster(ncores)
+               Xwer_dist_desc <- bigmemory::describe(Xwer_dist)
+               parallel::clusterExport(cl, envir=environment(),
+                       varlist=c("parll","n","L","Xwer_dist_desc","getCWT","verbose"))
+       }
+
+       if (verbose)
+               cat(paste("--- Compute WER distances\n", sep=""))
+
+       ignored <-
+               if (parll)
+                       parallel::clusterApplyLB(cl, seq_len(n-1), computeDistances)
+               else
+                       lapply(seq_len(n-1), computeDistances)
+       Xwer_dist[n,n] <- 0.
+
+       if (parll)
+               parallel::stopCluster(cl)
+
+       unlink(cwt_file) #remove binary file
+
+       Xwer_dist[,] #~small matrix K1 x K1
+}