Ubuntu Manpage: mlpack_gmm_train - gaussian mixture model (gmm) training

NAME

       mlpack_gmm_train - gaussian mixture model (gmm) training

SYNOPSIS

        mlpack_gmm_train -g int -i string [-d bool] [-m unknown] [-k int] [-n int] [-P bool] [-N double] [-p double] [-r bool] [-S int] [-s int] [-T double] [-t int] [-V bool] [-M unknown] [-h -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 and reused by
other mlpack GMM tools.

The input data to train on must be specified with the '--input_file (-i)' parameter, and
the number of Gaussians in the model must be specified with the ’--gaussians (-g)'
parameter. Optionally, many trials with different random initializations may be run, and
the result with highest log-likelihood on the training data will be taken. The number of
trials to run is specified with the '--trials (-t)' parameter. By default, only one trial
is run.

The tolerance for convergence and maximum number of iterations of the EM algorithm are
specified with the '--tolerance (-T)' and '--max_iterations (-n)' parameters,
respectively. The GMM may be initialized for training with another model, specified with
the '--input_model_file (-m)' parameter. Otherwise, the model is initialized by running
k-means on the data. The k-means clustering initialization can be controlled with the
’--kmeans_max_iterations (-k)', '--refined_start (-r)', '--samplings (-S)', and
'--percentage (-p)' parameters. If '--refined_start (-r)' is specified, then the Bradley-
Fayyad refined start initialization will be used. This can often lead to better clustering
results.

The 'diagonal_covariance' flag will cause the learned covariances to be diagonal matrices.
This significantly simplifies the model itself and causes training to be faster, but
restricts the ability to fit more complex GMMs.

If GMM training fails with an error indicating that a covariance matrix could not be
inverted, make sure that the '--no_force_positive (-P)' parameter is not specified.
Alternately, adding a small amount of Gaussian noise (using the '--noise (-N)' 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 (-P)' parameter, 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.

As an example, to train a 6-Gaussian GMM on the data in 'data.csv' with a maximum of 100
iterations of EM and 3 trials, saving the trained GMM to ’gmm.bin', the following command
can be used:

$ mlpack_gmm_train --input_file data.csv --gaussians 6 --trials 3 --output_model_file
gmm.bin

To re-train that GMM on another set of data 'data2.csv', the following command may be
used:

$ mlpack_gmm_train --input_model_file gmm.bin --input_file data2.csv --gaussians 6
--output_model_file new_gmm.bin

REQUIRED INPUT OPTIONS

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

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

OPTIONAL INPUT OPTIONS

       --diagonal_covariance (-d) [bool]
              Force the covariance of the Gaussians to be diagonal. This can accelerate  training
              time significantly.

       --help (-h) [bool]
              Default help info.

       --info [string]
              Print help on a specific option. Default value ''.

       --input_model_file (-m) [unknown]
              Initial input GMM model to start training with.

       --kmeans_max_iterations (-k) [int]
              Maximum  number  of  iterations  for the k-means algorithm (used to initialize EM).
              Default value 1000.

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

       --no_force_positive (-P) [bool]
              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.

       --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) [bool]
              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) [bool]
              Display informational messages and the full list of parameters and  timers  at  the
              end of execution.

       --version (-V) [bool]
              Display the version of mlpack.

OPTIONAL OUTPUT OPTIONS

       --output_model_file (-M) [unknown]
              Output for trained GMM model.

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.