decision_tree.h

   1
   2 ///////////////////////////////////////////////////////////////////////////
   3 // This program is free software: you can redistribute it and/or modify  //
   4 // it under the terms of the version 3 of the GNU General Public License //
   5 // as published by the Free Software Foundation.                         //
   6 //                                                                       //
   7 // This program is distributed in the hope that it will be useful, but   //
   8 // WITHOUT ANY WARRANTY; without even the implied warranty of            //
   9 // MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU      //
  10 // General Public License for more details.                              //
  11 //                                                                       //
  12 // You should have received a copy of the GNU General Public License     //
  13 // along with this program. If not, see <http://www.gnu.org/licenses/>.  //
  14 //                                                                       //
  15 // Written by Francois Fleuret, (C) IDIAP                                //
  16 // Contact <francois.fleuret@idiap.ch> for comments & bug reports        //
  17 ///////////////////////////////////////////////////////////////////////////
  18
  19 #ifndef DECISION_TREE_H
  20 #define DECISION_TREE_H
  21
  22 #include "misc.h"
  23 #include "classifier.h"
  24 #include "sample_set.h"
  25 #include "loss_machine.h"
  26
  27 class DecisionTree : public Classifier {
  28
  29   int _feature_index;
  30   scalar_t _threshold;
  31   scalar_t _weight;
  32
  33   DecisionTree *_subtree_lesser, *_subtree_greater;
  34
  35   static const int min_nb_samples_for_split = 5;
  36
  37   void pick_best_split(SampleSet *sample_set,
  38                        scalar_t *loss_derivatives);
  39
  40   void train(LossMachine *loss_machine,
  41              SampleSet *sample_set,
  42              scalar_t *current_responses,
  43              scalar_t *loss_derivatives,
  44              int depth);
  45
  46 public:
  47
  48   DecisionTree();
  49   ~DecisionTree();
  50
  51   int nb_leaves();
  52   int depth();
  53
  54   scalar_t response(SampleSet *sample_set, int n_sample);
  55
  56   void train(LossMachine *loss_machine,
  57              SampleSet *sample_set,
  58              scalar_t *current_responses);
  59
  60   void tag_used_features(bool *used);
  61   void re_index_features(int *new_indexes);
  62
  63   void read(istream *is);
  64   void write(ostream *os);
  65 };
  66
  67 #endif