NAME

       mlpack_gmm_train - gaussian mixture model (gmm) training

SYNOPSIS

        mlpack_gmm_train [-h] [-v] -g int -i string [-m string] [-n int] [-P] [-N double] [-M string] [-p double] [-r] [-S int] [-s int] [-T double] [-t int] -V

DESCRIPTION

       This  program  takes  a parametric estimate of a Gaussian mixture model (GMM) using the EM
       algorithm to find the maximum likelihood estimate. The model may be saved to  file,  which
       will contain information about each Gaussian.

       If  GMM  training  fails  with  an  error indicating that a covariance matrix could not be
       inverted, make sure that the --no_force_positive  flag  is  not  specified.   Alternately,
       adding  a  small  amount  of  Gaussian  noise  (using the --noise parameter) to the entire
       dataset may help prevent Gaussians with zero variance in a particular dimension, which  is
       usually the cause of non-invertible covariance matrices.

       The 'no_force_positive' flag, if set, will avoid the checks after each iteration of the EM
       algorithm which ensure that the covariance matrices are positive definite. Specifying  the
       flag  can  cause  faster  runtime,  but  may  also  cause non-positive definite covariance
       matrices, which will cause the program to crash.

       Optionally, multiple trials may be performed, by specifying the --trials option. The model
       with greatest log-likelihood will be taken.

REQUIRED OPTIONS

       --gaussians (-g) [int]
              Number of Gaussians in the GMM.

       --input_file (-i) [string]
              File containing the data on which the model will be fit.

OPTIONS

       --help (-h)
              Default help info.

       --info [string]
              Get help on a specific module or option.  Default value ''.

       --input_model_file (-m) [string]
              File containing initial input GMM model.  Default value ''.

       --max_iterations (-n) [int]
              Maximum   number   of  iterations  of  EM  algorithm  (passing  0  will  run  until
              convergence). Default value 250.

       --no_force_positive (-P)
              Do not force the covariance matrices to be positive definite.

       --noise (-N) [double]
              Variance of zero-mean Gaussian noise to add to data. Default value 0.

       --output_model_file (-M) [string]
              File to save trained GMM model to. Default value ''.

       --percentage (-p) [double]
              If using --refined_start, specify the percentage  of  the  dataset  used  for  each
              sampling (should be between 0.0 and 1.0). Default value 0.02.

       --refined_start (-r)
              During  the  initialization,  use  refined initial positions for k-means clustering
              (Bradley and Fayyad, 1998).

       --samplings (-S) [int]
              If using --refined_start, specify the number of samplings used for initial  points.
              Default value 100.

       --seed (-s) [int]
              Random seed. If 0, 'std::time(NULL)' is used.  Default value 0.

       --tolerance (-T) [double]
              Tolerance for convergence of EM. Default value 1e-10.

       --trials (-t) [int]
              Number of trials to perform in training GMM.  Default value 1.

       --verbose (-v)
              Display  informational  messages  and the full list of parameters and timers at the
              end of execution.

       --version (-V)
              Display the version of mlpack.

ADDITIONAL INFORMATION

       For further information, including relevant papers, citations,  and  theory,  consult  the
       documentation found at http://www.mlpack.org or included with your DISTRIBUTION OF MLPACK.

                                                                              mlpack_gmm_train(1)