This class contains methods which can fit a GMM to observations using the EM algorithm. More...

Public Member Functions
	EMFit (const size_t maxIterations=300, const double tolerance=1e-10, InitialClusteringType clusterer=InitialClusteringType(), CovarianceConstraintPolicy constraint=CovarianceConstraintPolicy())
	Construct the EMFit object, optionally passing an InitialClusteringType object (just in case it needs to store state). More...

const InitialClusteringType &	Clusterer () const
	Get the clusterer. More...

InitialClusteringType &	Clusterer ()
	Modify the clusterer. More...

const CovarianceConstraintPolicy &	Constraint () const
	Get the covariance constraint policy class. More...

CovarianceConstraintPolicy &	Constraint ()
	Modify the covariance constraint policy class. More...

void	Estimate (const arma::mat &observations, std::vector< distribution::GaussianDistribution > &dists, arma::vec &weights, const bool useInitialModel=false)
	Fit the observations to a Gaussian mixture model (GMM) using the EM algorithm. More...

void	Estimate (const arma::mat &observations, const arma::vec &probabilities, std::vector< distribution::GaussianDistribution > &dists, arma::vec &weights, const bool useInitialModel=false)
	Fit the observations to a Gaussian mixture model (GMM) using the EM algorithm, taking into account the probabilities of each point being from this mixture. More...

size_t	MaxIterations () const
	Get the maximum number of iterations of the EM algorithm. More...

size_t &	MaxIterations ()
	Modify the maximum number of iterations of the EM algorithm. More...

template<typename Archive >
void	Serialize (Archive &ar, const unsigned int version)
	Serialize the fitter. More...

double	Tolerance () const
	Get the tolerance for the convergence of the EM algorithm. More...

double &	Tolerance ()
	Modify the tolerance for the convergence of the EM algorithm. More...

Private Member Functions
void	InitialClustering (const arma::mat &observations, std::vector< distribution::GaussianDistribution > &dists, arma::vec &weights)
	Run the clusterer, and then turn the cluster assignments into Gaussians. More...

double	LogLikelihood (const arma::mat &data, const std::vector< distribution::GaussianDistribution > &dists, const arma::vec &weights) const
	Calculate the log-likelihood of a model. More...

Private Attributes
InitialClusteringType	clusterer
	Object which will perform the clustering. More...

CovarianceConstraintPolicy	constraint
	Object which applies constraints to the covariance matrix. More...

size_t	maxIterations
	Maximum iterations of EM algorithm. More...

double	tolerance
	Tolerance for convergence of EM. More...

Detailed Description

template<typename InitialClusteringType = kmeans::KMeans<>, typename CovarianceConstraintPolicy = PositiveDefiniteConstraint>
class mlpack::gmm::EMFit< InitialClusteringType, CovarianceConstraintPolicy >

This class contains methods which can fit a GMM to observations using the EM algorithm.

It requires an initial clustering mechanism, which is by default the KMeans algorithm. The clustering mechanism must implement the following method:

void Cluster(const arma::mat& observations, const size_t clusters, arma::Row<size_t>& assignments);

This method should create 'clusters' clusters, and return the assignment of each point to a cluster.

Definition at line 43 of file em_fit.hpp.

Constructor & Destructor Documentation

template<typename InitialClusteringType = kmeans::KMeans<>, typename CovarianceConstraintPolicy = PositiveDefiniteConstraint>

mlpack::gmm::EMFit< InitialClusteringType, CovarianceConstraintPolicy >::EMFit	(	const size_t	maxIterations = `300`,
		const double	tolerance = `1e-10`,
		InitialClusteringType	clusterer = `InitialClusteringType()`,
		CovarianceConstraintPolicy	constraint = `CovarianceConstraintPolicy()`
	)

Construct the EMFit object, optionally passing an InitialClusteringType object (just in case it needs to store state).

Setting the maximum number of iterations to 0 means that the EM algorithm will iterate until convergence (with the given tolerance).

The parameter forcePositive controls whether or not the covariance matrices are checked for positive definiteness at each iteration. This could be a time-consuming task, so, if you know your data is well-behaved, you can set it to false and save some runtime.

Parameters

maxIterations	Maximum number of iterations for EM.
tolerance	Log-likelihood tolerance required for convergence.
forcePositive	Check for positive-definiteness of each covariance matrix at each iteration.
clusterer	Object which will perform the initial clustering.

Member Function Documentation

template<typename InitialClusteringType = kmeans::KMeans<>, typename CovarianceConstraintPolicy = PositiveDefiniteConstraint>

const InitialClusteringType& mlpack::gmm::EMFit< InitialClusteringType, CovarianceConstraintPolicy >::Clusterer ( ) const

inline

Get the clusterer.

Definition at line 112 of file em_fit.hpp.

References mlpack::gmm::EMFit< InitialClusteringType, CovarianceConstraintPolicy >::clusterer.

template<typename InitialClusteringType = kmeans::KMeans<>, typename CovarianceConstraintPolicy = PositiveDefiniteConstraint>

InitialClusteringType& mlpack::gmm::EMFit< InitialClusteringType, CovarianceConstraintPolicy >::Clusterer ( )

inline

Modify the clusterer.

Definition at line 114 of file em_fit.hpp.

References mlpack::gmm::EMFit< InitialClusteringType, CovarianceConstraintPolicy >::clusterer.

template<typename InitialClusteringType = kmeans::KMeans<>, typename CovarianceConstraintPolicy = PositiveDefiniteConstraint>

const CovarianceConstraintPolicy& mlpack::gmm::EMFit< InitialClusteringType, CovarianceConstraintPolicy >::Constraint ( ) const

inline

Get the covariance constraint policy class.

Definition at line 117 of file em_fit.hpp.

References mlpack::gmm::EMFit< InitialClusteringType, CovarianceConstraintPolicy >::constraint.

template<typename InitialClusteringType = kmeans::KMeans<>, typename CovarianceConstraintPolicy = PositiveDefiniteConstraint>

CovarianceConstraintPolicy& mlpack::gmm::EMFit< InitialClusteringType, CovarianceConstraintPolicy >::Constraint ( )

inline

Modify the covariance constraint policy class.

Definition at line 119 of file em_fit.hpp.

References mlpack::gmm::EMFit< InitialClusteringType, CovarianceConstraintPolicy >::constraint.

template<typename InitialClusteringType = kmeans::KMeans<>, typename CovarianceConstraintPolicy = PositiveDefiniteConstraint>

void mlpack::gmm::EMFit< InitialClusteringType, CovarianceConstraintPolicy >::Estimate	(	const arma::mat &	observations,
		std::vector< distribution::GaussianDistribution > &	dists,
		arma::vec &	weights,
		const bool	useInitialModel = `false`
	)

Fit the observations to a Gaussian mixture model (GMM) using the EM algorithm.

The size of the vectors (indicating the number of components) must already be set. Optionally, if useInitialModel is set to true, then the model given in the means, covariances, and weights parameters is used as the initial model, instead of using the InitialClusteringType::Cluster() option.

Parameters

observations	List of observations to train on.
means	Vector to store trained means in.
covariances	Vector to store trained covariances in.
weights	Vector to store a priori weights in.
useInitialModel	If true, the given model is used for the initial clustering.

template<typename InitialClusteringType = kmeans::KMeans<>, typename CovarianceConstraintPolicy = PositiveDefiniteConstraint>

void mlpack::gmm::EMFit< InitialClusteringType, CovarianceConstraintPolicy >::Estimate	(	const arma::mat &	observations,
		const arma::vec &	probabilities,
		std::vector< distribution::GaussianDistribution > &	dists,
		arma::vec &	weights,
		const bool	useInitialModel = `false`
	)

Fit the observations to a Gaussian mixture model (GMM) using the EM algorithm, taking into account the probabilities of each point being from this mixture.

The size of the vectors (indicating the number of components) must already be set. Optionally, if useInitialModel is set to true, then the model given in the means, covariances, and weights parameters is used as the initial model, instead of using the InitialClusteringType::Cluster() option.

Parameters

observations	List of observations to train on.
probabilities	Probability of each point being from this model.
means	Vector to store trained means in.
covariances	Vector to store trained covariances in.
weights	Vector to store a priori weights in.
useInitialModel	If true, the given model is used for the initial clustering.

template<typename InitialClusteringType = kmeans::KMeans<>, typename CovarianceConstraintPolicy = PositiveDefiniteConstraint>

void mlpack::gmm::EMFit< InitialClusteringType, CovarianceConstraintPolicy >::InitialClustering	(	const arma::mat &	observations,
		std::vector< distribution::GaussianDistribution > &	dists,
		arma::vec &	weights
	)

private

Run the clusterer, and then turn the cluster assignments into Gaussians.

This is a helper function for both overloads of Estimate(). The vectors must be already set to the number of clusters.

Parameters

observations	List of observations.
means	Vector to store means in.
covariances	Vector to store covariances in.
weights	Vector to store a priori weights in.

Referenced by mlpack::gmm::EMFit< InitialClusteringType, CovarianceConstraintPolicy >::Tolerance().

template<typename InitialClusteringType = kmeans::KMeans<>, typename CovarianceConstraintPolicy = PositiveDefiniteConstraint>

double mlpack::gmm::EMFit< InitialClusteringType, CovarianceConstraintPolicy >::LogLikelihood	(	const arma::mat &	data,
		const std::vector< distribution::GaussianDistribution > &	dists,
		const arma::vec &	weights
	)		const

private

Calculate the log-likelihood of a model.

Yes, this is reimplemented in the GMM code. Intuition suggests that the log-likelihood is not the best way to determine if the EM algorithm has converged.

Parameters

data	Data matrix.
means	Vector of means.
covariances	Vector of covariance matrices.
weights	Vector of a priori weights.

Referenced by mlpack::gmm::EMFit< InitialClusteringType, CovarianceConstraintPolicy >::Tolerance().

template<typename InitialClusteringType = kmeans::KMeans<>, typename CovarianceConstraintPolicy = PositiveDefiniteConstraint>

size_t mlpack::gmm::EMFit< InitialClusteringType, CovarianceConstraintPolicy >::MaxIterations ( ) const

inline

Get the maximum number of iterations of the EM algorithm.

Definition at line 122 of file em_fit.hpp.

References mlpack::gmm::EMFit< InitialClusteringType, CovarianceConstraintPolicy >::maxIterations.

template<typename InitialClusteringType = kmeans::KMeans<>, typename CovarianceConstraintPolicy = PositiveDefiniteConstraint>

size_t& mlpack::gmm::EMFit< InitialClusteringType, CovarianceConstraintPolicy >::MaxIterations ( )

inline

Modify the maximum number of iterations of the EM algorithm.

Definition at line 124 of file em_fit.hpp.

References mlpack::gmm::EMFit< InitialClusteringType, CovarianceConstraintPolicy >::maxIterations.

template<typename InitialClusteringType = kmeans::KMeans<>, typename CovarianceConstraintPolicy = PositiveDefiniteConstraint>

template<typename Archive >

void mlpack::gmm::EMFit< InitialClusteringType, CovarianceConstraintPolicy >::Serialize	(	Archive &	ar,
		const unsigned int	version
	)

Serialize the fitter.

Referenced by mlpack::gmm::EMFit< InitialClusteringType, CovarianceConstraintPolicy >::Tolerance().

template<typename InitialClusteringType = kmeans::KMeans<>, typename CovarianceConstraintPolicy = PositiveDefiniteConstraint>

double mlpack::gmm::EMFit< InitialClusteringType, CovarianceConstraintPolicy >::Tolerance ( ) const

inline

Get the tolerance for the convergence of the EM algorithm.

Definition at line 127 of file em_fit.hpp.

References mlpack::gmm::EMFit< InitialClusteringType, CovarianceConstraintPolicy >::tolerance.

template<typename InitialClusteringType = kmeans::KMeans<>, typename CovarianceConstraintPolicy = PositiveDefiniteConstraint>

double& mlpack::gmm::EMFit< InitialClusteringType, CovarianceConstraintPolicy >::Tolerance ( )

inline

Modify the tolerance for the convergence of the EM algorithm.

Definition at line 129 of file em_fit.hpp.

References mlpack::gmm::EMFit< InitialClusteringType, CovarianceConstraintPolicy >::InitialClustering(), mlpack::gmm::EMFit< InitialClusteringType, CovarianceConstraintPolicy >::LogLikelihood(), mlpack::gmm::EMFit< InitialClusteringType, CovarianceConstraintPolicy >::Serialize(), and mlpack::gmm::EMFit< InitialClusteringType, CovarianceConstraintPolicy >::tolerance.

Member Data Documentation

template<typename InitialClusteringType = kmeans::KMeans<>, typename CovarianceConstraintPolicy = PositiveDefiniteConstraint>

InitialClusteringType mlpack::gmm::EMFit< InitialClusteringType, CovarianceConstraintPolicy >::clusterer

private

Object which will perform the clustering.

Definition at line 170 of file em_fit.hpp.

Referenced by mlpack::gmm::EMFit< InitialClusteringType, CovarianceConstraintPolicy >::Clusterer().

template<typename InitialClusteringType = kmeans::KMeans<>, typename CovarianceConstraintPolicy = PositiveDefiniteConstraint>

CovarianceConstraintPolicy mlpack::gmm::EMFit< InitialClusteringType, CovarianceConstraintPolicy >::constraint

private

Object which applies constraints to the covariance matrix.

Definition at line 172 of file em_fit.hpp.

Referenced by mlpack::gmm::EMFit< InitialClusteringType, CovarianceConstraintPolicy >::Constraint().

template<typename InitialClusteringType = kmeans::KMeans<>, typename CovarianceConstraintPolicy = PositiveDefiniteConstraint>

size_t mlpack::gmm::EMFit< InitialClusteringType, CovarianceConstraintPolicy >::maxIterations

private

Maximum iterations of EM algorithm.

Definition at line 166 of file em_fit.hpp.

Referenced by mlpack::gmm::EMFit< InitialClusteringType, CovarianceConstraintPolicy >::MaxIterations().

template<typename InitialClusteringType = kmeans::KMeans<>, typename CovarianceConstraintPolicy = PositiveDefiniteConstraint>

double mlpack::gmm::EMFit< InitialClusteringType, CovarianceConstraintPolicy >::tolerance

private

Tolerance for convergence of EM.

Definition at line 168 of file em_fit.hpp.

Referenced by mlpack::gmm::EMFit< InitialClusteringType, CovarianceConstraintPolicy >::Tolerance().

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

src/mlpack/methods/gmm/em_fit.hpp

Public Member Functions

Private Member Functions

Private Attributes

Detailed Description

template<typename InitialClusteringType = kmeans::KMeans<>, typename CovarianceConstraintPolicy = PositiveDefiniteConstraint> class mlpack::gmm::EMFit< InitialClusteringType, CovarianceConstraintPolicy >

Constructor & Destructor Documentation

Member Function Documentation

Member Data Documentation

template<typename InitialClusteringType = kmeans::KMeans<>, typename CovarianceConstraintPolicy = PositiveDefiniteConstraint>
class mlpack::gmm::EMFit< InitialClusteringType, CovarianceConstraintPolicy >