/usr/include/shogun/classifier/mkl/MKLMulticlassOptimizationBase.h is in libshogun-dev 3.2.0-7.3build4.
This file is owned by root:root, with mode 0o644.
The actual contents of the file can be viewed below.
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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) 2009 Alexander Binder
* Copyright (C) 2009 Fraunhofer Institute FIRST and Max-Planck-Society
*/
#ifndef MKLMulticlassOPTIMIZATIONBASE_H_
#define MKLMulticlassOPTIMIZATIONBASE_H_
#include <vector>
#include <shogun/base/SGObject.h>
namespace shogun
{
/** @brief MKLMulticlassOptimizationBase is a helper class for MKLMulticlass.
*
* it is a virtual base class for MKLMulticlassGLPK and MKLMulticlassGradient which are instances of optimization
*
*/
class MKLMulticlassOptimizationBase: public CSGObject
{
public:
/** Class default Constructor
*
*/
MKLMulticlassOptimizationBase();
/** Class default Destructor
*
*/
virtual ~MKLMulticlassOptimizationBase();
/** initializes solver
*
* @param numkernels2 is the number of kernels
*
*
*/
virtual void setup(const int32_t numkernels2);
/** adds a constraint to the LP arising in L1 MKL based on two parameters
*
* @param normw2 is the vector of \f$ \|w_k \|^2 \f$ for all kernels
* @param sumofpositivealphas is a term depending on alphas, labels and
* biases, see in the function float64_t getsumofsignfreealphas() from
* MKLMulticlass.h, it depends on the formulation of the underlying GMNPSVM.
*
*/
virtual void addconstraint(const ::std::vector<float64_t> & normw2,
const float64_t sumofpositivealphas);
/** computes MKL weights
*
* @param weights2 stores the new weights
*
*/
virtual void computeweights(std::vector<float64_t> & weights2);
/** @return object name */
virtual const char* get_name() const { return "MKLMulticlassOptimizationBase"; }
/** sets p-norm parameter for MKL
* @param norm the MKL norm
*/
virtual void set_mkl_norm(float64_t norm);
protected:
protected:
};
}
#endif
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