A Diagonal Gaussian Mixture Model. More...

#include <diagonal_gmm.hpp>

Public Member Functions
	DiagonalGMM ()
	Create an empty Diagonal Gaussian Mixture Model, with zero gaussians.

	DiagonalGMM (const size_t gaussians, const size_t dimensionality)
	Create a GMM with the given number of Gaussians, each of which have the specified dimensionality. More...

	DiagonalGMM (const std::vector< distribution::DiagonalGaussianDistribution > &dists, const arma::vec &weights)
	Create a DiagonalGMM with the given dists and weights. More...

	DiagonalGMM (const DiagonalGMM &other)
	Copy constructor for DiagonalGMMs.

DiagonalGMM &	operator= (const DiagonalGMM &other)
	Copy operator for DiagonalGMMs.

size_t	Gaussians () const
	Return the number of Gaussians in the model.

size_t	Dimensionality () const
	Return the dimensionality of the model.

const distribution::DiagonalGaussianDistribution &	Component (size_t i) const
	Return a const reference to a component distribution. More...

distribution::DiagonalGaussianDistribution &	Component (size_t i)
	Return a reference to a component distribution. More...

const arma::vec &	Weights () const
	Return a const reference to the a priori weights of each Gaussian.

arma::vec &	Weights ()
	Return a reference to the a priori weights of each Gaussian.

double	Probability (const arma::vec &observation) const
	Return the probability that the given observation came from this distribution. More...

void	Probability (const arma::mat &observation, arma::vec &probs) const
	Return the probability that the given observation matrix. More...

double	LogProbability (const arma::vec &observation) const
	Return the log probability that the given observation came from this distribution. More...

void	LogProbability (const arma::mat &observation, arma::vec &logProbs) const
	Return the log probability that the given observation matrix. More...

double	Probability (const arma::vec &observation, const size_t component) const
	Return the probability that the given observation came from the given Gaussian component in this distribution. More...

double	LogProbability (const arma::vec &observation, const size_t component) const
	Return the log probability that the given observation came from the given Gaussian component in this distribution. More...

arma::vec	Random () const
	Return a randomly generated observation according to the probability distribution defined by this object. More...

template<typename FittingType = EMFit<kmeans::KMeans<>, DiagonalConstraint, distribution::DiagonalGaussianDistribution>>
double	Train (const arma::mat &observations, const size_t trials=1, const bool useExistingModel=false, FittingType fitter=FittingType())
	Estimate the probability distribution directly from the given observations, using the given algorithm in the FittingType class to fit the data. More...

template<typename FittingType = EMFit<kmeans::KMeans<>, DiagonalConstraint, distribution::DiagonalGaussianDistribution>>
double	Train (const arma::mat &observations, const arma::vec &probabilities, const size_t trials=1, const bool useExistingModel=false, FittingType fitter=FittingType())
	Estimate the probability distribution directly from the given observations, taking into account the probability of each observation actually being from this distribution, and using the given algorithm in the FittingType class to fit the data. More...

void	Classify (const arma::mat &observations, arma::Row< size_t > &labels) const
	Classify the given observations as being from an individual component in this DiagonalGMM. More...

template<typename Archive >
void	serialize (Archive &ar, const uint32_t)
	Serialize the DiagonalGMM. More...

Detailed Description

A Diagonal Gaussian Mixture Model.

This class uses maximum likelihood loss functions to estimate the parameters of the DiagonalGMM on a given dataset via the given fitting mechanism, defined by the FittingType template parameter. The DiagonalGMM can be trained using normal data, or data with probabilities of being from this GMM (see DiagonalGMM::Train() for more information). The DiagonalGMM is the same as GMM except for wrapping gmm_diag class.

The Train() method uses a template type 'FittingType'. The FittingType template class must provide a way for the DiagonalGMM to train on data. It must provide the following two functions:

void Estimate(
    const arma::mat& observations,
    std::vector<distribution::DiagonalGaussianDistribution>& dists,
    arma::vec& weights);
void Estimate(
    const arma::mat& observations,
    const arma::vec& probabilities,
    std::vector<distribution::DiagonalGaussianDistribution>& dists,
    arma::vec& weights);

Example use:

// Set up a mixture of 5 gaussians in a 4-dimensional space.
DiagonalGMM g(5, 4);
// Train the DiagonalGMM given the data observations, using the default
// EM fitting mechanism.
g.Train(data);
// Get the probability of 'observation' being observed from this
// DiagoanlGMM.
double probability = g.Probability(observation);
// Get a random observation from the DiagonalGMM.
arma::vec observation = g.Random();

Constructor & Destructor Documentation

◆ DiagonalGMM() [1/2]

mlpack::gmm::DiagonalGMM::DiagonalGMM	(	const size_t	gaussians,
		const size_t	dimensionality
	)

Create a GMM with the given number of Gaussians, each of which have the specified dimensionality.

Create a DiagonalGMM with the given number of Gaussians, each of which have the specified dimensionality.

The means and covariances will be set to 0.

Parameters

gaussians	Number of Gaussians in this DiagonalGMM.
dimensionality	Dimensionality of each Gaussian.

The means and covariances will be set to 0.

Parameters

gaussians	Number of Gaussians in this GMM.
dimensionality	Dimensionality of each Gaussian.

◆ DiagonalGMM() [2/2]

mlpack::gmm::DiagonalGMM::DiagonalGMM	(	const std::vector< distribution::DiagonalGaussianDistribution > &	dists,
		const arma::vec &	weights
	)

inline

Create a DiagonalGMM with the given dists and weights.

Parameters

dists	Distributions of the model.
weights	Weights of the model.

Member Function Documentation

◆ Classify()

void mlpack::gmm::DiagonalGMM::Classify	(	const arma::mat &	observations,
		arma::Row< size_t > &	labels
	)		const

Classify the given observations as being from an individual component in this DiagonalGMM.

Classify the given observations as being from an individual component in this GMM.

The resultant classifications are stored in the 'labels' object, and each label will be between 0 and (Gaussians() - 1). Supposing that a point was classified with label 2, and that our DiagonalGMM object was called 'dgmm', one could access the relevant Gaussian distribution as follows:

arma::vec mean = dgmm.Means()[2];
arma::mat covariance = dgmm.Covariances()[2];
double priorWeight = dgmm.Weights()[2];

Parameters

observations	Matrix of observations to classify.
labels	Object which will be filled with labels.

◆ Component() [1/2]

const distribution::DiagonalGaussianDistribution& mlpack::gmm::DiagonalGMM::Component ( size_t i ) const

inline

Return a const reference to a component distribution.

Parameters

i	Index of component.

◆ Component() [2/2]

distribution::DiagonalGaussianDistribution& mlpack::gmm::DiagonalGMM::Component ( size_t i )

inline

Return a reference to a component distribution.

Parameters

i	Index of component.

◆ LogProbability() [1/3]

double mlpack::gmm::DiagonalGMM::LogProbability ( const arma::vec & observation ) const

Return the log probability that the given observation came from this distribution.

Return the log probability of the given observation being from this GMM.

Parameters

observation Observation to evaluate the log-probability of.

◆ LogProbability() [2/3]

void mlpack::gmm::DiagonalGMM::LogProbability	(	const arma::mat &	observation,
		arma::vec &	logProbs
	)		const

Return the log probability that the given observation matrix.

Return the log probability of the given observation GMM matrix.

Parameters

observation	Observation to evaluate the log-probability of.
logProbs	Stores the value of log-probability for observation.
observation	Observation matrix to compute log-probabilty.
logProbs	Stores the value of log-probability for input.

◆ LogProbability() [3/3]

double mlpack::gmm::DiagonalGMM::LogProbability	(	const arma::vec &	observation,
		const size_t	component
	)		const

Return the log probability that the given observation came from the given Gaussian component in this distribution.

Return the log probability of the given observation being from the given component in the mixture.

Parameters

observation	Observation to evaluate the probability of.
component	Index of the component of the DiagonalGMM.

◆ Probability() [1/3]

double mlpack::gmm::DiagonalGMM::Probability ( const arma::vec & observation ) const

Return the probability that the given observation came from this distribution.

Return the probability of the given observation being from this GMM.

Parameters

observation Observation to evaluate the probability of.

◆ Probability() [2/3]

void mlpack::gmm::DiagonalGMM::Probability	(	const arma::mat &	observation,
		arma::vec &	probs
	)		const

Return the probability that the given observation matrix.

Return the probability of the given observation GMM matrix.

Parameters

observation	Observation to evaluate the probability of.
probs	Stores the value of probability for observation.
observation	Observation matrix to compute probabilty.
probs	Stores the value of probability for observation.

◆ Probability() [3/3]

double mlpack::gmm::DiagonalGMM::Probability	(	const arma::vec &	observation,
		const size_t	component
	)		const

Return the probability that the given observation came from the given Gaussian component in this distribution.

Return the probability of the given observation being from the given component in the mixture.

Parameters

observation	Observation to evaluate the probability of.
component	Index of the component of the DiagonalGMM.

◆ Random()

arma::vec mlpack::gmm::DiagonalGMM::Random ( ) const

Return a randomly generated observation according to the probability distribution defined by this object.

Returns: Random observation from this DiagonalGMM.

◆ serialize()

template<typename Archive >

void mlpack::gmm::DiagonalGMM::serialize	(	Archive &	ar,
		const uint32_t
	)

Serialize the DiagonalGMM.

Serialize the object.

◆ Train() [1/2]

template<typename FittingType >

double mlpack::gmm::DiagonalGMM::Train	(	const arma::mat &	observations,
		const size_t	trials = `1`,
		const bool	useExistingModel = `false`,
		FittingType	fitter = `FittingType()`
	)

Estimate the probability distribution directly from the given observations, using the given algorithm in the FittingType class to fit the data.

Fit the DiagonalGMM to the given observations.

The fitting will be performed 'trials' times; from these trials, the model with the greatest log-likelihood will be selected. By default, only one trial is performed. The log-likelihood of the best fitting is returned.

Optionally, the existing model can be used as an initial model for the estimation by setting 'useExistingModel' to true. If the fitting procedure is deterministic after the initial position is given, then 'trials' should be set to 1.

Parameters

observations	Observations of the model.
trials	Number of trials to perform; the model in these trials with the greatest log-likelihood will be selected.
useExistingModel	If true, the existing model is used as an initial model for the estimation.
fitter	Fitting type that estimates observations.

Returns: The log-likelihood of the best fit.

◆ Train() [2/2]

template<typename FittingType >

double mlpack::gmm::DiagonalGMM::Train	(	const arma::mat &	observations,
		const arma::vec &	probabilities,
		const size_t	trials = `1`,
		const bool	useExistingModel = `false`,
		FittingType	fitter = `FittingType()`
	)

Estimate the probability distribution directly from the given observations, taking into account the probability of each observation actually being from this distribution, and using the given algorithm in the FittingType class to fit the data.

Fit the DiagonalGMM to the given observations, each of which has a certain probability of being from this distribution.

The fitting will be performed 'trials' times; from these trials, the model with the greatest log-likelihood will be selected. By default, only one trial is performed. The log-likelihood of the best fitting is returned.

Optionally, the existing model can be used as an initial model for the estimation by setting 'useExistingModel' to true. If the fitting procedure is deterministic after the initial position is given, then 'trials' should be set to 1.

Parameters

observations	Observations of the model.
probabilities	Probability of each observation being from this distribution.
trials	Number of trials to perform; the model in these trials with the greatest log-likelihood will be selected.
useExistingModel	If true, the existing model is used as an initial model for the estimation.
fitter	Fitting type that estimates observations.

Returns: The log-likelihood of the best fit.

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

src/mlpack/methods/gmm/diagonal_gmm.hpp
src/mlpack/methods/gmm/diagonal_gmm.cpp
src/mlpack/methods/gmm/diagonal_gmm_impl.hpp

Public Member Functions

Detailed Description

Constructor & Destructor Documentation

◆ DiagonalGMM() [1/2]

◆ DiagonalGMM() [2/2]

Member Function Documentation

◆ Classify()

◆ Component() [1/2]

◆ Component() [2/2]

◆ LogProbability() [1/3]

◆ LogProbability() [2/3]

◆ LogProbability() [3/3]

◆ Probability() [1/3]

◆ Probability() [2/3]

◆ Probability() [3/3]

◆ Random()

◆ serialize()

◆ Train() [1/2]

◆ Train() [2/2]