/usr/include/shogun/classifier/svm/GMNPSVM.h is in libshogun-dev 1.1.0-4ubuntu2.
This file is owned by root:root, with mode 0o644.
The actual contents of the file can be viewed below.
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 | /*
* 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) 1999-2008 Vojtech Franc, xfrancv@cmp.felk.cvut.cz
* Copyright (C) 1999-2008 Center for Machine Perception, CTU FEL Prague
*/
#ifndef _GMNPSVM_H___
#define _GMNPSVM_H___
#include <shogun/lib/common.h>
#include <shogun/classifier/svm/MultiClassSVM.h>
#include <shogun/features/Features.h>
namespace shogun
{
/** @brief Class GMNPSVM implements a one vs. rest MultiClass SVM.
*
* It uses CGMNPLib for training (in true multiclass-SVM fashion).
*/
class CGMNPSVM : public CMultiClassSVM
{
void init();
public:
/** default constructor */
CGMNPSVM();
/** constructor
*
* @param C constant C
* @param k kernel
* @param lab labels
*/
CGMNPSVM(float64_t C, CKernel* k, CLabels* lab);
/** default destructor */
virtual ~CGMNPSVM();
/** get classifier type
*
* @return classifier type GMNPSVM
*/
virtual inline EClassifierType get_classifier_type() { return CT_GMNPSVM; }
/** required for CMKLMulticlass constraint computation
*
* @param y height of basealphas
* @param x width of basealphas
*
* @return basealphas basealphas[k][j] is the alpha for class
* k and sample j which is untransformed compared to
* the alphas stored in CSVM* members
*/
float64_t* get_basealphas_ptr(index_t* y, index_t* x);
/** @return object name */
inline virtual const char* get_name() const { return "GMNPSVM"; }
protected:
/** train SVM
*
* @param data training data (parameter can be avoided if distance or
* kernel-based classifiers are used and distance/kernels are
* initialized with train data)
*
* @return whether training was successful
*/
virtual bool train_machine(CFeatures* data=NULL);
protected:
/** required for CMKLMulticlass
* stores the untransformed alphas of this algorithm
* whereas CSVM* members stores a transformed version of it
* m_basealphas[k][j] is the alpha for class k and sample j
*/
// is the basic untransformed alpha, needed for MKL
float64_t* m_basealphas;
/** base alphas y */
index_t m_basealphas_y;
/** base alphas x */
index_t m_basealphas_x;
};
}
#endif
|