// 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 //
+// Written by Francois Fleuret //
+// (C) Idiap Research Institute //
+// //
// 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,