mlpack_hmm_train(1) - Linux man page

Name

hmm_train - hidden markov model (hmm) training

Synopsis

 hmm_train [-h] [-v] -i string -t string [-b] [-g int] [-l string] [-m string] [-o string] [-s int] [-n int]

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 --label_file, and if --batch is used, the file given to --label_file should contain a list of files of labels corresponding to the sequences in the file given to --input_file.

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 Options

--input_file (-i) [string]

File containing input observations.

--type (-t) [string]

Type of HMM: discrete | gaussian | gmm.

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 (optional). Default value ''.

--output_file (-o) [string]

File to save trained HMM to (XML). Default value 'output_hmm.xml'.

--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.

--verbose (-v)

Display informational messages and the full list of parameters and timers at the end of execution.

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.