/usr/include/shogun/classifier/svm/SGDQN.h is in libshogun-dev 3.2.0-7.3build4.
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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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 | #ifndef _SGDQN_H___
#define _SGDQN_H___
/*
SVM with Quasi-Newton stochastic gradient
Copyright (C) 2009- Antoine Bordes
This program is free software; you can redistribute it and/or
modify it under the terms of the GNU Lesser General Public
License as published by the Free Software Foundation; either
version 2.1 of the License, or (at your option) any later version.
This program is distributed in the hope that it will be useful,
but WITHOUT ANY WARRANTY; without even the implied warranty of
MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
GNU General Public License for more details.
You should have received a copy of the GNU Lesser General Public
License along with this library; if not, write to the Free Software
Foundation, Inc., 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA
Shogun adjustments (w) 2011 Siddharth Kherada
*/
#include <shogun/lib/common.h>
#include <shogun/machine/LinearMachine.h>
#include <shogun/features/DotFeatures.h>
#include <shogun/labels/Labels.h>
#include <shogun/loss/LossFunction.h>
namespace shogun
{
/** @brief class SGDQN */
class CSGDQN : public CLinearMachine
{
public:
/** problem type */
MACHINE_PROBLEM_TYPE(PT_BINARY);
/** default constructor */
CSGDQN();
/** constructor
*
* @param C constant C
*/
CSGDQN(float64_t C);
/** constructor
*
* @param C constant C
* @param traindat training features
* @param trainlab labels for training features
*/
CSGDQN(
float64_t C, CDotFeatures* traindat,
CLabels* trainlab);
virtual ~CSGDQN();
/** get classifier type
*
* @return classifier type SVMSGDQN
*/
virtual EMachineType get_classifier_type() { return CT_SGDQN; }
/** train classifier
*
* @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(CFeatures* data=NULL);
/** set C
*
* @param c_neg new C constant for negatively labeled examples
* @param c_pos new C constant for positively labeled examples
*
*/
inline void set_C(float64_t c_neg, float64_t c_pos) { C1=c_neg; C2=c_pos; }
/** get C1
*
* @return C1
*/
inline float64_t get_C1() { return C1; }
/** get C2
*
* @return C2
*/
inline float64_t get_C2() { return C2; }
/** set epochs
*
* @param e new number of training epochs
*/
inline void set_epochs(int32_t e) { epochs=e; }
/** get epochs
*
* @return the number of training epochs
*/
inline int32_t get_epochs() { return epochs; }
/**computing diagonal scaling matrix B as ratio*/
void compute_ratio(float64_t* W,float64_t* W_1,float64_t* B,float64_t* dst,int32_t dim,float64_t regularizer_lambda,float64_t loss);
/** combine and clip scaling matrix B */
void combine_and_clip(float64_t* Bc,float64_t* B,int32_t dim,float64_t c1,float64_t c2,float64_t v1,float64_t v2);
/** Set the loss function to use
*
* @param loss_func object derived from CLossFunction
*/
void set_loss_function(CLossFunction* loss_func);
/** Return the loss function
*
* @return loss function as CLossFunction*
*/
inline CLossFunction* get_loss_function() { SG_REF(loss); return loss; }
/** @return object name */
virtual const char* get_name() const { return "SGDQN"; }
protected:
/** calibrate */
void calibrate();
private:
void init();
private:
float64_t t;
float64_t C1;
float64_t C2;
int32_t epochs;
int32_t skip;
int32_t count;
CLossFunction* loss;
};
}
#endif
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