23 #ifndef __MLPACK_METHODS_HMM_HMM_HPP
24 #define __MLPACK_METHODS_HMM_HMM_HPP
92 template<
typename Distribution = distribution::DiscreteDistribution>
110 HMM(
const size_t states,
111 const Distribution emissions,
136 const std::vector<Distribution>&
emission,
167 void Train(
const std::vector<arma::mat>& dataSeq);
190 void Train(
const std::vector<arma::mat>& dataSeq,
191 const std::vector<arma::Col<size_t> >& stateSeq);
211 double Estimate(
const arma::mat& dataSeq,
212 arma::mat& stateProb,
213 arma::mat& forwardProb,
214 arma::mat& backwardProb,
215 arma::vec& scales)
const;
228 double Estimate(
const arma::mat& dataSeq,
229 arma::mat& stateProb)
const;
243 arma::mat& dataSequence,
244 arma::Col<size_t>& stateSequence,
245 const size_t startState = 0)
const;
257 double Predict(
const arma::mat& dataSeq,
258 arma::Col<size_t>& stateSeq)
const;
301 void Forward(
const arma::mat& dataSeq,
303 arma::mat& forwardProb)
const;
316 void Backward(
const arma::mat& dataSeq,
317 const arma::vec& scales,
318 arma::mat& backwardProb)
const;
337 #include "hmm_impl.hpp"
size_t Dimensionality() const
Get the dimensionality of observations.
std::vector< Distribution > emission
Set of emission probability distributions; one for each state.
size_t & Dimensionality()
Set the dimensionality of observations.
const arma::mat & Transition() const
Return the transition matrix.
std::vector< Distribution > & Emission()
Return a modifiable emission probability matrix reference.
void Forward(const arma::mat &dataSeq, arma::vec &scales, arma::mat &forwardProb) const
The Forward algorithm (part of the Forward-Backward algorithm).
double tolerance
Tolerance of Baum-Welch algorithm.
double & Tolerance()
Modify the tolerance of the Baum-Welch algorithm.
double LogLikelihood(const arma::mat &dataSeq) const
Compute the log-likelihood of the given data sequence.
double Tolerance() const
Get the tolerance of the Baum-Welch algorithm.
void Generate(const size_t length, arma::mat &dataSequence, arma::Col< size_t > &stateSequence, const size_t startState=0) const
Generate a random data sequence of the given length.
A class that represents a Hidden Markov Model with an arbitrary type of emission distribution.
const std::vector< Distribution > & Emission() const
Return the emission distributions.
double Estimate(const arma::mat &dataSeq, arma::mat &stateProb, arma::mat &forwardProb, arma::mat &backwardProb, arma::vec &scales) const
Estimate the probabilities of each hidden state at each time step for each given data observation...
void Backward(const arma::mat &dataSeq, const arma::vec &scales, arma::mat &backwardProb) const
The Backward algorithm (part of the Forward-Backward algorithm).
void Train(const std::vector< arma::mat > &dataSeq)
Train the model using the Baum-Welch algorithm, with only the given unlabeled observations.
size_t dimensionality
Dimensionality of observations.
arma::mat & Transition()
Return a modifiable transition matrix reference.
double Predict(const arma::mat &dataSeq, arma::Col< size_t > &stateSeq) const
Compute the most probable hidden state sequence for the given data sequence, using the Viterbi algori...
HMM(const size_t states, const Distribution emissions, const double tolerance=1e-5)
Create the Hidden Markov Model with the given number of hidden states and the given default distribut...
arma::mat transition
Transition probability matrix.