OnlineLibLinear.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
 * Written (W) 2011 Shashwat Lal Das
 * Copyright (c) 2007-2009 The LIBLINEAR Project.
 * Copyright (C) 2007-2010 Fraunhofer Institute FIRST and Max-Planck-Society
 */

#ifndef _ONLINELIBLINEAR_H__
#define _ONLINELIBLINEAR_H__

#include <shogun/lib/config.h>

#include <shogun/lib/SGVector.h>
#include <shogun/lib/common.h>
#include <shogun/base/Parameter.h>
#include <shogun/machine/OnlineLinearMachine.h>

namespace shogun
{
class COnlineLibLinear : public COnlineLinearMachine
{
public:

        MACHINE_PROBLEM_TYPE(PT_BINARY);

        COnlineLibLinear();

        COnlineLibLinear(float64_t C);

        COnlineLibLinear(float64_t C, CStreamingDotFeatures* traindat);

        COnlineLibLinear(COnlineLibLinear *mch);

        virtual ~COnlineLibLinear();

        virtual void set_C(float64_t c_neg, float64_t c_pos) { C1=c_neg; C2=c_pos; }

        virtual float64_t get_C1() { return C1; }

        float64_t get_C2() { return C2; }

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

        virtual bool get_bias_enabled() { return use_bias; }

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

        virtual void start_train();

        virtual void stop_train();

        virtual void train_example(CStreamingDotFeatures *feature, float64_t label);

        virtual void train_one(SGVector<float32_t> ex, float64_t label);

private:
        void init();

private:
        bool use_bias;
        float64_t C1;
        float64_t C2;

private:
        //========================================
        // "local" variables used during training

        float64_t C, d, G;
        float64_t QD;

        // y and alpha for example being processed
        int32_t y_current;
        float64_t alpha_current;

        // Cost constants
        float64_t Cp;
        float64_t Cn;

        // PG: projected gradient, for shrinking and stopping
        float64_t PG;
        float64_t PGmax_old;
        float64_t PGmin_old;
        float64_t PGmax_new;
        float64_t PGmin_new;

        // Diag is probably unnecessary
        float64_t diag[3];
        float64_t upper_bound[3];

        // Objective value = v/2
        float64_t v;
        // Number of support vectors
        int32_t nSV;
};
}
#endif // _ONLINELIBLINEAR_H__