MyMulticlassLibLinear.h

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/*
 * This software is distributed under BSD 3-clause license (see LICENSE file).
 *
 * Authors: Sergey Lisitsyn, Fernando Iglesias, Yuyu Zhang, Evan Shelhamer, 
 *          Bjoern Esser, Soeren Sonnenburg
 */
 
#ifndef _MULTICLASSLIBLINEAR_H___
#define _MULTICLASSLIBLINEAR_H___
 
#include <shogun/lib/config.h>
#include <shogun/lib/common.h>
#include <shogun/features/DotFeatures.h>
#include <shogun/machine/LinearMulticlassMachine.h>
#include <shogun/optimization/liblinear/shogun_liblinear.h>
#include <shogun/lib/config.h>
#include <shogun/multiclass/MulticlassOneVsRestStrategy.h>
#include <shogun/mathematics/Math.h>
#include <shogun/lib/v_array.h>
#include <shogun/lib/Signal.h>
#include <shogun/labels/MulticlassLabels.h>
#include <vector>
#include <Eigen/Dense>
using std::vector;
 
 
namespace shogun
{
 
    class CMyMulticlassLibLinear : public CLinearMulticlassMachine
    {
        public:
            
            MACHINE_PROBLEM_TYPE(PT_MULTICLASS);
            
            CMyMulticlassLibLinear();
 
            CMyMulticlassLibLinear(float64_t C, CDotFeatures* features, CLabels* labs);
 
            ~CMyMulticlassLibLinear();
 
            virtual const char* get_name() const;
 
            inline void set_C(float64_t C);
            
            inline float64_t get_C() const;
 
            inline void set_epsilon(float64_t epsilon);
            
            inline float64_t get_epsilon() const;
 
            inline void set_use_bias(bool use_bias);
            
            inline bool get_use_bias() const;
 
            inline void set_save_train_state(bool save_train_state);
            
            inline bool get_save_train_state() const;
 
            inline void set_max_iter(int32_t max_iter);
            
            inline int32_t get_max_iter() const;
 
            void reset_train_state();
 
            SGVector<int32_t> get_support_vectors() const;
    
            /* get the weights for each SVM Subclass
            * @return the vector of weights for each subclass
            */
            vector<SGVector<float64_t>> get_w() const;
            void set_w(vector<Eigen::VectorXd> wnew);
 
    protected:
 
            bool train_machine(CFeatures* data);
 
            SGMatrix<float64_t> obtain_regularizer_matrix() const;
 
    private:
 
            void init_defaults();
 
            void register_parameters();
 
 
    protected:
 
            float64_t m_C;
 
            float64_t m_epsilon;
 
            int32_t m_max_iter;
 
            bool m_use_bias;
 
            bool m_save_train_state;
 
            mcsvm_state* m_train_state;
    };
}
#endif