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# ===============================
# private function
# NEAR NEIGHBOUR FINDER based on ANN C++ library and RANN functions
# This function is a modified version of the nn2.R function of the RANN package,
# copyrigth under Samuel Kemp 2005-9 and Gregory Jefferis 2009-2013.
# Date of last edit: 17-09-2013
# ===============================
nn.search = function(data, query, k = min(10, nrow(data)),
treetype = c("kd", "bd"),
searchtype = c("standard", "priority", "radius"),
radius = 1.0, eps = 0.0) {
dimension = ncol(data)
ND = nrow(data)
NQ = nrow(query)
# Check that both datasets have same dimensionality
if (ncol(data) != ncol(query)) {
stop("Query and data tables must have same dimensions")
}
if (k > ND) {
stop("Cannot find more nearest neighbours than there are points")
}
searchtypeInt = pmatch(searchtype[1], c("standard", "priority", "radius"))
if (is.na(searchtypeInt)) {
stop(paste("Unknown search type", searchtype))
}
treetype = match.arg(treetype, c("kd", "bd"))
# Coerce to matrix form
if (is.data.frame(data)) {
data = unlist(data, use.names = FALSE)
}
# Coerce to matrix form
if (!is.matrix(query)) {
query = unlist(query, use.names = FALSE)
}
results = .Call(
"_nonlinearTseries_get_NN_2Set_wrapper",
data, query, dimension, ND, NQ, as.integer(k), as.double(eps),
as.integer(searchtypeInt), as.integer(treetype == "bd"),
as.double(radius * radius), nn.idx = integer(k * NQ),
nn = double(k * NQ), PACKAGE = "nonlinearTseries"
)
# now put the returned vectors into (nq x k) arrays
nn.indexes = matrix(results$nn_index, ncol = k, byrow = TRUE)
nn.dist = matrix(results$distances, ncol = k, byrow = TRUE)
list(nn.idx = nn.indexes, nn.dists = nn.dist ^ 2)
}
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