#' computeWerDists
#'
#' Compute the WER distances between the synchrones curves (in columns), which are
#' returned (e.g.) by \code{computeSynchrones()}
#'
#' @param indices Range of series indices to cluster
#' @inheritParams claws
#' @inheritParams computeSynchrones
#'
#' @return A distances matrix of size K x K where K == length(indices)
#'
#' @export
computeWerDists = function(indices, getSeries, nb_series_per_chunk, nvoice, nbytes, endian,
	ncores_clust=1, verbose=FALSE, parll=TRUE)
{
	n <- length(indices)
	L <- length(getSeries(1)) #TODO: not very nice 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")

	# Generate n(n-1)/2 pairs for WER distances computations
	pairs = list()
	V = seq_len(n)
	for (i in 1:n)
	{
		V = V[-1]
		pairs = c(pairs, lapply(V, function(v) c(i,v)))
	}

	cwt_file = ".cwt.bin"
	# Compute the getSeries(indices) CWT, and store the results in the binary file
	computeSaveCWT = function(indices)
	{
		if (parll)
		{
			require("bigmemory", quietly=TRUE)
			require("Rwave", quietly=TRUE)
			require("epclust", quietly=TRUE)
		}

		# Obtain CWT as big vectors of real part + imaginary part (concatenate)
		ts_cwt <- sapply(indices, function(i) {
			ts <- scale(ts(getSeries(i)), center=TRUE, scale=FALSE)
			ts_cwt <- Rwave::cwt(ts, noctave, nvoice, w0=2*pi, twoD=TRUE, plot=FALSE)
			c( as.double(Re(ts_cwt)),as.double(Im(ts_cwt)) )
		})

		# Serialization
		binarize(ts_cwt, cwt_file, nb_cwt_per_chunk, ",", nbytes, endian)
	}

	if (parll)
	{
		cl = parallel::makeCluster(ncores_clust)
		Xwer_dist_desc <- bigmemory::describe(Xwer_dist)
		parallel::clusterExport(cl, varlist=c("parll","nb_cwt_per_chunk","L",
			"Xwer_dist_desc","noctave","nvoice","getCWT"), envir=environment())
	}

	if (verbose)
		cat(paste("--- Precompute and serialize synchrones CWT\n", sep=""))

	ignored <-
		if (parll)
			parallel::parLapply(cl, 1:n, computeSaveCWT)
		else
			lapply(1:n, computeSaveCWT)

	# 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
	}




#TODO: better repartition here, 



	# Compute distance between columns i and j in synchrones
	computeDistanceIJ = function(pair)
	{
		if (parll)
		{
			# parallel workers start with an empty environment
			require("bigmemory", quietly=TRUE)
			require("epclust", quietly=TRUE)
			Xwer_dist <- bigmemory::attach.big.matrix(Xwer_dist_desc)
		}

		i = pair[1] ; j = pair[2]
		if (verbose && j==i+1 && !parll)
			cat(paste("   Distances (",i,",",j,"), (",i,",",j+1,") ...\n", sep=""))

		# Compute CWT of columns i and j in synchrones
		cwt_i <- getCWT(i, L)
		cwt_j <- getCWT(j, L)

		# Compute the ratio of integrals formula 5.6 for WER^2
		# in https://arxiv.org/abs/1101.4744v2 §5.3
		num <- filterMA(Mod(cwt_i * Conj(cwt_j)))
		WX  <- filterMA(Mod(cwt_i * Conj(cwt_i)))
		WY <- filterMA(Mod(cwt_j * Conj(cwt_j)))
		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("--- Compute WER distances\n", sep=""))

	ignored <-
		if (parll)
			parallel::parLapply(cl, pairs, computeDistanceIJ)
		else
			lapply(pairs, computeDistanceIJ)

	if (parll)
		parallel::stopCluster(cl)

	unlink(cwt_file)

	Xwer_dist[n,n] = 0.
	Xwer_dist[,] #~small matrix K1 x K1
}