.\" Text automatically generated by txt2man
.TH mlpack_hmm_train 1 "12 December 2020" "mlpack-3.4.2" "User Commands"
.SH NAME
\fBmlpack_hmm_train \fP- hidden markov model (hmm) training
.SH SYNOPSIS
.nf
.fam C
 \fBmlpack_hmm_train\fP \fB-i\fP \fIstring\fP [\fB-b\fP \fIbool\fP] [\fB-g\fP \fIint\fP] [\fB-m\fP \fIunknown\fP] [\fB-l\fP \fIstring\fP] [\fB-s\fP \fIint\fP] [\fB-n\fP \fIint\fP] [\fB-T\fP \fIdouble\fP] [\fB-t\fP \fIstring\fP] [\fB-V\fP \fIbool\fP] [\fB-M\fP \fIunknown\fP] [\fB-h\fP \fB-v\fP] 
.fam T
.fi
.fam T
.fi
.SH 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
.PP
Either one input sequence can be specified (with '\fB--input_file\fP (\fB-i\fP)'), or, a
file containing files in which input sequences can be found (when
\(cq\fB--input_file\fP (\fB-i\fP)'and'\fB--batch\fP (\fB-b\fP)' are used together). In addition, labels
can be provided in the file specified by '\fB--labels_file\fP (\fB-l\fP)', and if '\fB--batch\fP
(\fB-b\fP)' is used, the file given to '\fB--labels_file\fP (\fB-l\fP)' should contain a list of
files of labels corresponding to the sequences in the file given to
\(cq\fB--input_file\fP (\fB-i\fP)'.
.PP
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 '\fB--tolerance\fP
(\fB-T\fP)'option. By default, the transition matrix is randomly initialized and
the emission distributions are initialized to fit the extent of the data.
.PP
Optionally, a pre-created HMM model can be used as a guess for the transition
matrix and emission probabilities; this is specifiable with
\(cq\fB--output_model_file\fP (\fB-M\fP)'.
.RE
.PP

.SH REQUIRED INPUT OPTIONS 

.TP
.B
\fB--input_file\fP (\fB-i\fP) [\fIstring\fP]
File containing input observations.  
.SH OPTIONAL INPUT OPTIONS 

.TP
.B
\fB--batch\fP (\fB-b\fP) [\fIbool\fP]
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). 
.TP
.B
\fB--gaussians\fP (\fB-g\fP) [\fIint\fP]
Number of gaussians in each GMM (necessary when type is 'gmm'). Default value 0. 
.TP
.B
\fB--help\fP (\fB-h\fP) [\fIbool\fP]
Default help info. 
.TP
.B
\fB--info\fP [\fIstring\fP]
Print help on a specific option. Default value ''. 
.TP
.B
\fB--input_model_file\fP (\fB-m\fP) [\fIunknown\fP]
Pre-existing HMM model to initialize training with. 
.TP
.B
\fB--labels_file\fP (\fB-l\fP) [\fIstring\fP]
Optional file of hidden states, used for labeled training. Default value ''. 
.TP
.B
\fB--seed\fP (\fB-s\fP) [\fIint\fP]
Random seed. If 0, 'std::time(NULL)' is used.  Default value 0. 
.TP
.B
\fB--states\fP (\fB-n\fP) [\fIint\fP]
Number of hidden states in HMM (necessary, unless model_file is specified). Default value 0. 
.TP
.B
\fB--tolerance\fP (\fB-T\fP) [\fIdouble\fP]
Tolerance of the Baum-Welch algorithm. Default value 1e-05. 
.TP
.B
\fB--type\fP (\fB-t\fP) [\fIstring\fP]
Type of HMM: discrete | gaussian | diag_gmm | gmm. Default value 'gaussian'. 
.TP
.B
\fB--verbose\fP (\fB-v\fP) [\fIbool\fP]
Display informational messages and the full list of parameters and timers at the end of execution. 
.TP
.B
\fB--version\fP (\fB-V\fP) [\fIbool\fP]
Display the version of mlpack.  
.SH OPTIONAL OUTPUT OPTIONS 

.TP
.B
\fB--output_model_file\fP (\fB-M\fP) [\fIunknown\fP]
Output for trained HMM.
.SH 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.