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nbcosts <- function(nb, data, method=c("euclidean", "maximum", "manhattan",
"canberra", "binary", "minkowski",
"mahalanobis"), p=2, cov, inverted=FALSE) {
# if ((!require(parallel)) | (length(nb)<300))
# clist <- lapply(1:length(nb), function(i)
# nbcost(data, i, nb[[i]], method,
# p, cov, inverted))
# else {
# if (.Platform$OS.type == "windows") {
# cl <- makeCluster(getOption("cl.cores", 2))
# clusterEvalQ(cl, library(spdep))
if (any(card(nb) == 0L)) stop("nbcosts: no-neighbour nodes")
nc <- n.comp.nb(nb)$nc
if (nc > 1) stop("nbcosts:", nc, "disjoint connected subgraphs")
if (missing(cov)) cov <- NULL
cores <- get.coresOption()
if (is.null(cores)) {
parallel <- "no"
} else {
parallel <- ifelse (get.mcOption(), "multicore", "snow")
}
ncpus <- ifelse(is.null(cores), 1L, cores)
cl <- NULL
if (parallel == "snow") {
cl <- get.ClusterOption()
if (is.null(cl)) {
parallel <- "no"
warning("no cluster in ClusterOption, parallel set to no")
}
}
if (length(nb)<300) parallel <- "no"
if (parallel == "snow") {
if (requireNamespace("parallel", quietly = TRUE)) {
# require(parallel)
sI <- parallel::splitIndices(length(nb), length(cl))
env <- new.env()
assign("nb", nb, envir=env)
assign("data", data, envir=env)
assign("method", method, envir=env)
assign("p", p, envir=env)
assign("cov", cov, envir=env)
assign("inverted", inverted, envir=env)
parallel::clusterExport(cl, varlist=c("nb", "data", "method", "p", "cov",
"inverted"), envir=env)
out <- parallel::clusterApply(cl, x = sI, fun=lapply, function(i) {
nbcost(data, i, nb[[i]], method, p, cov, inverted)})
clist <- do.call("c", out)
rm(env)
} else {
stop("parallel not available")
}
} else if (parallel == "multicore") {
if (requireNamespace("parallel", quietly = TRUE)) {
# require(parallel)
sI <- parallel::splitIndices(length(nb), ncpus)
out <- parallel::mclapply(sI, FUN=lapply, function(i) {nbcost(data, i, nb[[i]],
method, p, cov, inverted)}, mc.cores=ncpus)
clist <- do.call("c", out)
} else {
stop("parallel not available")
}
} else {
clist <- lapply(1:length(nb),
function(i) nbcost(data, i, nb[[i]], method,
p, cov, inverted))
}
attr(clist, "call") <- match.call()
attr(clist, "class") <- "nbdist"
return(clist)
}
nbcost <- function(data, id, id.neigh,
method=c("euclidean", "maximum", "manhattan",
"canberra", "binary", "minkowski",
"mahalanobis"), p=2, cov, inverted=FALSE) {
if (is.function(method))
return(method(data, id, id.neigh))
else {
method <- match.arg(method)
data <- as.matrix(data)
if (method=="mahalanobis")
return(mahalanobis(data[id.neigh,,drop=FALSE], data[id,,drop=FALSE],
cov, inverted))
else
return(dist(rbind(data[id,,drop=FALSE], data[id.neigh,,drop=FALSE]),
method=method,
p=p)[1:length(id.neigh)])
}
}
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