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#' @title Reduce Operation for Batch Systems
#'
#' @description
#' A parallel and asynchronous \code{\link[base]{Reduce}} for batch systems.
#' Note that this function only defines the computational jobs.
#' Each job reduces a certain number of elements on one slave.
#' The actual computation is started with \code{\link{submitJobs}}.
#' Results and partial results can be collected with \code{\link{reduceResultsList}}, \code{\link{reduceResults}} or
#' \code{\link{loadResult}}.
#'
#' @param fun [\code{function(aggr, x, ...)}]\cr
#' Function to reduce \code{xs} with.
#' @param xs [\code{vector}]\cr
#' Vector to reduce.
#' @param init [ANY]\cr
#' Initial object for reducing. See \code{\link[base]{Reduce}}.
#' @param chunks [\code{integer(length(xs))}]\cr
#' Group for each element of \code{xs}. Can be generated with \code{\link{chunk}}.
#' @param more.args [\code{list}]\cr
#' A list of additional arguments passed to \code{fun}.
#' @template reg
#' @return [\code{\link{data.table}}] with ids of added jobs stored in column \dQuote{job.id}.
#' @export
#' @seealso \code{\link{batchMap}}
#' @examples
#' \dontshow{ batchtools:::example_push_temp(1) }
#' # define function to reduce on slave, we want to sum a vector
#' tmp = makeRegistry(file.dir = NA, make.default = FALSE)
#' xs = 1:100
#' f = function(aggr, x) aggr + x
#'
#' # sum 20 numbers on each slave process, i.e. 5 jobs
#' chunks = chunk(xs, chunk.size = 5)
#' batchReduce(fun = f, 1:100, init = 0, chunks = chunks, reg = tmp)
#' submitJobs(reg = tmp)
#' waitForJobs(reg = tmp)
#'
#' # now reduce one final time on master
#' reduceResults(fun = function(aggr, job, res) f(aggr, res), reg = tmp)
batchReduce = function(fun, xs, init = NULL, chunks = seq_along(xs), more.args = list(), reg = getDefaultRegistry()) {
assertRegistry(reg, class = "Registry", writeable = TRUE)
if (nrow(reg$defs) > 0L)
stop("Registry must be empty")
assertFunction(fun, c("aggr", "x"))
assertAtomicVector(xs)
assertIntegerish(chunks, len = length(xs), any.missing = FALSE, lower = 0L)
assertList(more.args, names = "strict")
more.args = c(more.args, list(.fun = fun, .init = init))
batchMap(batchReduceWrapper, unname(split(xs, chunks)), more.args = more.args, reg = reg)
}
batchReduceWrapper = function(xs.block, .fun, .init, ...) {
fun = function(aggr, x) .fun(aggr, x, ...)
Reduce(fun, xs.block, init = .init)
}
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