Examples of aima.core.probability.hmm.HiddenMarkovModel

aima.core.probability.hmm.HiddenMarkovModel
Artificial Intelligence A Modern Approach (3rd Edition): page 578.

The hidden Markov model, or HMM. An HMM is a temporal probabilistic model in which the state of the process is described by a single discrete random variable. The possible values of the variable are the possible states of the world. @author Ciaran O'Reilly @author Ravi Mohan


  public static void forwardBackWardDemo() {


    System.out.println("\nForward BackWard Demo\n");


    HiddenMarkovModel rainmanHmm = HMMFactory.createRainmanHMM();
    System.out
        .println("Creating a Hdden Markov Model to represent the model in Fig 15.5 ");
    List<String> perceptions = new ArrayList<String>();
    perceptions.add(HmmConstants.SEE_UMBRELLA);
    perceptions.add(HmmConstants.SEE_UMBRELLA);


    List<VarDistribution> results = rainmanHmm
        .forward_backward(perceptions);


    VarDistribution smoothedDayOne = results.get(1);
    System.out.println("Smoothed Probability Of Raining on Day One = "
        + smoothedDayOne.getProbabilityOf(HmmConstants.RAINING));

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  }


  public static void particleFilterinfDemo() {
    System.out.println("\nParticle Filtering Demo\n");
    HiddenMarkovModel rainman = HMMFactory.createRainmanHMM();
    Randomizer r = new JavaRandomizer();
    ParticleSet starting = rainman.prior().toParticleSet(rainman, r, 1000);
    System.out.println("at the beginning, "
        + starting.numberOfParticlesWithState(HmmConstants.RAINING)
        + " particles 0f 1000 indicate status == 'raining' ");
    System.out.println("at the beginning, "
        + starting.numberOfParticlesWithState(HmmConstants.NOT_RAINING)

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Related Classes of aima.core.probability.hmm.HiddenMarkovModel

aima.core.util.math.Matrix

aima.gui.demo.probability.ProbabilityDemo

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