hmm_viterbi_main.cpp File Reference

#include <mlpack/prereqs.hpp>
#include <mlpack/core/util/io.hpp>
#include <mlpack/core/util/mlpack_main.hpp>
#include "hmm.hpp"
#include "hmm_model.hpp"
#include <mlpack/methods/gmm/gmm.hpp>
#include <mlpack/methods/gmm/diagonal_gmm.hpp>

Include dependency graph for hmm_viterbi_main.cpp:

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Classes
struct	Viterbi

Functions
	BINDING_NAME ("Hidden Markov Model (HMM) Viterbi State Prediction")
	BINDING_SHORT_DESC ("A utility for computing the most probable hidden state sequence for Hidden" " Markov Models (HMMs). Given a pre-trained HMM and an observed sequence, " "this uses the Viterbi algorithm to compute and return the most probable " "hidden state sequence.")
	BINDING_LONG_DESC ("This utility takes an already-trained HMM, specified as "+PRINT_PARAM_STRING("input_model")+", and evaluates the most probable " "hidden state sequence of a given sequence of observations (specified as " "'"+PRINT_PARAM_STRING("input")+", using the Viterbi algorithm. The " "computed state sequence may be saved using the "+PRINT_PARAM_STRING("output")+" output parameter.")
	BINDING_EXAMPLE ("For example, to predict the state sequence of the observations "+PRINT_DATASET("obs")+" using the HMM "+PRINT_MODEL("hmm")+", " "storing the predicted state sequence to "+PRINT_DATASET("states")+", the following command could be used:" "\"+PRINT_CALL("hmm_viterbi", "input", "obs", "input_model", "hmm", "output", "states"))
	BINDING_SEE_ALSO ("@hmm_train", "#hmm_train")
	BINDING_SEE_ALSO ("@hmm_generate", "#hmm_generate")
	BINDING_SEE_ALSO ("@hmm_loglik", "#hmm_loglik")
	BINDING_SEE_ALSO ("Hidden Mixture Models on Wikipedia", "https://en.wikipedia.org/wiki/Hidden_Markov_model")
	BINDING_SEE_ALSO ("mlpack::hmm::HMM class documentation", "@doxygen/classmlpack_1_1hmm_1_1HMM.html")
	PARAM_MATRIX_IN_REQ ("input", "Matrix containing observations,", "i")
	PARAM_MODEL_IN_REQ (HMMModel, "input_model", "Trained HMM to use.", "m")
	PARAM_UMATRIX_OUT ("output", "File to save predicted state sequence to.", "o")

Detailed Description

Author

Ryan Curtin

Compute the most probably hidden state sequence of a given observation sequence for a given HMM.

mlpack is free software; you may redistribute it and/or modify it under the terms of the 3-clause BSD license. You should have received a copy of the 3-clause BSD license along with mlpack. If not, see http://www.opensource.org/licenses/BSD-3-Clause for more information.