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prunecost <- function(edges, data,
method=c("euclidean", "maximum", "manhattan",
"canberra", "binary", "minkowski",
"mahalanobis"), p=2, cov, inverted=FALSE) {
sswt <- ssw(data, unique(as.integer(edges)),
method, p, cov, inverted)
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 (nrow(edges)<300) parallel <- "no"
# if (parallel == "snow") {
# parallel <- "no"
# warning("no parallel calculations available")
# }
if (parallel == "snow") {
if (requireNamespace("parallel", quietly = TRUE)) {
# require(parallel)
sI <- parallel::splitIndices(nrow(edges), length(cl))
# if (.Platform$OS.type == "windows") {
# cl <- makeCluster(getOption("cl.cores", 2))
# clusterEvalQ(cl, library(spdep))
sswp <- do.call("c", parallel::parLapply(cl, sI, sapply, function(i) {
pruned.ids <- prunemst(rbind(edges[i, ], edges[-i, ]),
only.nodes=TRUE)
sum(sapply(pruned.ids, function(j)
ssw(data, j, method, p, cov, inverted)))
}))
} else {
stop("parallel not available")
}
} else if (parallel == "multicore") {
if (requireNamespace("parallel", quietly = TRUE)) {
# require(parallel)
sI <- parallel::splitIndices(nrow(edges), ncpus)
out <- parallel::mclapply(sI, sapply, function(i) {
pruned.ids <- prunemst(rbind(edges[i, ], edges[-i, ]),
only.nodes=TRUE)
sum(sapply(pruned.ids, function(j)
ssw(data, j, method, p, cov, inverted)))
}, mc.cores=ncpus)
sswp <- do.call("c", out)
} else {
stop("parallel not available")
}
} else {
sswp <- sapply(1:nrow(edges), function(i) {
pruned.ids <- prunemst(rbind(edges[i, ], edges[-i, ]),
only.nodes=TRUE)
sum(sapply(pruned.ids, function(j)
ssw(data, j, method, p, cov, inverted)))
})
}
return(sswt - sswp)
}
ssw <- function(data, id, method=c("euclidean", "maximum", "manhattan",
"canberra", "binary", "minkowski",
"mahalanobis"), p=2, cov, inverted=FALSE) {
if (is.function(method))
return(method(data, id))
else {
method <- match.arg(method)
data <- as.matrix(data)
if (method=="mahalanobis")
return(sum(mahalanobis(data[id,,drop=FALSE],
colMeans(data[id,,drop=FALSE]),
cov, inverted)))
else
return(sum(dist(rbind(colMeans(data[id,,drop=FALSE]),
data[id,,drop=FALSE]),
method, p=p)[1:length(id)]))
}
}
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