KernelKnn (version 1.1.5)

distMat.knn.index.dist: indices and distances of k-nearest-neighbors using a distance matrix

Description

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
)

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.

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.

Author

Lampros Mouselimis

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.

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)