/usr/include/mlpack/methods/radical/radical.hpp is in libmlpack-dev 2.0.1-1.
This file is owned by root:root, with mode 0o644.
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1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 | /**
* @file radical.hpp
* @author Nishant Mehta
*
* Declaration of Radical class (RADICAL is Robust, Accurate, Direct ICA
* aLgorithm).
*
* This file is part of mlpack 2.0.1.
*
* mlpack is free software; you may redstribute it and/or modify it under the
* terms of the 3-clause BSD license. You should have received a copy of the
* 3-clause BSD license along with mlpack. If not, see
* http://www.opensource.org/licenses/BSD-3-Clause for more information.
*/
#ifndef __MLPACK_METHODS_RADICAL_RADICAL_HPP
#define __MLPACK_METHODS_RADICAL_RADICAL_HPP
#include <mlpack/core.hpp>
namespace mlpack {
namespace radical {
/**
* An implementation of RADICAL, an algorithm for independent component
* analysis (ICA).
*
* Let X be a matrix where each column is a point and each row a dimension.
* The goal is to find a square unmixing matrix W such that Y = W X and
* the rows of Y are independent components.
*
* For more details, see the following paper:
*
* @code
* @article{learned2003ica,
* title = {ICA Using Spacings Estimates of Entropy},
* author = {Learned-Miller, E.G. and Fisher III, J.W.},
* journal = {Journal of Machine Learning Research},
* volume = {4},
* pages = {1271--1295},
* year = {2003}
* }
* @endcode
*/
class Radical
{
public:
/**
* Set the parameters to RADICAL.
*
* @param noiseStdDev Standard deviation of the Gaussian noise added to the
* replicates of the data points during Radical2D
* @param replicates Number of Gaussian-perturbed replicates to use (per
* point) in Radical2D
* @param angles Number of angles to consider in brute-force search during
* Radical2D
* @param sweeps Number of sweeps. Each sweep calls Radical2D once for each
* pair of dimensions
* @param m The variable m from Vasicek's m-spacing estimator of entropy.
*/
Radical(const double noiseStdDev = 0.175,
const size_t replicates = 30,
const size_t angles = 150,
const size_t sweeps = 0,
const size_t m = 0);
/**
* Run RADICAL.
*
* @param matX Input data into the algorithm - a matrix where each column is
* a point and each row is a dimension.
* @param matY Estimated independent components - a matrix where each column
* is a point and each row is an estimated independent component.
* @param matW Estimated unmixing matrix, where matY = matW * matX.
*/
void DoRadical(const arma::mat& matX, arma::mat& matY, arma::mat& matW);
/**
* Vasicek's m-spacing estimator of entropy, with overlap modification from
* (Learned-Miller and Fisher, 2003).
*
* @param x Empirical sample (one-dimensional) over which to estimate entropy.
*/
double Vasicek(arma::vec& x) const;
/**
* Make replicates of each data point (the number of replicates is set in
* either the constructor or with Replicates()) and perturb data with Gaussian
* noise with standard deviation noiseStdDev.
*/
void CopyAndPerturb(arma::mat& xNew, const arma::mat& x) const;
//! Two-dimensional version of RADICAL.
double DoRadical2D(const arma::mat& matX);
//! Get the standard deviation of the additive Gaussian noise.
double NoiseStdDev() const { return noiseStdDev; }
//! Modify the standard deviation of the additive Gaussian noise.
double& NoiseStdDev() { return noiseStdDev; }
//! Get the number of Gaussian-perturbed replicates used per point.
size_t Replicates() const { return replicates; }
//! Modify the number of Gaussian-perturbed replicates used per point.
size_t& Replicates() { return replicates; }
//! Get the number of angles considered during brute-force search.
size_t Angles() const { return angles; }
//! Modify the number of angles considered during brute-force search.
size_t& Angles() { return angles; }
//! Get the number of sweeps.
size_t Sweeps() const { return sweeps; }
//! Modify the number of sweeps.
size_t& Sweeps() { return sweeps; }
private:
//! Standard deviation of the Gaussian noise added to the replicates of
//! the data points during Radical2D.
double noiseStdDev;
//! Number of Gaussian-perturbed replicates to use (per point) in Radical2D.
size_t replicates;
//! Number of angles to consider in brute-force search during Radical2D.
size_t angles;
//! Number of sweeps; each sweep calls Radical2D once for each pair of
//! dimensions.
size_t sweeps;
//! Value of m to use for Vasicek's m-spacing estimator of entropy.
size_t m;
//! Internal matrix, held as member variable to prevent memory reallocations.
arma::mat perturbed;
//! Internal matrix, held as member variable to prevent memory reallocations.
arma::mat candidate;
};
void WhitenFeatureMajorMatrix(const arma::mat& matX,
arma::mat& matXWhitened,
arma::mat& matWhitening);
} // namespace radical
} // namespace mlpack
#endif
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