mlpack_kfn - k-furthest-neighbors search
mlpack_kfn [-a string] [-e double] [-m unknown] [-k int] [-l int] [-p double] [-q string] [-R bool] [-r string] [-s int] [-t string] [-D string] [-T string] [-V bool] [-d string] [-n string] [-M unknown] [-h -v]
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.
For example, the following will calculate the 5 furthest neighbors
of eachpoint in 'input.csv' and store the distances in 'distances.csv' and
the neighbors in 'neighbors.csv':
$ mlpack_kfn --k 5 --reference_file input.csv
--distances_file distances.csv --neighbors_file
neighbors.csv
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.
- --algorithm
(-a) [string]
- Type of neighbor search: 'naive', 'single_tree', 'dual_tree', 'greedy'.
Default value 'dual_tree'.
- --epsilon
(-e) [double]
- If specified, will do approximate furthest neighbor search with given
relative error. Must be in the range [0,1). Default value 0.
- --help (-h)
[bool]
- Default help info.
- --info
[string]
- Print help on a specific option. Default value ''.
- --input_model_file
(-m) [unknown]
- Pre-trained kFN model.
- --k (-k)
[int]
- Number of furthest neighbors to find. Default value 0.
- --leaf_size
(-l) [int]
- 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.
- --percentage
(-p) [double]
- 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) %
of the distance as the true furthest neighbor. Default value 1.
- --query_file
(-q) [string]
- Matrix containing query points (optional).
- --random_basis
(-R) [bool]
- Before tree-building, project the data onto a random orthogonal
basis.
- --reference_file
(-r) [string]
- Matrix containing the reference dataset.
- --seed (-s)
[int]
- Random seed (if 0, std::time(NULL) is used). Default value 0.
- --tree_type
(-t) [string]
- 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'.
- --true_distances_file
(-D) [string]
- Matrix of true distances to compute the effective error (average relative
error) (it is printed when -v is specified).
- --true_neighbors_file
(-T) [string]
- Matrix of true neighbors to compute the recall (it is printed when
-v is specified).
- --verbose
(-v) [bool]
- Display informational messages and the full list of parameters and timers
at the end of execution.
- --version
(-V) [bool]
- Display the version of mlpack.
For further information, including relevant papers, citations, and
theory, consult the documentation found at http://www.mlpack.org or included
with your distribution of mlpack.