Man page - mlpack_hmm_generate(1)

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mlpack_hmm_generate

NAME
SYNOPSIS
DESCRIPTION
REQUIRED INPUT OPTIONS
OPTIONAL INPUT OPTIONS
OPTIONAL OUTPUT OPTIONS
ADDITIONAL INFORMATION

NAME

mlpack_hmm_generate - hidden markov model (hmm) sequence generator

SYNOPSIS

mlpack_hmm_generate -l int -m unknown [ -s int ] [ -t int ] [ -V bool ] [ -o unknown ] [ -S unknown ] [ -h -v ]

DESCRIPTION

This utility takes an already-trained HMM, specified as the ’ --model_file ( -m )’ parameter, and generates a random observation sequence and hidden state sequence based on its parameters. The observation sequence may be saved with the ’ --output_file ( -o )’ output parameter, and the internal state sequence may be saved with the ’ --state_file ( -S )’ output parameter.

The state to start the sequence in may be specified with the ’ --start_state ( -t )’ parameter.

For example, to generate a sequence of length 150 from the HMM ’hmm.bin’ and save the observation sequence to ’observations.csv’ and the hidden state sequence to ’states.csv’, the following command may be used:

$ mlpack_hmm_generate --model_file hmm.bin --length 150 --output_file observations.csv --state_file states.csv

REQUIRED INPUT OPTIONS

--length (-l) [ int ]

Length of sequence to generate.

--model_file (-m) [ unknown ]

Trained HMM to generate sequences with.

OPTIONAL INPUT OPTIONS

--help (-h) [ bool ]

Default help info.

--info [string]

Print help on a specific option. Default value ’’.

--seed (-s) [ int ]

Random seed. If 0, ’std::time(NULL)’ is used. Default value 0.

--start_state (-t) [ int ]

Starting state of sequence. Default value 0.

--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.

OPTIONAL OUTPUT OPTIONS

--output_file ( -o ) [ unknown ] Matrix to save observation sequence to.
--state_file (-S) [
unknown ]

Matrix to save hidden state sequence to.

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.