/usr/include/shogun/classifier/svm/SVMSGD.h is in libshogun-dev 1.1.0-4ubuntu2.
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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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 | #ifndef _SVMSGD_H___
#define _SVMSGD_H___
/*
SVM with stochastic gradient
Copyright (C) 2007- Leon Bottou
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 General Public License
along with this program; if not, write to the Free Software
Foundation, Inc., 59 Temple Place, Suite 330, Boston, MA 02111, USA
Shogun adjustments (w) 2008 Soeren Sonnenburg
*/
#include <shogun/lib/common.h>
#include <shogun/machine/LinearMachine.h>
#include <shogun/features/DotFeatures.h>
#include <shogun/features/Labels.h>
#include <shogun/loss/LossFunction.h>
namespace shogun
{
/** @brief class SVMSGD */
class CSVMSGD : public CLinearMachine
{
public:
/** default constructor */
CSVMSGD();
/** constructor
*
* @param C constant C
*/
CSVMSGD(float64_t C);
/** constructor
*
* @param C constant C
* @param traindat training features
* @param trainlab labels for training features
*/
CSVMSGD(
float64_t C, CDotFeatures* traindat,
CLabels* trainlab);
virtual ~CSVMSGD();
/** get classifier type
*
* @return classifier type SVMOCAS
*/
virtual inline EClassifierType get_classifier_type() { return CT_SVMSGD; }
/** 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; }
/** set if bias shall be enabled
*
* @param enable_bias if bias shall be enabled
*/
inline void set_bias_enabled(bool enable_bias) { use_bias=enable_bias; }
/** check if bias is enabled
*
* @return if bias is enabled
*/
inline bool get_bias_enabled() { return use_bias; }
/** set if regularized bias shall be enabled
*
* @param enable_bias if regularized bias shall be enabled
*/
inline void set_regularized_bias_enabled(bool enable_bias) { use_regularized_bias=enable_bias; }
/** check if regularized bias is enabled
*
* @return if regularized bias is enabled
*/
inline bool get_regularized_bias_enabled() { return use_regularized_bias; }
/** 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 */
inline virtual const char* get_name() const { return "SVMSGD"; }
protected:
/** calibrate */
void calibrate();
/** 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_machine(CFeatures* data=NULL);
private:
void init();
private:
float64_t t;
float64_t C1;
float64_t C2;
float64_t wscale;
float64_t bscale;
int32_t epochs;
int32_t skip;
int32_t count;
bool use_bias;
bool use_regularized_bias;
CLossFunction* loss;
};
}
#endif
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