kfn | R Documentation |

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.

```
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)
)
```

`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). |

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.

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). |

mlpack developers

```
# 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.
```

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