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em_fit.hpp
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1 
14 #ifndef MLPACK_METHODS_GMM_EM_FIT_HPP
15 #define MLPACK_METHODS_GMM_EM_FIT_HPP
16 
17 #include <mlpack/prereqs.hpp>
19 
20 // Default clustering mechanism.
22 // Default covariance matrix constraint.
24 
25 namespace mlpack {
26 namespace gmm {
27 
41 template<typename InitialClusteringType = kmeans::KMeans<>,
42  typename CovarianceConstraintPolicy = PositiveDefiniteConstraint>
43 class EMFit
44 {
45  public:
63  EMFit(const size_t maxIterations = 300,
64  const double tolerance = 1e-10,
65  InitialClusteringType clusterer = InitialClusteringType(),
66  CovarianceConstraintPolicy constraint = CovarianceConstraintPolicy());
67 
83  void Estimate(const arma::mat& observations,
84  std::vector<distribution::GaussianDistribution>& dists,
85  arma::vec& weights,
86  const bool useInitialModel = false);
87 
105  void Estimate(const arma::mat& observations,
106  const arma::vec& probabilities,
107  std::vector<distribution::GaussianDistribution>& dists,
108  arma::vec& weights,
109  const bool useInitialModel = false);
110 
112  const InitialClusteringType& Clusterer() const { return clusterer; }
114  InitialClusteringType& Clusterer() { return clusterer; }
115 
117  const CovarianceConstraintPolicy& Constraint() const { return constraint; }
119  CovarianceConstraintPolicy& Constraint() { return constraint; }
120 
122  size_t MaxIterations() const { return maxIterations; }
124  size_t& MaxIterations() { return maxIterations; }
125 
127  double Tolerance() const { return tolerance; }
129  double& Tolerance() { return tolerance; }
130 
132  template<typename Archive>
133  void Serialize(Archive& ar, const unsigned int version);
134 
135  private:
146  void InitialClustering(const arma::mat& observations,
147  std::vector<distribution::GaussianDistribution>& dists,
148  arma::vec& weights);
149 
160  double LogLikelihood(const arma::mat& data,
161  const std::vector<distribution::GaussianDistribution>&
162  dists,
163  const arma::vec& weights) const;
164 
168  double tolerance;
170  InitialClusteringType clusterer;
172  CovarianceConstraintPolicy constraint;
173 };
174 
175 } // namespace gmm
176 } // namespace mlpack
177 
178 // Include implementation.
179 #include "em_fit_impl.hpp"
180 
181 #endif
This class contains methods which can fit a GMM to observations using the EM algorithm.
Definition: em_fit.hpp:43
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.
Definition: em_fit.hpp:129
const CovarianceConstraintPolicy & Constraint() const
Get the covariance constraint policy class.
Definition: em_fit.hpp:117
Linear algebra utility functions, generally performed on matrices or vectors.
Definition: binarize.hpp:18
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.
Definition: em_fit.hpp:166
CovarianceConstraintPolicy constraint
Object which applies constraints to the covariance matrix.
Definition: em_fit.hpp:172
size_t & MaxIterations()
Modify the maximum number of iterations of the EM algorithm.
Definition: em_fit.hpp:124
size_t MaxIterations() const
Get the maximum number of iterations of the EM algorithm.
Definition: em_fit.hpp:122
InitialClusteringType & Clusterer()
Modify the clusterer.
Definition: em_fit.hpp:114
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.
Definition: em_fit.hpp:119
double Tolerance() const
Get the tolerance for the convergence of the EM algorithm.
Definition: em_fit.hpp:127
const InitialClusteringType & Clusterer() const
Get the clusterer.
Definition: em_fit.hpp:112
InitialClusteringType clusterer
Object which will perform the clustering.
Definition: em_fit.hpp:170
double tolerance
Tolerance for convergence of EM.
Definition: em_fit.hpp:168
void Serialize(Archive &ar, const unsigned int version)
Serialize the fitter.