/usr/include/shogun/statistics/MMDKernelSelectionCombMaxL2.h is in libshogun-dev 3.2.0-7.3build4.
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* This program is free software; you can redistribute it and/or modify
* it under the terms of the GNU General Public License as published by
* the Free Software Foundation; either version 3 of the License, or
* (at your option) any later version.
*
* Written (W) 2013 Heiko Strathmann
*/
#ifndef __MMDKERNELSELECTIONCOMBMAXL2_H_
#define __MMDKERNELSELECTIONCOMBMAXL2_H_
#include <shogun/lib/config.h>
#include <shogun/statistics/MMDKernelSelection.h>
#include <shogun/statistics/MMDKernelSelectionComb.h>
namespace shogun
{
/** @brief Implementation of maximum MMD kernel selection for combined kernel.
* This class selects a combination of baseline kernels that maximises the
* the MMD for a combined kernel based on a L2-regularization approach. This
* boils down to solve the convex program
* \f[
* \min_\beta \{\beta^T \beta \quad \text{s.t.}\quad \beta^T \eta=1, \beta\succeq 0\},
* \f]
* where \f$\eta\f$ is a vector whose elements are the MMDs of the baseline
* kernels.
*
* This is meant to work for the CQuadraticTimeMMD statistic.
* Optimal weight selecton for CLinearTimeMMD can be found in
* CMMDKernelSelectionCombOpt.
*
* The method is described in
* Gretton, A., Sriperumbudur, B., Sejdinovic, D., Strathmann, H.,
* Balakrishnan, S., Pontil, M., & Fukumizu, K. (2012).
* Optimal kernel choice for large-scale two-sample tests.
* Advances in Neural Information Processing Systems.
*/
class CMMDKernelSelectionCombMaxL2: public CMMDKernelSelectionComb
{
public:
/** Default constructor */
CMMDKernelSelectionCombMaxL2();
/** Constructor that initialises the underlying MMD instance
*
* @param mmd MMD instance to use. Has to be an MMD based kernel two-sample
* test. Currently: linear or quadratic time MMD.
*/
CMMDKernelSelectionCombMaxL2(CKernelTwoSampleTestStatistic* mmd);
/** Destructor */
virtual ~CMMDKernelSelectionCombMaxL2();
#ifdef HAVE_LAPACK
/** Computes kernel weights which maximise the MMD of the underlying
* combined kernel using L2-regularization.
*
* This boils down to solving a convex program which is quadratic in the
* number of kernels. See class description.
*
* SHOGUN has to be compiled with LAPACK to make this available. See
* set_opt* methods for optimization parameters.
*
* IMPORTANT: Kernel weights have to be learned on different data than is
* used for testing/evaluation!
*/
virtual SGVector<float64_t> compute_measures();
#endif
/** @return name of the SGSerializable */
const char* get_name() const { return "MMDKernelSelectionCombMaxL2"; }
};
}
#endif /* __MMDKERNELSELECTIONCOMBMAXL2_H_ */
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