classifier.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 /*
  22
  23   This class is mostly able to learn a classifier from a SampleSet and
  24   to provide a scalar response on any test sample. Additional methods
  25   are required for persistence and select the possibly very few used
  26   features.
  27
  28 */
  29
  30 #ifndef CLASSIFIER_H
  31 #define CLASSIFIER_H
  32
  33 #include "storable.h"
  34 #include "pi_feature_family.h"
  35 #include "sample_set.h"
  36 #include "pose_cell_hierarchy.h"
  37 #include "labelled_image_pool.h"
  38 #include "loss_machine.h"
  39
  40 class Classifier : public Storable {
  41 public:
  42   virtual ~Classifier();
  43
  44   // Evaluate on a sample
  45   virtual scalar_t response(SampleSet *sample_set, int n_sample) = 0;
  46
  47   // Train the classifier
  48   virtual void train(LossMachine *loss_machine, SampleSet *train, scalar_t *response) = 0;
  49
  50   // Tag in the boolean array which features are actually used
  51   virtual void tag_used_features(bool *used) = 0;
  52   // Change the indexes of the used features
  53   virtual void re_index_features(int *new_indexes) = 0;
  54
  55   // Storage
  56   virtual void read(istream *is) = 0;
  57   virtual void write(ostream *os) = 0;
  58
  59   void extract_pi_feature_family(PiFeatureFamily *full_pi_feature_family,
  60                                  PiFeatureFamily *extracted_pi_feature_family);
  61 };
  62
  63 #endif