kfn: k-Furthest-Neighbors Search

In mlpack: 'Rcpp' Integration for the 'mlpack' Library

k-Furthest-Neighbors Search

Description

An implementation of k-furthest-neighbor search using single-tree and dual-tree algorithms. Given a set of reference points and query points, this can find the k furthest neighbors in the reference set of each query point using trees; trees that are built can be saved for future use.

Usage

kfn(
  algorithm = NA,
  epsilon = NA,
  input_model = NA,
  k = NA,
  leaf_size = NA,
  percentage = NA,
  query = NA,
  random_basis = FALSE,
  reference = NA,
  seed = NA,
  tree_type = NA,
  true_distances = NA,
  true_neighbors = NA,
  verbose = getOption("mlpack.verbose", FALSE)
)

Arguments

algorithm	Type of neighbor search: 'naive', 'single_tree', 'dual_tree', 'greedy'. Default value "dual_tree" (character).
epsilon	If specified, will do approximate furthest neighbor search with given relative error. Must be in the range [0,1). Default value "0" (numeric).
input_model	Pre-trained kFN model (KFNModel).
k	Number of furthest neighbors to find. Default value "0" (integer).
leaf_size	Leaf size for tree building (used for kd-trees, vp trees, random projection trees, UB trees, R trees, R* trees, X trees, Hilbert R trees, R+ trees, R++ trees, and octrees). Default value "20" (integer).
percentage	If specified, will do approximate furthest neighbor search. Must be in the range (0,1] (decimal form). Resultant neighbors will be at least (p*100) Default value "1" (numeric).
query	Matrix containing query points (optional) (numeric matrix).
random_basis	Before tree-building, project the data onto a random orthogonal basis. Default value "FALSE" (logical).
reference	Matrix containing the reference dataset (numeric matrix).
seed	Random seed (if 0, std::time(NULL) is used). Default value "0" (integer).
tree_type	Type of tree to use: 'kd', 'vp', 'rp', 'max-rp', 'ub', 'cover', 'r', 'r-star', 'x', 'ball', 'hilbert-r', 'r-plus', 'r-plus-plus', 'oct'. Default value "kd" (character).
true_distances	Matrix of true distances to compute the effective error (average relative error) (it is printed when -v is specified) (numeric matrix).
true_neighbors	Matrix of true neighbors to compute the recall (it is printed when -v is specified) (integer matrix).
verbose	Display informational messages and the full list of parameters and timers at the end of execution. Default value "getOption("mlpack.verbose", FALSE)" (logical).

Details

This program will calculate the k-furthest-neighbors of a set of points. You may specify a separate set of reference points and query points, or just a reference set which will be used as both the reference and query set.

Value

A list with several components:

distances	Matrix to output distances into (numeric matrix).
neighbors	Matrix to output neighbors into (integer matrix).
output_model	If specified, the kFN model will be output here (KFNModel).

Author(s)

mlpack developers

Examples

# For example, the following will calculate the 5 furthest neighbors of
# eachpoint in "input" and store the distances in "distances" and the
# neighbors in "neighbors": 

## Not run: 
output <- kfn(k=5, reference=input)
distances <- output$distances
neighbors <- output$neighbors

## End(Not run)

# The output files are organized such that row i and column j in the
# neighbors output matrix corresponds to the index of the point in the
# reference set which is the j'th furthest neighbor from the point in the
# query set with index i.  Row i and column j in the distances output file
# corresponds to the distance between those two points.

mlpack documentation built on Oct. 5, 2024, 9:08 a.m.