/usr/include/shogun/classifier/LPM.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 | /*
* 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) 2007-2009 Soeren Sonnenburg
* Copyright (C) 2007-2009 Fraunhofer Institute FIRST and Max-Planck-Society
*/
#ifndef _LPM_H___
#define _LPM_H___
#include <shogun/lib/config.h>
#ifdef USE_CPLEX
#include <stdio.h>
#include <shogun/lib/common.h>
#include <shogun/features/Features.h>
#include <shogun/machine/LinearMachine.h>
namespace shogun
{
/** @brief Class LPM trains a linear classifier called Linear Programming
* Machine, i.e. a SVM using a \f$\ell_1\f$ norm regularizer.
*
* It solves the following optimization problem using CPLEX:
*
* \f{eqnarray*}
* \min_{{\bf w}={(\bf w^+},{\bf w^-}), b, {\bf \xi}} &&
* \sum_{i=1}^N ( {\bf w}^+_i + {\bf w}^-_i) + C \sum_{i=1}^{N} \xi_i\\
*
* \mbox{s.t.} && -y_i(({\bf w}^+-{\bf w}^-)^T {\bf x}_i + b)-{\bf \xi}_i \leq -1\\
* && \quad {\bf x}_i \geq 0\\\
* && {\bf w}_i \geq 0,\quad \forall i=1\dots N
* \f}
*
* Note that currently CPLEX is required to solve this problem. A
* faster implementation is available in CLPBoost.
*
* \sa CLPBoost
*/
class CLPM : public CLinearMachine
{
public:
MACHINE_PROBLEM_TYPE(PT_BINARY);
CLPM();
virtual ~CLPM();
virtual EMachineType get_classifier_type()
{
return CT_LPM;
}
/** set features
*
* @param feat features to set
*/
virtual void set_features(CDotFeatures* feat)
{
if (feat->get_feature_class() != C_SPARSE ||
feat->get_feature_type() != F_DREAL)
SG_ERROR("LPM requires SPARSE REAL valued features\n")
CLinearMachine::set_features(feat);
}
/** 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; }
inline float64_t get_C1() { return C1; }
inline float64_t get_C2() { return C2; }
inline void set_bias_enabled(bool enable_bias) { use_bias=enable_bias; }
inline bool get_bias_enabled() { return use_bias; }
inline void set_epsilon(float64_t eps) { epsilon=eps; }
inline float64_t get_epsilon() { return epsilon; }
/** @return object name */
virtual const char* get_name() const { return "LPM"; }
protected:
/** 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);
protected:
float64_t C1;
float64_t C2;
bool use_bias;
float64_t epsilon;
};
}
#endif //USE_CPLEX
#endif //_LPM_H___
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