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mlpack_hmm_train - hidden markov model (hmm) training


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


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.

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.


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

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


--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_model_file (-o) [string]
File to save trained HMM to. 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.

--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.


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.


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