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                                           //
  16 // (C) Idiap Research Institute                                          //
  17 //                                                                       //
  18 // Contact <francois.fleuret@idiap.ch> for comments & bug reports        //
  19 ///////////////////////////////////////////////////////////////////////////
  20
  21 #ifndef DECISION_TREE_H
  22 #define DECISION_TREE_H
  23
  24 #include "misc.h"
  25 #include "classifier.h"
  26 #include "sample_set.h"
  27 #include "loss_machine.h"
  28
  29 class DecisionTree : public Classifier {
  30
  31   int _feature_index;
  32   scalar_t _threshold;
  33   scalar_t _weight;
  34
  35   DecisionTree *_subtree_lesser, *_subtree_greater;
  36
  37   static const int min_nb_samples_for_split = 5;
  38
  39   void pick_best_split(SampleSet *sample_set,
  40                        scalar_t *loss_derivatives);
  41
  42   void train(LossMachine *loss_machine,
  43              SampleSet *sample_set,
  44              scalar_t *current_responses,
  45              scalar_t *loss_derivatives,
  46              int depth);
  47
  48 public:
  49
  50   DecisionTree();
  51   ~DecisionTree();
  52
  53   int nb_leaves();
  54   int depth();
  55
  56   scalar_t response(SampleSet *sample_set, int n_sample);
  57
  58   void train(LossMachine *loss_machine,
  59              SampleSet *sample_set,
  60              scalar_t *current_responses);
  61
  62   void tag_used_features(bool *used);
  63   void re_index_features(int *new_indexes);
  64
  65   void read(istream *is);
  66   void write(ostream *os);
  67 };
  68
  69 #endif