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mlpack_hmm_train(1) User Commands mlpack_hmm_train(1)

NAME

mlpack_hmm_train - hidden markov model (hmm) training

SYNOPSIS


mlpack_hmm_train -i string [-b bool] [-g int] [-m unknown] [-l string] [-s int] [-n int] [-T double] [-t string] [-V bool] [-M unknown] [-h -v]

DESCRIPTION

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)'.

REQUIRED INPUT OPTIONS

File containing input observations.

OPTIONAL INPUT OPTIONS

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).
Number of gaussians in each GMM (necessary when type is 'gmm'). Default value 0.
Default help info.
Print help on a specific option. Default value ''.
Pre-existing HMM model to initialize training with.
Optional file of hidden states, used for labeled training. Default value ''.
Random seed. If 0, 'std::time(NULL)' is used. Default value 0.
Number of hidden states in HMM (necessary, unless model_file is specified). Default value 0.
Tolerance of the Baum-Welch algorithm. Default value 1e-05.
Type of HMM: discrete | gaussian | diag_gmm | gmm. Default value 'gaussian'.
Display informational messages and the full list of parameters and timers at the end of execution.
Display the version of mlpack.

OPTIONAL OUTPUT OPTIONS

Output for trained HMM.

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.

23 September 2024 mlpack-4.5.0