mlpack_approx_kfn - approximate furthest neighbor search
mlpack_approx_kfn [-a string] [-e bool] [-x string] [-m unknown] [-k int] [-p int] [-t int] [-q string] [-r string] [-V bool] [-d string] [-n string] [-M unknown] [-h -v]
This program implements two strategies for furthest neighbor search. These strategies are:
The ’qdafn’ algorithm from "Approximate Furthest Neighbor in High Dimensions" by R. Pagh, F. Silvestri, J. Sivertsen, and M. Skala, in Similarity Search and Applications 2015 (SISAP).
The ’DrusillaSelect’ algorithm from "Fast approximate furthest neighbors with data-dependent candidate selection", by R.R. Curtin and A.B. Gardner, in Similarity Search and Applications 2016 (SISAP).
These two strategies give approximate results for the furthest neighbor search problem and can be used as fast replacements for other furthest neighbor techniques such as those found in the mlpack_kfn program. Note that typically, the ’ds’ algorithm requires far fewer tables and projections than the ’qdafn’ algorithm.
Specify a reference set (set to search in) with ’--reference_file (-r)’, specify a query set with ’--query_file (-q)’, and specify algorithm parameters with ’--num_tables (-t)’ and ’--num_projections (-p)’ (or don’t and defaults will be used). The algorithm to be used (either ’ds’---the default---or ’qdafn’) may be specified with ’--algorithm (-a)’. Also specify the number of neighbors to search for with ’--k (-k)’.
If no query set is specified, the reference set will be used as the query set. The ’--output_model_file (-M)’ output parameter may be used to store the built model, and an input model may be loaded instead of specifying a reference set with the ’--input_model_file (-m)’ option.
Results for each query point can be stored with the ’--neighbors_file (-n)’ and ’--distances_file (-d)’ output parameters. Each row of these output matrices holds the k distances or neighbor indices for each query point.
For example, to find the 5 approximate furthest neighbors with ’reference_set.csv’ as the reference set and ’query_set.csv’ as the query set using DrusillaSelect, storing the furthest neighbor indices to ’neighbors.csv’ and the furthest neighbor distances to ’distances.csv’, one could call
$ approx_kfn --query_file query_set.csv --reference_file reference_set.csv --k 5 --algorithm ds --neighbors_file neighbors.csv --distances_file distances.csv
and to perform approximate all-furthest-neighbors search with k=1 on the set ’data.csv’ storing only the furthest neighbor distances to ’distances.csv’, one could call
$ approx_kfn --reference_file reference_set.csv --k 1 --distances_file distances.csv
A trained model can be re-used. If a model has been previously saved to ’model.bin’, then we may find 3 approximate furthest neighbors on a query set ’new_query_set.csv’ using that model and store the furthest neighbor indices into ’neighbors.csv’ by calling
$ approx_kfn --input_model_file model.bin --query_file new_query_set.csv --k 3 --neighbors_file neighbors.csv
--algorithm (-a) [string]
Algorithm to use: ’ds’ or ’qdafn’. Default value ’ds’.
--calculate_error (-e) [bool]
If set, calculate the average distance error for the first furthest neighbor only.
--exact_distances_file (-x) [string]
Matrix containing exact distances to furthest neighbors; this can be used to avoid explicit
calculation when --calculate_error is set.
Default value ’’.
--help (-h) [bool]
Default help info.
Get help on a specific module or option. Default value ’’.
--input_model_file (-m) [unknown]
File containing input model. Default value ’’.
--k (-k) [int]
Number of furthest neighbors to search for. Default value 0. --num_projections (-p) [int] Number of projections to use in each hash table. Default value 5.
--num_tables (-t) [int]
Number of hash tables to use. Default value 5.
--query_file (-q) [string]
Matrix containing query points. Default value ’’.
--reference_file (-r) [string]
Matrix containing the reference dataset. Default value ’’.
--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.
--distances_file (-d) [string]
Matrix to save furthest neighbor distances to. Default value ’’.
--neighbors_file (-n) [string]
Matrix to save neighbor indices to. Default value ’’.
--output_model_file (-M) [unknown]
File to save output model to. Default value ’’.
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.