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. //
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. //
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/>. //
15 // Written by Francois Fleuret, (C) IDIAP //
16 // Contact <francois.fleuret@idiap.ch> for comments & bug reports //
17 ///////////////////////////////////////////////////////////////////////////
21 This class is an implementation of the Classifier with a boosting of
22 tree. It works with samples from R^n and has no concept of the
27 #ifndef BOOSTED_CLASSIFIER_H
28 #define BOOSTED_CLASSIFIER_H
30 #include "classifier.h"
31 #include "sample_set.h"
32 #include "decision_tree.h"
33 #include "loss_machine.h"
35 class BoostedClassifier : public Classifier {
39 int _nb_weak_learners;
40 DecisionTree **_weak_learners;
44 BoostedClassifier(int nb_weak_learners);
46 virtual ~BoostedClassifier();
48 virtual scalar_t response(SampleSet *sample_set, int n_sample);
49 virtual void train(LossMachine *loss_machine, SampleSet *train, scalar_t *response);
51 virtual void tag_used_features(bool *used);
52 virtual void re_index_features(int *new_indexes);
54 virtual void read(istream *is);
55 virtual void write(ostream *os);