GClasses::GHiddenMarkovModel Class Reference

#include <GHiddenMarkovModel.h>

Public Member Functions
	GHiddenMarkovModel (int stateCount, int symbolCount)
	~GHiddenMarkovModel ()
void	baumWelch (std::vector< int * > &sequences, std::vector< int > &lengths, int maxPasses=0x7fffffff)
	Uses expectation maximization to refine the model based on a training set of observation sequences. (You should have already set prior values for the initial, transition and symbol probabilites before you call this method.) More...
double	forwardAlgorithm (const int *pObservations, int len)
	Calculates the log probability that the specified observation sequence would occur with this model. More...
double *	initialStateProbabilities ()
	Returns the current vector of initial state probabilities. More...
double *	symbolProbabilities ()
	Returns the current vector of symbol probabilities, such that pSymbolProbabilities[stateCount * i + j] is the probability of observing symbol j when in state i. More...
double *	transitionProbabilities ()
	Returns the current vector of transition probabilities, such that pTransitionProbabilities[stateCount * i + j] is the probability of transitioning from state i to state j. More...
double	viterbi (int *pMostLikelyStates, const int *pObservations, int len)
	Finds the most likely state sequence to explain the specified observation sequence, and also returns the log probability of that state sequence given the observation sequence. More...

Static Public Member Functions
static void	test ()
	Performs unit tests for this class. Throws an exception if there is a failure. More...

Protected Member Functions
void	backwardAlgorithm (const int *pObservations, int len)
void	baumWelchAddSequence (const int *pObservations, int len)
void	baumWelchBeginPass ()
void	baumWelchBeginTraining (int maxLen)
double	baumWelchEndPass ()
void	baumWelchEndTraining ()

Protected Attributes
int	m_maxLen
double *	m_pInitialStateProbabilities
double *	m_pSymbolProbabilities
double *	m_pTrainingBuffer
double *	m_pTransitionProbabilities
int	m_stateCount
int	m_symbolCount

Constructor & Destructor Documentation

GClasses::GHiddenMarkovModel::GHiddenMarkovModel	(	int	stateCount,
		int	symbolCount
	)

GClasses::GHiddenMarkovModel::~GHiddenMarkovModel

(

)

Member Function Documentation

void GClasses::GHiddenMarkovModel::backwardAlgorithm ( const int *  pObservations, int  len  )

protected

void GClasses::GHiddenMarkovModel::baumWelch	(	std::vector< int * > &	sequences,
		std::vector< int > &	lengths,
		int	maxPasses = 0x7fffffff
	)

Uses expectation maximization to refine the model based on a training set of observation sequences. (You should have already set prior values for the initial, transition and symbol probabilites before you call this method.)

void GClasses::GHiddenMarkovModel::baumWelchAddSequence ( const int *  pObservations, int  len  )

protected

void GClasses::GHiddenMarkovModel::baumWelchBeginPass ( )

protected

void GClasses::GHiddenMarkovModel::baumWelchBeginTraining ( int  maxLen )

protected

double GClasses::GHiddenMarkovModel::baumWelchEndPass ( )

protected

void GClasses::GHiddenMarkovModel::baumWelchEndTraining ( )

protected

double GClasses::GHiddenMarkovModel::forwardAlgorithm	(	const int *	pObservations,
		int	len
	)

Calculates the log probability that the specified observation sequence would occur with this model.

double* GClasses::GHiddenMarkovModel::initialStateProbabilities ( )

inline

Returns the current vector of initial state probabilities.

double* GClasses::GHiddenMarkovModel::symbolProbabilities ( )

inline

Returns the current vector of symbol probabilities, such that pSymbolProbabilities[stateCount * i + j] is the probability of observing symbol j when in state i.

static void GClasses::GHiddenMarkovModel::test ( )

static

Performs unit tests for this class. Throws an exception if there is a failure.

double* GClasses::GHiddenMarkovModel::transitionProbabilities ( )

inline

Returns the current vector of transition probabilities, such that pTransitionProbabilities[stateCount * i + j] is the probability of transitioning from state i to state j.

double GClasses::GHiddenMarkovModel::viterbi	(	int *	pMostLikelyStates,
		const int *	pObservations,
		int	len
	)

Finds the most likely state sequence to explain the specified observation sequence, and also returns the log probability of that state sequence given the observation sequence.

Member Data Documentation

int GClasses::GHiddenMarkovModel::m_maxLen

protected

double* GClasses::GHiddenMarkovModel::m_pInitialStateProbabilities

protected

double* GClasses::GHiddenMarkovModel::m_pSymbolProbabilities

protected

double* GClasses::GHiddenMarkovModel::m_pTrainingBuffer

protected

double* GClasses::GHiddenMarkovModel::m_pTransitionProbabilities

protected

int GClasses::GHiddenMarkovModel::m_stateCount

protected

int GClasses::GHiddenMarkovModel::m_symbolCount

protected