NAME

       mlpack_hmm_generate - hidden markov model (hmm) sequence generator

SYNOPSIS

        mlpack_hmm_generate -l int -m unknown [-s int] [-t int] [-V bool] [-o string] [-S string] [-h -v]

DESCRIPTION

       This  utility takes an already-trained HMM, specified as the '--model_file (-m)' parameter, and generates
       a random observation sequence and hidden state sequence based on its parameters. The observation sequence
       may be saved with the '--output_file (-o)' output parameter, and the internal state sequence may be saved
       with the '--state_file (-S)' output parameter.

       The state to start the sequence in may be specified with the '--start_state (-t)' parameter.

       For example, to generate a sequence of length 150  from  the  HMM  'hmm.bin'  and  save  the  observation
       sequence  to  'observations.csv' and the hidden state sequence to 'states.csv', the following command may
       be used:

       $ mlpack_hmm_generate --model_file  hmm.bin  --length  150  --output_file  observations.csv  --state_file
       states.csv

REQUIRED INPUT OPTIONS

       --length (-l) [int]
              Length of sequence to generate.

       --model_file (-m) [unknown]
              Trained HMM to generate sequences with.

OPTIONAL INPUT OPTIONS

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

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

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

       --start_state (-t) [int]
              Starting state of sequence. Default value 0.

       --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_file (-o) [string]
              Matrix to save observation sequence to.

       --state_file (-S) [string]
              Matrix to save hidden state sequence to.

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-3.2.2                                    21 February 2020                          mlpack_hmm_generate(1)