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>
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);
132 template<
typename Archive>
133 void Serialize(Archive& ar,
const unsigned int version);
147 std::vector<distribution::GaussianDistribution>& dists,
161 const std::vector<distribution::GaussianDistribution>&
163 const arma::vec& weights)
const;
179 #include "em_fit_impl.hpp" This class contains methods which can fit a GMM to observations using the EM algorithm.
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.
double LogLikelihood(const arma::mat &data, const std::vector< distribution::GaussianDistribution > &dists, const arma::vec &weights) const
Calculate the log-likelihood of a model.
double & Tolerance()
Modify the tolerance for the convergence of the EM algorithm.
const CovarianceConstraintPolicy & Constraint() const
Get the covariance constraint policy class.
Linear algebra utility functions, generally performed on matrices or vectors.
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.
The core includes that mlpack expects; standard C++ includes and Armadillo.
size_t maxIterations
Maximum iterations of EM algorithm.
CovarianceConstraintPolicy constraint
Object which applies constraints to the covariance matrix.
size_t & MaxIterations()
Modify the maximum number of iterations of the EM algorithm.
size_t MaxIterations() const
Get the maximum number of iterations of the EM algorithm.
InitialClusteringType & Clusterer()
Modify the clusterer.
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.
double Tolerance() const
Get the tolerance for the convergence of the EM algorithm.
const InitialClusteringType & Clusterer() const
Get the clusterer.
InitialClusteringType clusterer
Object which will perform the clustering.
double tolerance
Tolerance for convergence of EM.
void Serialize(Archive &ar, const unsigned int version)
Serialize the fitter.