SubGradientSVM.h

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/*
 * 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
 * Written (W) 2007-2008 Vojtech Franc
 * Copyright (C) 2007-2009 Fraunhofer Institute FIRST and Max-Planck-Society
 */

#ifndef _SUBGRADIENTSVM_H___
#define _SUBGRADIENTSVM_H___

#include <shogun/lib/common.h>
#include <shogun/machine/LinearMachine.h>
#include <shogun/features/DotFeatures.h>
#include <shogun/labels/Labels.h>

namespace shogun
{
class CSubGradientSVM : public CLinearMachine
{
    public:

        MACHINE_PROBLEM_TYPE(PT_BINARY);

        CSubGradientSVM();

        CSubGradientSVM(
            float64_t C, CDotFeatures* traindat,
            CLabels* trainlab);
        virtual ~CSubGradientSVM();

        virtual inline EMachineType get_classifier_type() { return CT_SUBGRADIENTSVM; }

        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; }

        inline void set_qpsize(int32_t q) { qpsize=q; }

        inline int32_t get_qpsize() { return qpsize; }

        inline void set_qpsize_max(int32_t q) { qpsize_max=q; }

        inline int32_t get_qpsize_max() { return qpsize_max; }

    protected:
        int32_t find_active(
            int32_t num_feat, int32_t num_vec, int32_t& num_active,
            int32_t& num_bound);

        void update_active(int32_t num_feat, int32_t num_vec);

        float64_t compute_objective(int32_t num_feat, int32_t num_vec);

        float64_t compute_min_subgradient(
            int32_t num_feat, int32_t num_vec, int32_t num_active,
            int32_t num_bound);

        float64_t line_search(int32_t num_feat, int32_t num_vec);

        void compute_projection(int32_t num_feat, int32_t num_vec);

        void update_projection(float64_t alpha, int32_t num_vec);

        void init(int32_t num_vec, int32_t num_feat);

        void cleanup();

        inline virtual const char* get_name() const { return "SubGradientSVM"; }

    protected:
        virtual bool train_machine(CFeatures* data=NULL);

    protected:
        float64_t C1;
        float64_t C2;
        float64_t epsilon;
        float64_t work_epsilon;
        float64_t autoselected_epsilon;
        int32_t qpsize;
        int32_t qpsize_max;
        int32_t qpsize_limit;
        bool use_bias;

        int32_t last_it_noimprovement;
        int32_t num_it_noimprovement;

        //idx vectors of length num_vec
        uint8_t* active;
        uint8_t* old_active;
        int32_t* idx_active;
        int32_t* idx_bound;
        int32_t delta_active;
        int32_t delta_bound;
        float64_t* proj;
        float64_t* tmp_proj;
        int32_t* tmp_proj_idx;

        //vector of length num_feat
        float64_t* sum_CXy_active;
        float64_t* v;
        float64_t* old_v;
        float64_t sum_Cy_active;

        //vector of length num_feat
        float64_t* grad_w;
        float64_t grad_b;
        float64_t* grad_proj;
        float64_t* hinge_point;
        int32_t* hinge_idx;

        //vectors/sym matrix of size qpsize_limit
        float64_t* beta;
        float64_t* old_beta;
        float64_t* Zv;
        float64_t* old_Zv;
        float64_t* Z;
        float64_t* old_Z;

        float64_t tim;
};
}
#endif