A model of a temporal sequence consisting of k discrete states each with a gaussian emission probability depending on the prior continuous state. More...

#include <LinearProcessHMM.h>

Public Member Functions
	LinearProcessHMM (int k, int d)

	LinearProcessHMM (const GaussianMixtureModel &)

int	NumDims () const

void	Resize (int k, int d)

void	SetUniformTransitions ()

void	SetUniformExitProbabilities (Real pExit)

bool	TrainEM (const std::vector< std::vector< Vector > > &examples, Real &tol, int maxIters, int verbose=0)
	Solves the max likelihood model from training data. More...

bool	TrainDiagonalEM (const std::vector< std::vector< Vector > > &examples, Real &tol, int maxIters, int verbose=0)

Real	LogLikelihood (const std::vector< int > &dstates, const std::vector< Vector > &observations) const
	Computes the log likelihood of the time series.

Real	LogLikelihood (const std::vector< Vector > &observations) const

Real	Probability (const Vector &p0, const Vector &prevObs, const Vector &obs) const

void	Predict (const Vector &p0, Vector &pt, int numsteps=1) const

void	Update (const Vector &p0, const Vector &prevObs, const Vector &obs, Vector &pobs) const

void	Filter (const Vector &p0, const Vector &prevObs, const Vector &obs, Vector &pnext) const

void	ObservationDistribution (const Vector &pt, const Vector &prevObs, GaussianMixtureModel &model) const

void	MAP (const std::vector< Vector > &observations, std::vector< int > &dstates) const
	Computes the MAP assignment using the Viterbi algorithm.

void	Posterior (const std::vector< Vector > &observations, std::vector< Vector > &pstate) const

void	Posterior (const std::vector< Vector > &observations, std::vector< Vector > &pstate, Matrix &tstate) const

Public Attributes
Vector	discretePrior

Matrix	transitionMatrix

std::vector< Gaussian< Real > >	continuousPriors

std::vector< LinearProcess >	emissionModels

Detailed Description

A model of a temporal sequence consisting of k discrete states each with a gaussian emission probability depending on the prior continuous state.

The graphical model representation is as follows: x0 -> x1 -> ... | | v v o0 -> o1 -> ...

where each P(o[t] | x[t], o[t-1]) is a linear process o[t]=Ao[t-1]+b+err with the coefficients A, b, and error covariance depending on x[t].

Member Function Documentation

void Statistics::LinearProcessHMM::Filter	(	const Vector &	p0,
		const Vector &	prevObs,
		const Vector &	obs,
		Vector &	pnext
	)		const

Given prior distribution over previous discrete state p0, the previous observation, and the current observation obs, return the posterior distribution over the current state (shortcut to Predict + Update)

void Statistics::LinearProcessHMM::ObservationDistribution	(	const Vector &	pt,
		const Vector &	prevObs,
		GaussianMixtureModel &	model
	)		const

Given distribution over discrete states and the previous observation, compute distribution over the current observation

References Statistics::GaussianMixtureModel::phi.

void Statistics::LinearProcessHMM::Posterior	(	const std::vector< Vector > &	observations,
		std::vector< Vector > &	pstate
	)		const

Computes the posterior probabilities of each discrete state using the forward-backward algorithm

void Statistics::LinearProcessHMM::Posterior	(	const std::vector< Vector > &	observations,
		std::vector< Vector > &	pstate,
		Matrix &	tstate
	)		const

Computes the posterior probabilities of each discrete state and accumulates state-to-state transition pairs using the forward-backward algorithm

References Statistics::ExpNormalize(), Math::GaussianCondition(), Math::GaussianMarginalize(), Math::GetElements(), Statistics::Normalize(), Statistics::GaussianMixtureModelRaw::phi, Math::Gaussian< T >::probability(), Math::RandomAllocate(), and Math::SetElements().

void Statistics::LinearProcessHMM::Predict	(	const Vector &	p0,
		Vector &	pt,
		int	numsteps = `1`
	)		const

Given prior distribution over discrete states p0, predicts the distribution after numsteps time steps

bool Statistics::LinearProcessHMM::TrainEM	(	const std::vector< std::vector< Vector > > &	examples,
		Real &	tol,
		int	maxIters,
		int	verbose = `0`
	)

Solves the max likelihood model from training data.

Runs expectation-maximization, starting from current values of gaussians, given the training data, a tolerance, # of max iterations. Returns true if tolerance was reached, returns log-likelihood of data in tol.

References Math::IsFinite().

void Statistics::LinearProcessHMM::Update	(	const Vector &	p0,
		const Vector &	prevObs,
		const Vector &	obs,
		Vector &	pobs
	)		const

Given prior distribution over current state p0, the previous observation, and the current observation obs, updates the state distribution

References Statistics::ExpNormalize(), and Math::IsInf().

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

LinearProcessHMM.h
LinearProcessHMM.cpp

Public Member Functions

Public Attributes

Detailed Description

Member Function Documentation