/usr/include/torch/BayesClassifierMachine.h is in libtorch3-dev 3.1-2.1.
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// and Bison Ravi (francois.belisle@idiap.ch)
//
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#ifndef BAYES_CLASSIFIER_MACHINE_INC
#define BAYES_CLASSIFIER_MACHINE_INC
#include "Machine.h"
#include "EMTrainer.h"
#include "ClassFormat.h"
namespace Torch {
/** BayesClassifierMachine is the machine used by the #BayesClassifier#
trainer to perform a Bayes Classification using different distributions.
The output corresponds to the class that is the most probable
(using prior AND posterior information).
@author Samy Bengio (bengio@idiap.ch)
@author Bison Ravi (francois.belisle@idiap.ch)
*/
class BayesClassifierMachine : public Machine
{
public:
/// the number of classes corresponds to the number of #Trainer#
int n_trainers;
/// the number of outputs in this machine
int n_outputs;
/// the actual trainers (EMTrainer since we are training distributions).
EMTrainer** trainers;
/** the log_prior probabilities of each class. default: log_priors are
taken as the log of the proportions in the training set.
*/
real* log_priors;
/// it contains the log posterior probability plus the log prior of the class.
Sequence* log_probabilities;
/// used to know if log_priors where given or allocated
bool allocated_log_priors;
/// the class format of the output
ClassFormat* class_format;
/// the measurers for each individual trainer
MeasurerList** trainers_measurers;
/** creates a machine for BayesClassifier trainers, given a vector of
trainers (one per class), an associate measurer for each trainer,
a class_format that explains how the classes are coded, and an eventual
vector (of size #n_trainers_#) containing the log of the class priors.
*/
BayesClassifierMachine( EMTrainer**, int n_trainers_, MeasurerList** trainers_measurers_ , ClassFormat* class_format_, real* log_priors_=NULL);
virtual ~BayesClassifierMachine();
/** definition of virtual functions of #Machine# */
virtual void forward(Sequence *inputs);
virtual void reset();
virtual void loadXFile( XFile* );
virtual void saveXFile( XFile* );
};
}
#endif
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