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hmm.hpp
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1 
14 #ifndef MLPACK_METHODS_HMM_HMM_HPP
15 #define MLPACK_METHODS_HMM_HMM_HPP
16 
17 #include <mlpack/prereqs.hpp>
19 
20 namespace mlpack {
21 namespace hmm {
22 
84 template<typename Distribution = distribution::DiscreteDistribution>
85 class HMM
86 {
87  public:
105  HMM(const size_t states = 0,
106  const Distribution emissions = Distribution(),
107  const double tolerance = 1e-5);
108 
136  HMM(const arma::vec& initial,
137  const arma::mat& transition,
138  const std::vector<Distribution>& emission,
139  const double tolerance = 1e-5);
140 
169  void Train(const std::vector<arma::mat>& dataSeq);
170 
192  void Train(const std::vector<arma::mat>& dataSeq,
193  const std::vector<arma::Row<size_t> >& stateSeq);
194 
213  double Estimate(const arma::mat& dataSeq,
214  arma::mat& stateProb,
215  arma::mat& forwardProb,
216  arma::mat& backwardProb,
217  arma::vec& scales) const;
218 
230  double Estimate(const arma::mat& dataSeq,
231  arma::mat& stateProb) const;
232 
244  void Generate(const size_t length,
245  arma::mat& dataSequence,
246  arma::Row<size_t>& stateSequence,
247  const size_t startState = 0) const;
248 
259  double Predict(const arma::mat& dataSeq,
260  arma::Row<size_t>& stateSeq) const;
261 
268  double LogLikelihood(const arma::mat& dataSeq) const;
269 
282  void Filter(const arma::mat& dataSeq,
283  arma::mat& filterSeq,
284  size_t ahead = 0) const;
285 
297  void Smooth(const arma::mat& dataSeq,
298  arma::mat& smoothSeq) const;
299 
301  const arma::vec& Initial() const { return initial; }
303  arma::vec& Initial() { return initial; }
304 
306  const arma::mat& Transition() const { return transition; }
308  arma::mat& Transition() { return transition; }
309 
311  const std::vector<Distribution>& Emission() const { return emission; }
313  std::vector<Distribution>& Emission() { return emission; }
314 
316  size_t Dimensionality() const { return dimensionality; }
318  size_t& Dimensionality() { return dimensionality; }
319 
321  double Tolerance() const { return tolerance; }
323  double& Tolerance() { return tolerance; }
324 
328  template<typename Archive>
329  void Serialize(Archive& ar, const unsigned int version);
330 
331  protected:
332  // Helper functions.
343  void Forward(const arma::mat& dataSeq,
344  arma::vec& scales,
345  arma::mat& forwardProb) const;
346 
358  void Backward(const arma::mat& dataSeq,
359  const arma::vec& scales,
360  arma::mat& backwardProb) const;
361 
363  std::vector<Distribution> emission;
364 
366  arma::mat transition;
367 
368  private:
370  arma::vec initial;
371 
374 
376  double tolerance;
377 };
378 
379 } // namespace hmm
380 } // namespace mlpack
381 
382 // Include implementation.
383 #include "hmm_impl.hpp"
384 
385 #endif
arma::vec & Initial()
Modify the vector of initial state probabilities.
Definition: hmm.hpp:303
size_t Dimensionality() const
Get the dimensionality of observations.
Definition: hmm.hpp:316
std::vector< Distribution > emission
Set of emission probability distributions; one for each state.
Definition: hmm.hpp:363
void Smooth(const arma::mat &dataSeq, arma::mat &smoothSeq) const
HMM smoothing.
Linear algebra utility functions, generally performed on matrices or vectors.
Definition: binarize.hpp:18
const arma::vec & Initial() const
Return the vector of initial state probabilities.
Definition: hmm.hpp:301
size_t & Dimensionality()
Set the dimensionality of observations.
Definition: hmm.hpp:318
const arma::mat & Transition() const
Return the transition matrix.
Definition: hmm.hpp:306
std::vector< Distribution > & Emission()
Return a modifiable emission probability matrix reference.
Definition: hmm.hpp:313
The core includes that mlpack expects; standard C++ includes and Armadillo.
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.
Definition: hmm.hpp:376
arma::vec initial
Initial state probability vector.
Definition: hmm.hpp:370
double & Tolerance()
Modify the tolerance of the Baum-Welch algorithm.
Definition: hmm.hpp:323
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.
Definition: hmm.hpp:321
A class that represents a Hidden Markov Model with an arbitrary type of emission distribution.
Definition: hmm.hpp:85
const std::vector< Distribution > & Emission() const
Return the emission distributions.
Definition: hmm.hpp:311
void Generate(const size_t length, arma::mat &dataSequence, arma::Row< size_t > &stateSequence, const size_t startState=0) const
Generate a random data sequence of the given length.
double Predict(const arma::mat &dataSeq, arma::Row< size_t > &stateSeq) const
Compute the most probable hidden state sequence for the given data sequence, using the Viterbi algori...
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.
Definition: hmm.hpp:373
arma::mat & Transition()
Return a modifiable transition matrix reference.
Definition: hmm.hpp:308
void Serialize(Archive &ar, const unsigned int version)
Serialize the object.
void Filter(const arma::mat &dataSeq, arma::mat &filterSeq, size_t ahead=0) const
HMM filtering.
HMM(const size_t states=0, const Distribution emissions=Distribution(), 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.
Definition: hmm.hpp:366