mlpack (version 4.6.0)

fastmks: FastMKS (Fast Max-Kernel Search)

Description

An implementation of the single-tree and dual-tree fast max-kernel search (FastMKS) algorithm. Given a set of reference points and a set of query points, this can find the reference point with maximum kernel value for each query point; trained models can be reused for future queries.

Usage

fastmks(
  bandwidth = NA,
  base = NA,
  degree = NA,
  input_model = NA,
  k = NA,
  kernel = NA,
  naive = FALSE,
  offset = NA,
  query = NA,
  reference = NA,
  scale = NA,
  single = FALSE,
  verbose = getOption("mlpack.verbose", FALSE)
)

Value

A list with several components:

indices

Output matrix of indices (integer matrix).

kernels

Output matrix of kernels (numeric matrix).

output_model

Output for FastMKS model (FastMKSModel).

Arguments

bandwidth

Bandwidth (for Gaussian, Epanechnikov, and triangular kernels). Default value "1" (numeric).

base

Base to use during cover tree construction. Default value "2" (numeric).

degree

Degree of polynomial kernel. Default value "2" (numeric).

input_model

Input FastMKS model to use (FastMKSModel).

k

Number of maximum kernels to find. Default value "0" (integer).

kernel

Kernel type to use: 'linear', 'polynomial', 'cosine', 'gaussian', 'epanechnikov', 'triangular', 'hyptan'. Default value "linear" (character).

naive

If true, O(n^2) naive mode is used for computation. Default value "FALSE" (logical).

offset

Offset of kernel (for polynomial and hyptan kernels). Default value "0" (numeric).

query

The query dataset (numeric matrix).

reference

The reference dataset (numeric matrix).

scale

Scale of kernel (for hyptan kernel). Default value "1" (numeric).

single

If true, single-tree search is used (as opposed to dual-tree search. Default value "FALSE" (logical).

verbose

Display informational messages and the full list of parameters and timers at the end of execution. Default value "getOption("mlpack.verbose", FALSE)" (logical).

Author

mlpack developers

Details

This program will find the k maximum kernels of a set of points, using a query set and a reference set (which can optionally be the same set). More specifically, for each point in the query set, the k points in the reference set with maximum kernel evaluations are found. The kernel function used is specified with the "kernel" parameter.

Examples

# For example, the following command will calculate, for each point in the
# query set "query", the five points in the reference set "reference" with
# maximum kernel evaluation using the linear kernel.  The kernel evaluations
# may be saved with the  "kernels" output parameter and the indices may be
# saved with the "indices" output parameter.

if (FALSE) {
output <- fastmks(k=5, reference=reference, query=query, kernel="linear")
indices <- output$indices
kernels <- output$kernels
}

# The output matrices are organized such that row i and column j in the
# indices matrix corresponds to the index of the point in the reference set
# that has j'th largest kernel evaluation with the point in the query set
# with index i.  Row i and column j in the kernels matrix corresponds to the
# kernel evaluation between those two points.
# 
# This program performs FastMKS using a cover tree.  The base used to build
# the cover tree can be specified with the "base" parameter.