+/*
+ * folded-ctf is an implementation of the folded hierarchy of
+ * classifiers for object detection, developed by Francois Fleuret
+ * and Donald Geman.
+ *
+ * Copyright (c) 2008 Idiap Research Institute, http://www.idiap.ch/
+ * Written by Francois Fleuret <francois.fleuret@idiap.ch>
+ *
+ * This file is part of folded-ctf.
+ *
+ * folded-ctf is free software: you can redistribute it and/or modify
+ * it under the terms of the GNU General Public License version 3 as
+ * published by the Free Software Foundation.
+ *
+ * folded-ctf 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 folded-ctf. If not, see <http://www.gnu.org/licenses/>.
+ *
+ */
-///////////////////////////////////////////////////////////////////////////
-// This program is free software: you can redistribute it and/or modify //
-// it under the terms of the version 3 of the GNU General Public License //
-// as published by the Free Software Foundation. //
-// //
-// This program 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 program. If not, see <http://www.gnu.org/licenses/>. //
-// //
-// Written by Francois Fleuret, (C) IDIAP //
-// Contact <francois.fleuret@idiap.ch> for comments & bug reports //
-///////////////////////////////////////////////////////////////////////////
+/*
+
+ A LossMachine provides all the methods necessary to do boosting with
+ a certain loss. Note that only the LOSS_EXPONENTIAL has been really
+ tested. Using the others may result in unexpected effects.
+
+ */
#ifndef LOSS_MACHINE_H
#define LOSS_MACHINE_H
scalar_t *weak_learner_responses,
scalar_t *current_responses);
- // This method returns in sample_nb_occurences[k] the number of time
- // the example k was sampled, and in sample_responses[k] the
- // consistent response so that the overall loss remains the same. If
- // allow_duplicates is set to 1, all samples will have an identical
- // response (i.e. weight), but some may have more than one
- // occurence. On the contrary, if allow_duplicates is 0, samples
- // will all have only one occurence (or zero) but the responses may
- // vary to account for the multiple sampling.
+ /* This method returns in sample_nb_occurences[k] the number of time
+ the example k was sampled, and in sample_responses[k] the
+ consistent response so that the overall loss remains the same. If
+ allow_duplicates is set to 1, all samples will have an identical
+ response (i.e. weight), but some may have more than one
+ occurence. On the contrary, if allow_duplicates is 0, samples
+ will all have only one occurence (or zero) but the responses may
+ vary to account for the multiple sampling. */
void subsample(int nb, scalar_t *labels, scalar_t *responses,
int nb_to_sample, int *sample_nb_occurences, scalar_t *sample_responses,