class BayesClassifierMachine

BayesClassifierMachine is the machine used by the BayesClassifier trainer to perform a Bayes Classification using different distributions.

Inheritance:

Public Fields

int n_trainers

the number of classes corresponds to the number of Trainer

int n_outputs

the number of outputs in this machine

EMTrainer** trainers

the actual trainers (EMTrainer since we are training distributions)

real* log_priors

the log_prior probabilities of each class.

Sequence* log_probabilities

it contains the log posterior probability plus the log prior of the class

bool allocated_log_priors

used to know if log_priors where given or allocated

ClassFormat* class_format

the class format of the output

MeasurerList** trainers_measurers

the measurers for each individual trainer

Public Methods

BayesClassifierMachine( EMTrainer**, int n_trainers_, MeasurerList** trainers_measurers_, ClassFormat* class_format_, real* log_priors_=NULL)

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

virtual void forward(Sequence* inputs)

definition of virtual functions of Machine

Inherited from Machine:

Public Fields

Sequence* outputs

Public Methods

virtual void reset()

virtual void setDataSet(DataSet* dataset_)

Inherited from Object:

Public Fields

Allocator* allocator

Public Methods

void addOption(const char* name, int size, void* ptr, const char* help="")

void addIOption(const char* name, int* ptr, int init_value, const char* help="")

void addROption(const char* name, real* ptr, real init_value, const char* help="")

void addBOption(const char* name, bool* ptr, bool init_value, const char* help="")

void addOOption(const char* name, Object** ptr, Object* init_value, const char* help="")

void setOption(const char* name, void* ptr)

void setIOption(const char* name, int option)

void setROption(const char* name, real option)

void setBOption(const char* name, bool option)

void setOOption(const char* name, Object* option)

virtual void loadXFile(XFile* file)

virtual void saveXFile(XFile* file)

void load(const char* filename)

void save(const char* filename)

void* operator new(size_t size, Allocator* allocator_=NULL)

void* operator new(size_t size, Allocator* allocator_, void* ptr_)

void operator delete(void* ptr)

Documentation

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).

int n_trainers

the number of classes corresponds to the number of Trainer

int n_outputs

the number of outputs in this machine

EMTrainer** trainers

the actual trainers (EMTrainer since we are training distributions)

real* log_priors

the log_prior probabilities of each class. default: log_priors are taken as the log of the proportions in the training set.

Sequence* log_probabilities

it contains the log posterior probability plus the log prior of the class

bool allocated_log_priors

used to know if log_priors where given or allocated

ClassFormat* class_format

the class format of the output

MeasurerList** trainers_measurers

the measurers for each individual trainer

BayesClassifierMachine( EMTrainer**, int n_trainers_, MeasurerList** trainers_measurers_, ClassFormat* class_format_, real* log_priors_=NULL)

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

virtual void forward(Sequence* inputs)

definition of virtual functions of Machine

This class has no child classes.

Author:

Samy Bengio (bengio@idiap.ch) Bison Ravi (francois.belisle@idiap.ch)

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