adaboost.h

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//
//    Omlet - Open Machine Learning Enhanced Toolkit
//
//    Copyright (C) 2006   TALP Research Center
//                         Universitat Politecnica de Catalunya
//
//    This file is part of the Omlet library
//
//    The Omlet library 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.
//
//    This library 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
//    General Public License for more details.
//
//    You should have received a copy of the GNU General Public
//    License along with this library; if not, write to the Free Software
//    Foundation, Inc., 51 Franklin St, 5th Floor, Boston, MA 02110-1301 USA
//
//    contact: Lluis Padro (padro@lsi.upc.es)
//             TALP Research Center
//             despatx Omega.S112 - Campus Nord UPC
//             08034 Barcelona.  SPAIN
//

//
// Author: Xavier Carreras
//

#ifndef _ADABOOST
#define _ADABOOST

#include "freeling/omlet/weakrule.h"
#include "freeling/omlet/dataset.h"
#include "freeling/omlet/classifier.h"

#include <iostream>
#include <string>
#include <list>
#include <vector>



class adaboost : public std::list<weak_rule*>, public classifier {
 private:
  bool option_initialize_weights;

  std::wstring wr_type;

  adaboost::const_iterator pcl_pointer; // partial classification pointer
  int nrules;

  std::wostream *out;

  void initialize_weights(dataset &ds);
  void update_weights(weak_rule *wr, double Z, dataset &ds);
  void add_weak_rule(weak_rule *wr);

  adaboost(const adaboost &old_bab); 

 public:
  adaboost(int nl, std::wstring t);
  adaboost(const std::wstring &file, const std::wstring &codes);
  int n_rules();

  void classify(const example &i,  double pred[]) const;
  std::vector<double> classify(const example &i);

  void pcl_ini_pointer();
  int  pcl_advance_pointer(int steps);
  void pcl_classify(const example &i, double *pred, int nrules);

  void learn(dataset &ds, int nrounds, bool init, wr_params *p);
  void learn(dataset &ds, int nrounds, bool init, wr_params *p, const std::wstring &outf);

  void set_output(std::wostream *os);
  void read_from_stream(std::wistream *in);
  void read_from_file(const std::wstring &f);

  void set_initialize_weights(bool b);
};



#endif