.\" Text automatically generated by txt2man
.TH mlpack_hmm_viterbi 1 "18 November 2018" "mlpack-3.0.4" "User Commands"
.SH NAME
\fBmlpack_hmm_viterbi \fP- hidden markov model (hmm) viterbi state prediction
.SH SYNOPSIS
.nf
.fam C
 \fBmlpack_hmm_viterbi\fP \fB-i\fP \fIstring\fP \fB-m\fP \fIunknown\fP [\fB-V\fP \fIbool\fP] [\fB-o\fP \fIstring\fP] [\fB-h\fP \fB-v\fP] 
.fam T
.fi
.fam T
.fi
.SH DESCRIPTION


This utility takes an already-trained HMM, specified as '\fB--input_model_file\fP
(\fB-m\fP)', and evaluates the most probable hidden state sequence of a given
sequence of observations (specified as ''\fB--input_file\fP (\fB-i\fP)', using the Viterbi
algorithm. The computed state sequence may be saved using the '\fB--output_file\fP
(\fB-o\fP)' output parameter.
.PP
For example, to predict the state sequence of the observations 'obs.csv' using
the HMM 'hmm.bin', storing the predicted state sequence to 'states.csv', the
following command could be used:
.PP
$ hmm_viterbi \fB--input_file\fP obs.csv \fB--input_model_file\fP hmm.bin \fB--output_file\fP
states.csv
.RE
.PP

.SH REQUIRED INPUT OPTIONS 

.TP
.B
\fB--input_file\fP (\fB-i\fP) [\fIstring\fP]
Matrix containing observations, 
.TP
.B
\fB--input_model_file\fP (\fB-m\fP) [\fIunknown\fP]
Trained HMM to use.  
.SH OPTIONAL INPUT OPTIONS 

.TP
.B
\fB--help\fP (\fB-h\fP) [\fIbool\fP]
Default help info. 
.TP
.B
\fB--info\fP [\fIstring\fP]
Get help on a specific module or option.  Default value ''. 
.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_file\fP (\fB-o\fP) [\fIstring\fP]
File to save predicted state sequence to.  Default value ''.
.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.