shogun_liblinear.h

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/*
 * Copyright (c) 2007-2009 The LIBLINEAR Project.
 * All rights reserved.
 *
 * Redistribution and use in source and binary forms, with or without
 * modification, are permitted provided that the following conditions
 * are met:
 *
 * 1. Redistributions of source code must retain the above copyright
 * notice, this list of conditions and the following disclaimer.
 *
 * 2. Redistributions in binary form must reproduce the above copyright
 * notice, this list of conditions and the following disclaimer in the
 * documentation and/or other materials provided with the distribution.
 *
 * 3. Neither name of copyright holders nor the names of its contributors
 * may be used to endorse or promote products derived from this software
 * without specific prior written permission.
 *
 *
 * THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS
 * ``AS IS'' AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT
 * LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR
 * A PARTICULAR PURPOSE ARE DISCLAIMED.  IN NO EVENT SHALL THE REGENTS OR
 * CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL,
 * EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO,
 * PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR
 * PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF
 * LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING
 * NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
 * SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
 */
#ifndef DOXYGEN_SHOULD_SKIP_THIS

#ifndef _LIBLINEAR_H
#define _LIBLINEAR_H

#include <shogun/lib/config.h>

#ifdef HAVE_LAPACK
#include <shogun/optimization/liblinear/tron.h>
#include <shogun/features/DotFeatures.h>
#include <vector>

namespace shogun
{

#ifdef __cplusplus
extern "C" {
#endif

struct problem
{
    int32_t l;
    int32_t n;
    float64_t* y;
    CDotFeatures* x;
    bool use_bias;
};

struct parameter
{
    int32_t solver_type;

    /* these are for training only */
    float64_t eps;
    float64_t C;
    int32_t nr_weight;
    int32_t *weight_label;
    float64_t* weight;
};

struct model
{
    struct parameter param;
    int32_t nr_class;
    int32_t nr_feature;
    float64_t *w;
    int32_t *label;
    float64_t bias;
};

void destroy_model(struct model *model_);
void destroy_param(struct parameter *param);
#ifdef __cplusplus
}
#endif

class l2loss_svm_fun : public function
{
public:
    l2loss_svm_fun(const problem *prob, float64_t Cp, float64_t Cn);
    ~l2loss_svm_fun();

    float64_t fun(float64_t *w);

    void grad(float64_t *w, float64_t *g);

    void Hv(float64_t *s, float64_t *Hs);

    int32_t get_nr_variable();

private:
    void Xv(float64_t *v, float64_t *Xv);
    void subXv(float64_t *v, float64_t *Xv);
    void subXTv(float64_t *v, float64_t *XTv);

    float64_t *C;
    float64_t *z;
    float64_t *D;
    int32_t *I;
    int32_t sizeI;
    const problem *prob;
};

class l2r_lr_fun : public function
{
public:
    l2r_lr_fun(const problem *prob, float64_t* C);
    ~l2r_lr_fun();

    float64_t fun(float64_t *w);

    void grad(float64_t *w, float64_t *g);

    void Hv(float64_t *s, float64_t *Hs);

    int32_t get_nr_variable();

private:
    void Xv(float64_t *v, float64_t *Xv);
    void XTv(float64_t *v, float64_t *XTv);

    float64_t *C;
    float64_t *z;
    float64_t *D;
    const problem *m_prob;
};

class l2r_l2_svc_fun : public function
{
public:
    l2r_l2_svc_fun(const problem *prob, float64_t* Cs);
    ~l2r_l2_svc_fun();

    double fun(double *w);
    void grad(double *w, double *g);
    void Hv(double *s, double *Hs);

    int get_nr_variable();

protected:
    void Xv(double *v, double *Xv);
    void subXv(double *v, double *Xv);
    void subXTv(double *v, double *XTv);

    double *C;
    double *z;
    double *D;
    int *I;
    int sizeI;
    const problem *m_prob;
};

class l2r_l2_svr_fun: public l2r_l2_svc_fun
{
public:
    l2r_l2_svr_fun(const problem *prob, double *Cs, double p);

    double fun(double *w);
    void grad(double *w, double *g);

private:
    double m_p;
};


struct mcsvm_state
{
    double* w;
    double* B;
    double* G;
    double* alpha;
    double* alpha_new;
    int* index;
    double* QD;
    int* d_ind;
    double* d_val;
    int* alpha_index;
    int* y_index;
    int* active_size_i;
    bool allocated,inited;

    mcsvm_state()
    {
        w = NULL;
        B = NULL;
        G = NULL;
        alpha = NULL;
        alpha_new = NULL;
        index = NULL;
        QD = NULL;
        d_ind = NULL;
        d_val = NULL;
        alpha_index = NULL;
        y_index = NULL;
        active_size_i = NULL;
        allocated = false;
        inited = false;
    }

    ~mcsvm_state()
    {
        SG_FREE(w);
        SG_FREE(B);
        SG_FREE(G);
        SG_FREE(alpha);
        SG_FREE(alpha_new);
        SG_FREE(index);
        SG_FREE(QD);
        SG_FREE(d_ind);
        SG_FREE(d_val);
        SG_FREE(alpha_index);
        SG_FREE(y_index);
        SG_FREE(active_size_i);
    }
};

class Solver_MCSVM_CS
{
    public:
        Solver_MCSVM_CS(const problem *prob, int nr_class, double *C,
                        double *w0, double eps, int max_iter,
                        double train_time, mcsvm_state* given_state);
        ~Solver_MCSVM_CS();
        void solve();
    private:
        void solve_sub_problem(double A_i, int yi, double C_yi, int active_i, double *alpha_new);
        bool be_shrunk(int i, int m, int yi, double alpha_i, double minG);
        double *C;
        int w_size, l;
        int nr_class;
        int max_iter;
        double eps;
        double max_train_time;
        double* w0;
        const problem *prob;
        mcsvm_state* state;
};


}
#endif //HAVE_LAPACK
#endif //_LIBLINEAR_H

#endif // DOXYGEN_SHOULD_SKIP_THIS