R: k-Furthest-Neighbors Search

kfn {mlpack} | R Documentation

## 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 = 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 "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.

[Package mlpack version 3.4.2.1 Index]