NAME

       mlpack_hmm_train - hidden markov model (hmm) training

SYNOPSIS

        mlpack_hmm_train [-h] [-v]

DESCRIPTION

       This  program  allows a Hidden Markov Model to be trained on labeled or unlabeled data. It
       support three types of HMMs: discrete HMMs, Gaussian HMMs, or GMM HMMs.

       Either one input sequence can be specified (with  --input_file),  or,  a  file  containing
       files  in  which  input  sequences  can  be  found (when --input_file and --batch are used
       together). In addition, labels can be provided in the file specified by --labels_file, and
       if  --batch  is  used,  the  file given to --labels_file should contain a list of files of
       labels corresponding to the sequences in the file given to --input_file.

       The HMM is trained with the Baum-Welch algorithm if no labels are provided.  The tolerance
       of  the  Baum-Welch  algorithm  can  be  set  with the --tolerance option. By default, the
       transition matrix is randomly initialized and the emission distributions  are  initialized
       to fit the extent of the data.

       Optionally,  a  pre-created HMM model can be used as a guess for the transition matrix and
       emission probabilities; this is specifiable with --model_file.

REQUIRED INPUT OPTIONS

       --input_file (-i) [string]
              File containing input observations.

       --type (-t) [string]
              Type of HMM: discrete | gaussian | gmm.

OPTIONAL INPUT OPTIONS

       --batch (-b)
              If true, input_file (and if passed, labels_file) are expected to contain a list  of
              files to use as input observation sequences (and label sequences).

       --gaussians (-g) [int]
              Number of gaussians in each GMM (necessary when type is 'gmm'). Default value 0.

       --help (-h)
              Default help info.

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

       --labels_file (-l) [string]
              Optional file of hidden states, used for labeled training. Default value ''.

       --model_file (-m) [string]
              Pre-existing HMM model file. Default value ''.

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

       --states (-n) [int]
              Number of hidden states in HMM (necessary, unless model_file is specified). Default
              value

              0.

       --tolerance (-T) [double]
              Tolerance of the Baum-Welch algorithm. Default value 1e-05.

       --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.

OPTIONAL OUTPUT OPTIONS

       --output_model_file (-o) [string] File to save trained HMM to. Default value ''.

ADDITIONAL INFORMATION

ADDITIONAL INFORMATION

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

                                                               mlpack_hmm_train(16 November 2017)