mlpack_hmm_train - hidden markov model (hmm) training

       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-4.5.1                                     29 January 2025                             mlpack_hmm_train(1)

       This  program  allows  a Hidden Markov Model to be trained on labeled or unlabeled data. It supports four
       types of HMMs: Discrete HMMs, Gaussian HMMs, GMM HMMs, or Diagonal GMM HMMs

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

       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 (-T)'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 ’--output_model_file (-M)'.

mlpack_hmm_train - hidden markov model (hmm) training

--batch(-b)[bool]
              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)[bool]
              Default help info.

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

       --input_model_file(-m)[unknown]
              Pre-existing HMM model to initialize training with.

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

       --type(-t)[string]
              Type of HMM: discrete | gaussian | diag_gmm | gmm. Default value 'gaussian'.

       --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.

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

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

mlpack_hmm_train-istring [-bbool] [-gint] [-munknown] [-lstring] [-sint] [-nint] [-Tdouble] [-tstring] [-Vbool] [-Munknown] [-h-v]