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

Run this code

# 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.

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