14 #ifndef MLPACK_METHODS_GMM_EM_FIT_HPP
15 #define MLPACK_METHODS_GMM_EM_FIT_HPP
41 template<
typename InitialClusteringType = kmeans::KMeans<>,
42 typename CovarianceConstra
intPolicy = PositiveDefiniteConstra
int>
63 EMFit(
const size_t maxIterations = 300,
64 const double tolerance = 1e-10,
65 InitialClusteringType clusterer = InitialClusteringType(),
66 CovarianceConstraintPolicy constraint = CovarianceConstraintPolicy());
83 void Estimate(
const arma::mat& observations,
84 std::vector<distribution::GaussianDistribution>& dists,
86 const bool useInitialModel =
false);
105 void Estimate(
const arma::mat& observations,
106 const arma::vec& probabilities,
107 std::vector<distribution::GaussianDistribution>& dists,
109 const bool useInitialModel =
false);
112 const InitialClusteringType&
Clusterer()
const {
return clusterer; }
114 InitialClusteringType&
Clusterer() {
return clusterer; }
117 const CovarianceConstraintPolicy&
Constraint()
const {
return constraint; }
119 CovarianceConstraintPolicy&
Constraint() {
return constraint; }
132 template<
typename Archive>
133 void Serialize(Archive& ar,
const unsigned int version);
146 void InitialClustering(
const arma::mat& observations,
147 std::vector<distribution::GaussianDistribution>& dists,
160 double LogLikelihood(
const arma::mat& data,
161 const std::vector<distribution::GaussianDistribution>&
163 const arma::vec& weights)
const;
166 size_t maxIterations;
170 InitialClusteringType clusterer;
172 CovarianceConstraintPolicy constraint;
179 #include "em_fit_impl.hpp"
This class contains methods which can fit a GMM to observations using the EM algorithm.
The core includes that mlpack expects; standard C++ includes and Armadillo.
double Tolerance() const
Get the tolerance for the convergence of the EM algorithm.
double & Tolerance()
Modify the tolerance for the convergence of the EM algorithm.
const InitialClusteringType & Clusterer() const
Get the clusterer.
void Serialize(Archive &ar, const unsigned int version)
Serialize the fitter.
size_t & MaxIterations()
Modify the maximum number of iterations of the EM algorithm.
InitialClusteringType & Clusterer()
Modify the clusterer.
const CovarianceConstraintPolicy & Constraint() const
Get the covariance constraint policy class.
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.
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...
CovarianceConstraintPolicy & Constraint()
Modify the covariance constraint policy class.
size_t MaxIterations() const
Get the maximum number of iterations of the EM algorithm.