X-Git-Url: https://www.fleuret.org/cgi-bin/gitweb/gitweb.cgi?p=folded-ctf.git;a=blobdiff_plain;f=loss_machine.h;h=9d7a78c5406771999808706f30cda84f7fa747ca;hp=a293e8b71a9090bb2ed89fc1d31fafd7c819288d;hb=HEAD;hpb=d922ad61d35e9a6996730bec24b16f8bf7bc426c

diff --git a/loss_machine.h b/loss_machine.h
index a293e8b..9d7a78c 100644
--- a/loss_machine.h
+++ b/loss_machine.h
@@ -1,20 +1,34 @@
+/*
+ *  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
@@ -38,14 +52,14 @@ public:
                           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,