class EMTrainer

This class is used to train any distribution using the EM algorithm.

Inheritance:


Public Fields

[more]Distribution* distribution
the distribution to train
[more]real end_accuracy
the stopping criterion regarding the accuracy for EM
[more]int max_iter
the stopping criterion regarding the number of iterations for EM
[more]bool viterbi
when viterbi is true, use Viterbi training instead of EM training

Public Methods

[more] EMTrainer(Distribution* distribution_)
[more]virtual void decode(MeasurerList* measurers)
this method computes the most likely path into the distribution.


Inherited from Trainer:

Public Methods

ovirtual void train(DataSet* data_, MeasurerList* measurers)
ovirtual void test(MeasurerList* measurers)
ostatic Allocator* extractMeasurers(MeasurerList* measurers, DataSet* train, DataSet*** datas, Measurer**** meas, int** n_meas, int* n_datas)
ovirtual void loadXFile(XFile* file)
ovirtual void saveXFile(XFile* file)


Inherited from Object:

Public Fields

oAllocator* allocator

Public Methods

ovoid addOption(const char* name, int size, void* ptr, const char* help="")
ovoid addIOption(const char* name, int* ptr, int init_value, const char* help="")
ovoid addROption(const char* name, real* ptr, real init_value, const char* help="")
ovoid addBOption(const char* name, bool* ptr, bool init_value, const char* help="")
ovoid addOOption(const char* name, Object** ptr, Object* init_value, const char* help="")
ovoid setOption(const char* name, void* ptr)
ovoid setIOption(const char* name, int option)
ovoid setROption(const char* name, real option)
ovoid setBOption(const char* name, bool option)
ovoid setOOption(const char* name, Object* option)
ovoid load(const char* filename)
ovoid save(const char* filename)
ovoid* operator new(size_t size, Allocator* allocator_=NULL)
ovoid* operator new(size_t size, Allocator* allocator_, void* ptr_)
ovoid operator delete(void* ptr)


Documentation

This class is used to train any distribution using the EM algorithm. It can also train using the Viterbi training algorithm.

oDistribution* distribution
the distribution to train

oreal end_accuracy
the stopping criterion regarding the accuracy for EM

oint max_iter
the stopping criterion regarding the number of iterations for EM

obool viterbi
when viterbi is true, use Viterbi training instead of EM training

o EMTrainer(Distribution* distribution_)

ovirtual void decode(MeasurerList* measurers)
this method computes the most likely path into the distribution. mainly used for sequential distribution such as HMMs.


This class has no child classes.
Author:
Samy Bengio (bengio@idiap.ch)

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