1mlpack_hmm_train(1)              User Commands             mlpack_hmm_train(1)
2
3
4

NAME

6       mlpack_hmm_train - hidden markov model (hmm) training
7

SYNOPSIS

9        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]
10

DESCRIPTION

12       This  program  allows a Hidden Markov Model to be trained on labeled or
13       unlabeled data. It support three types of HMMs: discrete HMMs, Gaussian
14       HMMs, or GMM HMMs.
15
16       Either  one  input sequence can be specified (with --input_file), or, a
17       file containing files in which  input  sequences  can  be  found  (when
18       --input_file and --batch are used together). In addition, labels can be
19       provided in the file specified by  --labels_file,  and  if  --batch  is
20       used, the file given to --labels_file should contain a list of files of
21       labels  corresponding  to  the  sequences  in   the   file   given   to
22       --input_file.
23
24       The  HMM is trained with the Baum-Welch algorithm if no labels are pro‐
25       vided.  The tolerance of the Baum-Welch algorithm can be set  with  the
26       --tolerance  option. By default, the transition matrix is randomly ini‐
27       tialized and the emission distributions  are  initialized  to  fit  the
28       extent of the data.
29
30       Optionally,  a  pre-created  HMM  model  can be used as a guess for the
31       transition matrix and emission probabilities; this is specifiable  with
32       --model_file.
33

REQUIRED INPUT OPTIONS

35       --input_file (-i) [string]
36              File containing input observations.
37

OPTIONAL INPUT OPTIONS

39       --batch (-b) [bool]
40              If true, input_file (and if passed, labels_file) are expected to
41              contain a list of files to use as  input  observation  sequences
42              (and label sequences).
43
44       --gaussians (-g) [int]
45              Number  of gaussians in each GMM (necessary when type is 'gmm').
46              Default value 0.
47
48       --help (-h) [bool]
49              Default help info.
50
51       --info [string]
52              Get help on a specific module or option.  Default value ''.
53
54       --input_model_file (-m) [unknown]
55              Pre-existing HMM model  to  initialize  training  with.  Default
56              value ''.
57
58       --labels_file (-l) [string]
59              Optional  file  of  hidden  states,  used  for labeled training.
60              Default value ''.
61
62       --seed (-s) [int]
63              Random seed. If 0, 'std::time(NULL)' is used.  Default value 0.
64
65       --states (-n) [int]
66              Number of hidden states in HMM (necessary, unless model_file  is
67              specified). Default value 0.
68
69       --tolerance (-T) [double]
70              Tolerance of the Baum-Welch algorithm. Default value 1e-05.
71
72       --type (-t) [string]
73              Type  of  HMM:  discrete | gaussian | gmm. Default value 'gauss‐
74              ian'.
75
76       --verbose (-v) [bool]
77              Display informational messages and the full list  of  parameters
78              and timers at the end of execution.
79
80       --version (-V) [bool]
81              Display the version of mlpack.
82

OPTIONAL OUTPUT OPTIONS

84       --output_model_file (-M) [unknown]
85              Output for trained HMM. Default value ''.
86

ADDITIONAL INFORMATION

88       For further information, including relevant papers, citations, and the‐
89       ory,  consult  the  documentation  found  at  http://www.mlpack.org  or
90       included with your distribution of mlpack.
91
92
93
94mlpack-3.0.4                   21 February 2019            mlpack_hmm_train(1)