lsh: K-Approximate-Nearest-Neighbor Search with LSH

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

K-Approximate-Nearest-Neighbor Search with LSH

Description

An implementation of approximate k-nearest-neighbor search with locality-sensitive hashing (LSH). Given a set of reference points and a set of query points, this will compute the k approximate nearest neighbors of each query point in the reference set; models can be saved for future use.

Usage

lsh(
  bucket_size = NA,
  hash_width = NA,
  input_model = NA,
  k = NA,
  num_probes = NA,
  projections = NA,
  query = NA,
  reference = NA,
  second_hash_size = NA,
  seed = NA,
  tables = NA,
  true_neighbors = NA,
  verbose = getOption("mlpack.verbose", FALSE)
)

Arguments

bucket_size	The size of a bucket in the second level hash. Default value "500" (integer).
hash_width	The hash width for the first-level hashing in the LSH preprocessing. By default, the LSH class automatically estimates a hash width for its use. Default value "0" (numeric).
input_model	Input LSH model (LSHSearch).
k	Number of nearest neighbors to find. Default value "0" (integer).
num_probes	Number of additional probes for multiprobe LSH; if 0, traditional LSH is used. Default value "0" (integer).
projections	The number of hash functions for each tabl. Default value "10" (integer).
query	Matrix containing query points (optional) (numeric matrix).
reference	Matrix containing the reference dataset (numeric matrix).
second_hash_size	The size of the second level hash table. Default value "99901" (integer).
seed	Random seed. If 0, 'std::time(NULL)' is used. Default value "0" (integer).
tables	The number of hash tables to be used. Default value "30" (integer).
true_neighbors	Matrix of true neighbors to compute recall with (the recall 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 approximate-nearest-neighbors of a set of points using locality-sensitive hashing. 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	Output for trained LSH model (LSHSearch).

Author(s)

mlpack developers

Examples

# For example, the following will return 5 neighbors from the data for each
# point in "input" and store the distances in "distances" and the neighbors
# in "neighbors":

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

## End(Not run)

# The output is organized such that row i and column j in the neighbors
# output corresponds to the index of the point in the reference set which is
# the j'th nearest neighbor from the point in the query set with index i. 
# Row j and column i in the distances output file corresponds to the distance
# between those two points.
# 
# Because this is approximate-nearest-neighbors search, results may be
# different from run to run.  Thus, the "seed" parameter can be specified to
# set the random seed.
# 
# This program also has many other parameters to control its functionality;
# see the parameter-specific documentation for more information.

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