[mlpack-svn] [MLPACK] #211: Suggested additional functionality to GMM

[MLPACK Trac]

#211: Suggested additional functionality to GMM
-----------------------------------+----------------------------------------
  Reporter:  Adam                  |        Owner:     
      Type:  enhancement           |       Status:  new
  Priority:  major                 |    Milestone:     
 Component:  mlpack                |   Resolution:     
  Keywords:  GMM, EM, IGMM, DPGMM  |     Blocking:     
Blocked By:                        |  
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Comment (by rcurtin):

 Okay; I've been working on this for a while.  Now GMM is a templated class
 which allows any number of fitting methods.  The default is the EM
 algorithm (which can use any initial clustering mechanism, other than the
 default KMeans):

 {{{
 template<typename FittingType = EMFit<> >
 class GMM<FittingType>;
 }}}

 The FittingType class needs to implement the following two functions:

 {{{
 void Estimate(const arma::mat& observations,
               std::vector<arma::vec>& means,
               std::vector<arma::mat>& covariances,
               arma::vec& weights);

 void Estimate(const arma::mat& observations,
               const arma::vec& probabilities,
               std::vector<arma::vec>& means,
               std::vector<arma::mat>& covariances,
               arma::vec& weights);
 }}}

 These functions should produce a trained GMM from the given observations
 and probabilities.  These may modify the size of the model (by increasing
 the size of the mean and covariance vectors as well as the weight
 vectors), but
 the method should expect that these vectors are already set to the size of
 the GMM as specified in the constructor.

 The EMFit type, in `gmm/em_fit.hpp` and `gmm/em_fit_impl.hpp` provide an
 example implementation of the EMFit class.

 So hopefully, if you are writing an IGMM fitting type, you can plug it
 right in without having to modify the GMM code at all.

 If the API needs any changes, let me know.  Sorry this took so long; I
 kept stumbling over simple compilation issues.

-- 
Ticket URL: <http://trac.research.cc.gatech.edu/fastlab/ticket/211#comment:3>
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MLPACK is an intuitive, fast, and scalable C++ machine learning library developed by the FASTLAB at Georgia Tech under Dr. Alex Gray.