svmsgd_alg.h

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// Copyright (C) 2010-2011 Institut de Robotica i Informatica Industrial, CSIC-UPC.
// Author 
// All rights reserved.
//
// This file is part of iri-ros-pkg
// iri-ros-pkg is free software: you can redistribute it and/or modify
// it under the terms of the GNU Lesser General Public License as published by
// the Free Software Foundation, either version 3 of the License, or
// at your option) any later version.
//
// This program is distributed in the hope that it will be useful,
// but WITHOUT ANY WARRANTY; without even the implied warranty of
// MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
// GNU Lesser General Public License for more details.
//
// You should have received a copy of the GNU Lesser General Public License
// along with this program.  If not, see <http://www.gnu.org/licenses/>.
// 
// IMPORTANT NOTE: This code has been generated through a script from the 
// iri_ros_scripts. Please do NOT delete any comments to guarantee the correctness
// of the scripts. ROS topics can be easly add by using those scripts. Please
// refer to the IRI wiki page for more information:
// http://wikiri.upc.es/index.php/Robotics_Lab

#ifndef _svmsgd_alg_h_
#define _svmsgd_alg_h_

#include <iri_svmsgd/SvmsgdConfig.h>
#include "mutex.h"
#include <vector>
//#include <iri_svmsgd/learn_example.h>

//include svmsgd_alg main library

/* Available losses */
#define HINGELOSS 1
#define SMOOTHHINGELOSS 2
#define SQUAREDHINGELOSS 3
#define LOGLOSS 10
#define LOGLOSSMARGIN 11

/* One when bias is regularized */
#define REGULARIZEBIAS 0


class SvmsgdAlgorithm
{
  protected:
    CMutex alg_mutex_;

    // private attributes and methods
    int num_iters;
    int loss_type;
    int d;
    double *w;
    double bias;

    double lambda;
    int use_bias;
    int use_reg_bias;
    double bscale;
    int count;
    int skip;
    double t;
    double wscale;
    bool calibrated;

    inline void add_to_dense_vec(const std::vector<float> &vec1, double alpha, double* vec2)
    {
      for (int i=0; i<this->d; i++)
        vec2[i]+=alpha*vec1[i];
    }

    inline void add_to_dense_vec_abs(const std::vector<float> &vec1, double alpha, double* vec2)
    {
      for (int i=0; i<this->d; i++)
        vec2[i]+=alpha*abs(vec1[i]);
    }

    inline double dot(const std::vector<float> &v1, const double* v2, int n)
    {
      double r=0;
      for(int i=0; i<n; i++)
        r+=v1[i]*v2[i];
      return r;
    }

    //todo define how to calibrate
    double dloss(double z);
    double loss(double z);
  
  public:
    typedef iri_svmsgd::SvmsgdConfig Config;

    Config config_;

    SvmsgdAlgorithm(void);

    void lock(void) { alg_mutex_.enter(); };

    void unlock(void) { alg_mutex_.exit(); };

    bool try_enter(void) { return alg_mutex_.try_enter(); };

    void config_update(Config& new_cfg, uint32_t level=0);

    // here define all svmsgd_alg interface methods to retrieve and set
    // the driver parameters

    ~SvmsgdAlgorithm(void);
    //arnau 20/9/12 to be reviewed! void calibrate(const std::vector<iri_svmsgd::learn_example> &train_matrix);
    void process_new_example(std::vector<float> &new_example, double label);
    inline float get_bias(){return this->bias;}
    inline std::vector<float> get_w()
    {
      std::vector<float> wout(this->d);
      for(int i=0;i<this->d;i++)
        wout[i]=this->w[i];
      return wout;
    }
};

#endif