mlpack_hmm_train - hidden markov model (hmm) training
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]
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. 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 --model_file.
--input_file (-i) [string]
File containing input observations.
--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.
Get help on a specific module or option. Default value ’’.
--input_model_file (-m) [unknown]
Pre-existing HMM model to initialize training with. Default value ’’.
--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 | 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. Default value ’’.
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.