LibLinear.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) 2007-2010 Soeren Sonnenburg
 * Copyright (c) 2007-2009 The LIBLINEAR Project.
 * Copyright (C) 2007-2010 Fraunhofer Institute FIRST and Max-Planck-Society
 */

#ifndef _LIBLINEAR_H___
#define _LIBLINEAR_H___

#include "lib/config.h"

#include "lib/common.h"
#include "base/Parameter.h"
#include "classifier/LinearClassifier.h"
#include "classifier/svm/SVM_linear.h"

namespace shogun
{
    enum LIBLINEAR_SOLVER_TYPE
    {
        L2R_LR,
        L2R_L2LOSS_SVC_DUAL,
        L2R_L2LOSS_SVC,
        // (default since this is the standard SVM)
        L2R_L1LOSS_SVC_DUAL,
        MCSVM_CS,
        L1R_L2LOSS_SVC,
        L1R_LR
    };

#ifdef HAVE_LAPACK

class CLibLinear : public CLinearClassifier
{
    public:
        CLibLinear(void);

        CLibLinear(LIBLINEAR_SOLVER_TYPE liblinear_solver_type);

        CLibLinear(
            float64_t C, CDotFeatures* traindat,
            CLabels* trainlab);

        virtual ~CLibLinear();

        inline LIBLINEAR_SOLVER_TYPE get_liblinear_solver_type()
        {
            return liblinear_solver_type;
        }

        inline void set_liblinear_solver_type(LIBLINEAR_SOLVER_TYPE st)
        {
            liblinear_solver_type=st;
        }

        virtual bool train(CFeatures* data=NULL);

        virtual inline EClassifierType get_classifier_type() { return CT_LIBLINEAR; }

        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_epsilon(float64_t eps) { epsilon=eps; }

        inline float64_t get_epsilon() { return epsilon; }

        inline void set_bias_enabled(bool enable_bias) { use_bias=enable_bias; }

        inline bool get_bias_enabled() { return use_bias; }

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

        inline int32_t get_max_iterations()
        {
            return max_iterations;
        }

        inline void set_max_iterations(int32_t max_iter=1000)
        {
            max_iterations=max_iter;
        }

        inline void set_linear_term(float64_t* linear_term, int32_t len)
        {
            if (!labels)
                SG_ERROR("Please assign labels first!\n");

            int32_t num_labels=labels->get_num_labels();

            if (num_labels!=len)
            {
                SG_ERROR("Number of labels (%d) does not match number"
                        "of entries (%d) in linear term \n", num_labels, len);
            }

            m_linear_term = CMath::clone_vector(linear_term, len);
            m_linear_term_len = len;
        }

        void get_linear_term(float64_t** linear_term, int32_t* len);

        void init_linear_term();

    private:
        void init();

        void train_one(const problem *prob, const parameter *param, double Cp, double Cn);
        void solve_l2r_l1l2_svc(
            const problem *prob, double eps, double Cp, double Cn, LIBLINEAR_SOLVER_TYPE st);

        void solve_l1r_l2_svc(problem *prob_col, double eps, double Cp, double Cn);
        void solve_l1r_lr(const problem *prob_col, double eps, double Cp, double Cn);


    protected:
        float64_t C1;
        float64_t C2;
        bool use_bias;
        float64_t epsilon;
        int32_t max_iterations;

        float64_t* m_linear_term;
        int32_t m_linear_term_len;

        LIBLINEAR_SOLVER_TYPE liblinear_solver_type;
};

#else /* HAVE_LAPACK  */

#define IGNORE_IN_CLASSLIST
IGNORE_IN_CLASSLIST class CLibLinear : public CLinearClassifier
{
public:
    CLibLinear(void) {}

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

#endif //HAVE_LAPACK

} /* namespace shogun  */

#endif //_LIBLINEAR_H___