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mlpack
fast, flexible C++ machine learning library
EMFit< InitialClusteringType, CovarianceConstraintPolicy, Distribution > Class Template Reference

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 > &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 > &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...

 

Detailed Description


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

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 45 of file em_fit.hpp.

Constructor & Destructor Documentation

◆ EMFit()

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
maxIterationsMaximum number of iterations for EM.
toleranceLog-likelihood tolerance required for convergence.
clustererObject which will perform the initial clustering.
constraintConstraint policy of covariance.

Member Function Documentation

◆ Clusterer() [1/2]

const InitialClusteringType& Clusterer ( ) const
inline

Get the clusterer.

Definition at line 113 of file em_fit.hpp.

◆ Clusterer() [2/2]

InitialClusteringType& Clusterer ( )
inline

Modify the clusterer.

Definition at line 115 of file em_fit.hpp.

◆ Constraint() [1/2]

const CovarianceConstraintPolicy& Constraint ( ) const
inline

Get the covariance constraint policy class.

Definition at line 118 of file em_fit.hpp.

◆ Constraint() [2/2]

CovarianceConstraintPolicy& Constraint ( )
inline

Modify the covariance constraint policy class.

Definition at line 120 of file em_fit.hpp.

◆ Estimate() [1/2]

void Estimate ( const arma::mat &  observations,
std::vector< Distribution > &  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
observationsList of observations to train on.
meansVector to store trained means in.
covariancesVector to store trained covariances in.
weightsVector to store a priori weights in.
useInitialModelIf true, the given model is used for the initial clustering.

◆ Estimate() [2/2]

void Estimate ( const arma::mat &  observations,
const arma::vec &  probabilities,
std::vector< Distribution > &  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
observationsList of observations to train on.
probabilitiesProbability of each point being from this model.
meansVector to store trained means in.
covariancesVector to store trained covariances in.
weightsVector to store a priori weights in.
useInitialModelIf true, the given model is used for the initial clustering.

◆ MaxIterations() [1/2]

size_t MaxIterations ( ) const
inline

Get the maximum number of iterations of the EM algorithm.

Definition at line 123 of file em_fit.hpp.

◆ MaxIterations() [2/2]

size_t& MaxIterations ( )
inline

Modify the maximum number of iterations of the EM algorithm.

Definition at line 125 of file em_fit.hpp.

◆ serialize()

void serialize ( Archive &  ar,
const unsigned int  version 
)

◆ Tolerance() [1/2]

double Tolerance ( ) const
inline

Get the tolerance for the convergence of the EM algorithm.

Definition at line 128 of file em_fit.hpp.

◆ Tolerance() [2/2]

double& Tolerance ( )
inline

Modify the tolerance for the convergence of the EM algorithm.

Definition at line 130 of file em_fit.hpp.

References EMFit< InitialClusteringType, CovarianceConstraintPolicy, Distribution >::serialize().


The documentation for this class was generated from the following file:
  • /home/ryan/src/mlpack.org/_src/mlpack-git/src/mlpack/methods/gmm/em_fit.hpp