indices and distances of k-nearest-neighbors using a distance matrix

Usage

distMat.knn.index.dist(
  DIST_mat,
  TEST_indices = NULL,
  k = 5,
  threads = 1,
  minimize = T
)

Arguments

DIST_mat: a distance matrix (square matrix) having a diagonal filled with either zero's (0) or NA's (missing values)
TEST_indices: a numeric vector specifying the indices of the test data in the distance matrix (row-wise or column-wise). If the parameter equals NULL then no test data is included in the distance matrix
k: an integer specifying the k-nearest-neighbors
threads: the number of cores to be used in parallel (openmp will be employed)
minimize: either TRUE or FALSE. If TRUE then lower values will be considered as relevant for the k-nearest search, otherwise higher values.

Value

a list of length 2. The first sublist returns the indices and the second the distances of the k nearest neighbors for each observation. If TEST_indices is NULL the number of rows of each sublist equals the number of rows in the DIST_mat data. If TEST_indices is not NULL the number of rows of each sublist equals the length of the input TEST_indices.

Details

This function takes a number of arguments and it returns the indices and distances of the k-nearest-neighbors for each observation. If TEST_indices is NULL then the indices-distances for the DIST_mat be returned, whereas if TEST_indices is not NULL then the indices-distances for the test data only will be returned.

Author

Lampros Mouselimis

Examples


data(Boston)

X = Boston[, -ncol(Boston)]

dist_obj = dist(X)

dist_mat = as.matrix(dist_obj)

out = distMat.knn.index.dist(dist_mat, TEST_indices = NULL, k = 5)