MixtureofGaussianHMM Class Reference

A wrapper class for HMM functionals in Mixture of Gaussion case. More...

Collaboration diagram for MixtureofGaussianHMM:

Public Member Functions
void	ComputeLogLikelihood (const ArrayList< Matrix > &list_data_seq, ArrayList< double > *list_likelihood) const
	Compute the log-likelihood of a list of sequences.
double	ComputeLogLikelihood (const Matrix &data_seq) const
	Compute the log-likelihood of a sequence.
void	ComputeViterbiStateSequence (const Matrix &data_seq, Vector *state_seq) const
	Compute the most probable sequence (Viterbi).
void	DecodeInit (const Matrix &data_seq, Matrix state_prob_mat, Matrix forward_prob_mat, Matrix backward_prob_mat, Vector scale_vec) const
	A decode version that initialized the output matrices.
void	DecodeOverwrite (const Matrix &data_seq, Matrix state_prob_mat, Matrix forward_prob_mat, Matrix backward_prob_mat, Vector scale_vec) const
	Decode a sequence into probabilities of each state at each time step using scaled forward-backward algorithm.
void	EstimateModel (int numstate, int numcluster, const Matrix &data_seq, const Vector &state_seq)
void	EstimateModel (int numcluster, const Matrix &data_seq, const Vector &state_seq)
	Estimate the matrices by a data sequence and a state sequence Must be already initialized.
void	GenerateSequence (int L, Matrix data_seq, Vector state_seq) const
	Generate a random data sequence of a given length.
void	Init ()
	Initializes empty object.
void	Init (const Matrix &transmission, const ArrayList< MixtureGauss > &list_mixture_gauss)
	Initializes from computed transmission and Mixture of Gaussian parameters.
void	InitFromFile (const char *profile)
	Initializes by loading from a file.
const ArrayList< MixtureGauss > &	list_mixture_gauss () const
void	LoadProfile (const char *profile)
	Load from file, used when already initialized.
void	SaveProfile (const char *profile) const
	Save matrices to file.
void	setModel (const Matrix &transmission, const ArrayList< MixtureGauss > &list_mixture_gauss)
	Setter used when already initialized.
void	TrainBaumWelch (const ArrayList< Matrix > &list_data_seq, int max_iteration, double tolerance)
	Train the model with a list of sequences, must be already initialized using Baum-Welch EM algorithm.
void	TrainViterbi (const ArrayList< Matrix > &list_data_seq, int max_iteration, double tolerance)
	Train the model with a list of sequences, must be already initialized using Viterbi algorithm to determine the state sequence of each sequence.
const Matrix &	transmission () const
	Getters.
Static Public Member Functions
static void	BackwardProcedure (int L, const Matrix &trans, const Matrix &emis_prob, const Vector &scales, Matrix *bs)
static void	CalculateEmissionProb (const Matrix &seq, const ArrayList< MixtureGauss > &mixs, Matrix *emis_prob)
static double	Decode (int L, const Matrix &trans, const Matrix &emis_prob, Matrix pstates, Matrix fs, Matrix bs, Vector scales)
static double	Decode (const Matrix &trans, const Matrix &emis_prob, Matrix pstates, Matrix fs, Matrix bs, Vector scales)
static void	EstimateInit (int numStates, int NumClusters, const Matrix &seq, const Vector &states, Matrix trans, ArrayList< MixtureGauss > mixs)
static void	EstimateInit (int NumClusters, const Matrix &seq, const Vector &states, Matrix trans, ArrayList< MixtureGauss > mixs)
	Estimate transition and emission distribution from sequence and states.
static void	ForwardProcedure (int L, const Matrix &trans, const Matrix &emis_prob, Vector scales, Matrix fs)
	Calculate posteriori probabilities of states at each steps Scaled Forward - Backward procedure trans: Transition probabilities, size M x M emis_prob: Emission probabilities along the sequence, size M x L (L is the sequence length) pstates: size M x L fs: scaled forward probabities, size M x L bs: scaled backward probabities, size M x L scales: scale factors, length L RETURN: log probabilities of sequence.
static void	GenerateInit (int L, const Matrix &trans, const ArrayList< MixtureGauss > &mixs, Matrix seq, Vector states)
	Generating a sequence and states using transition and emission probabilities.
static success_t	LoadProfile (const char profile, Matrix trans, ArrayList< MixtureGauss > *mixs)
static success_t	SaveProfile (const char *profile, const Matrix &trans, const ArrayList< MixtureGauss > &mixs)
static void	Train (const ArrayList< Matrix > &seqs, Matrix guessTR, ArrayList< MixtureGauss > guessMG, int max_iter, double tol)
	Baum-Welch and Viterbi estimation of transition and emission distribution (Gaussian).
static void	TrainViterbi (const ArrayList< Matrix > &seqs, Matrix guessTR, ArrayList< MixtureGauss > guessMG, int max_iter, double tol)
static double	ViterbiInit (int L, const Matrix &trans, const Matrix &emis_prob, Vector *states)
static double	ViterbiInit (const Matrix &trans, const Matrix &emis_prob, Vector *states)
	Calculate the most probable states for a sequence Viterbi algorithm trans: Transition probabilities, size M x M emis_prob: Emission probabilities, size M x L states: Unitialized, will have length L RETURN: log probability of the most probable sequence.

Detailed Description

A wrapper class for HMM functionals in Mixture of Gaussion case.

This class maintains transition probabilities and Mixture of Gaussian parameters (mean and covariance) for each state, more details below.

Definition at line 53 of file mixgaussHMM.h.

Member Function Documentation

void MixtureofGaussianHMM::ComputeLogLikelihood	(	const ArrayList< Matrix > &	list_data_seq,
		ArrayList< double > *	list_likelihood
	)			const

Compute the log-likelihood of a list of sequences.

Definition at line 133 of file mixgaussHMM.cc.

References ForwardProcedure(), ArrayList< TElem >::Init(), GenVector< T >::Init(), ArrayList< TElem >::PushBackCopy(), and ArrayList< TElem >::size().

double MixtureofGaussianHMM::ComputeLogLikelihood ( const Matrix & data_seq ) const

Compute the log-likelihood of a sequence.

Definition at line 118 of file mixgaussHMM.cc.

References ForwardProcedure(), and GenVector< T >::Init().

void MixtureofGaussianHMM::ComputeViterbiStateSequence	(	const Matrix &	data_seq,
		Vector *	state_seq
	)			const

Compute the most probable sequence (Viterbi).

Definition at line 153 of file mixgaussHMM.cc.

References ViterbiInit().

void MixtureofGaussianHMM::DecodeInit	(	const Matrix &	data_seq,
		Matrix *	state_prob_mat,
		Matrix *	forward_prob_mat,
		Matrix *	backward_prob_mat,
		Vector *	scale_vec
	)			const

A decode version that initialized the output matrices.

Definition at line 102 of file mixgaussHMM.cc.

References GenVector< T >::Init().

void MixtureofGaussianHMM::DecodeOverwrite	(	const Matrix &	data_seq,
		Matrix *	state_prob_mat,
		Matrix *	forward_prob_mat,
		Matrix *	backward_prob_mat,
		Vector *	scale_vec
	)			const

Decode a sequence into probabilities of each state at each time step using scaled forward-backward algorithm.

Also return forward, backward probabilities and scale factors

Definition at line 90 of file mixgaussHMM.cc.

void MixtureofGaussianHMM::EstimateInit	(	int	NumClusters,
		const Matrix &	seq,
		const Vector &	states,
		Matrix *	trans,
		ArrayList< MixtureGauss > *	mixs
	)			`[static]`

Estimate transition and emission distribution from sequence and states.

Definition at line 320 of file mixgaussHMM.cc.

References GenVector< T >::length().

Referenced by EstimateModel().

void MixtureofGaussianHMM::EstimateModel	(	int	numcluster,
		const Matrix &	data_seq,
		const Vector &	state_seq
	)

Estimate the matrices by a data sequence and a state sequence Must be already initialized.

Definition at line 74 of file mixgaussHMM.cc.

References EstimateInit().

void MixtureofGaussianHMM::ForwardProcedure	(	int	L,
		const Matrix &	trans,
		const Matrix &	emis_prob,
		Vector *	scales,
		Matrix *	fs
	)			`[static]`

Calculate posteriori probabilities of states at each steps Scaled Forward - Backward procedure trans: Transition probabilities, size M x M emis_prob: Emission probabilities along the sequence, size M x L (L is the sequence length) pstates: size M x L fs: scaled forward probabities, size M x L bs: scaled backward probabities, size M x L scales: scale factors, length L RETURN: log probabilities of sequence.

Definition at line 329 of file mixgaussHMM.cc.

Referenced by ComputeLogLikelihood().

void MixtureofGaussianHMM::GenerateInit	(	int	L,
		const Matrix &	trans,
		const ArrayList< MixtureGauss > &	mixs,
		Matrix *	seq,
		Vector *	states
	)			`[static]`

Generating a sequence and states using transition and emission probabilities.

L: sequence length trans: Matrix M x M (M states) means: list of mean vectors length N (emission vector of length N) covs: list of square root of covariance matrices size N x N seq: generated sequence, uninitialized matrix, will have size N x L states: generated states, uninitialized vector, will have length L

Definition at line 216 of file mixgaussHMM.cc.

References GenVector< T >::Init(), and ArrayList< TElem >::size().

Referenced by GenerateSequence().

void MixtureofGaussianHMM::GenerateSequence	(	int	L,
		Matrix *	data_seq,
		Vector *	state_seq
	)			const

Generate a random data sequence of a given length.

Definition at line 70 of file mixgaussHMM.cc.

References GenerateInit().

void MixtureofGaussianHMM::Init ( ) [inline]

Initializes empty object.

Definition at line 82 of file mixgaussHMM.h.

References ArrayList< TElem >::Init().

void MixtureofGaussianHMM::Init	(	const Matrix &	transmission,
		const ArrayList< MixtureGauss > &	list_mixture_gauss
	)

Initializes from computed transmission and Mixture of Gaussian parameters.

void MixtureofGaussianHMM::InitFromFile ( const char * profile )

Initializes by loading from a file.

Definition at line 55 of file mixgaussHMM.cc.

References LoadProfile().

Referenced by LoadProfile().

void MixtureofGaussianHMM::LoadProfile ( const char * profile )

Load from file, used when already initialized.

Definition at line 60 of file mixgaussHMM.cc.

References InitFromFile().

Referenced by InitFromFile().

void MixtureofGaussianHMM::SaveProfile ( const char * profile ) const

Save matrices to file.

Definition at line 66 of file mixgaussHMM.cc.

void MixtureofGaussianHMM::setModel	(	const Matrix &	transmission,
		const ArrayList< MixtureGauss > &	list_mixture_gauss
	)

Setter used when already initialized.

Definition at line 45 of file mixgaussHMM.cc.

References ArrayList< TElem >::InitCopy(), and ArrayList< TElem >::size().

void MixtureofGaussianHMM::Train	(	const ArrayList< Matrix > &	seqs,
		Matrix *	guessTR,
		ArrayList< MixtureGauss > *	guessMG,
		int	max_iter,
		double	tol
	)			`[static]`

Baum-Welch and Viterbi estimation of transition and emission distribution (Gaussian).

Definition at line 368 of file mixgaussHMM.cc.

References GenVector< T >::get(), ArrayList< TElem >::Init(), GenVector< T >::Init(), GenMatrix< T >::Init(), ArrayList< TElem >::PushBackCopy(), GenVector< T >::SetZero(), and ArrayList< TElem >::size().

Referenced by TrainBaumWelch().

void MixtureofGaussianHMM::TrainBaumWelch	(	const ArrayList< Matrix > &	list_data_seq,
		int	max_iteration,
		double	tolerance
	)

Train the model with a list of sequences, must be already initialized using Baum-Welch EM algorithm.

Definition at line 161 of file mixgaussHMM.cc.

References Train().

void MixtureofGaussianHMM::TrainViterbi	(	const ArrayList< Matrix > &	list_data_seq,
		int	max_iteration,
		double	tolerance
	)

Train the model with a list of sequences, must be already initialized using Viterbi algorithm to determine the state sequence of each sequence.

Definition at line 165 of file mixgaussHMM.cc.

const Matrix& MixtureofGaussianHMM::transmission ( ) const [inline]

Getters.

Definition at line 68 of file mixgaussHMM.h.

double MixtureofGaussianHMM::ViterbiInit	(	const Matrix &	trans,
		const Matrix &	emis_prob,
		Vector *	states
	)			`[static]`

Calculate the most probable states for a sequence Viterbi algorithm trans: Transition probabilities, size M x M emis_prob: Emission probabilities, size M x L states: Unitialized, will have length L RETURN: log probability of the most probable sequence.

Definition at line 349 of file mixgaussHMM.cc.

Referenced by ComputeViterbiStateSequence().

The documentation for this class was generated from the following files:

mlpack/hmm/mixgaussHMM.h
mlpack/hmm/mixgaussHMM.cc

MixtureofGaussianHMM Class Reference

Public Member Functions

Static Public Member Functions

Detailed Description

Member Function Documentation