R/nnSearch.R

In nonlinearTseries: Nonlinear Time Series Analysis

# ===============================
# 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)
}