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java.lang.Objectbe.ac.ulg.montefiore.run.jahmm.learn.BaumWelchLearner
public class BaumWelchLearner
An implementation of the Baum-Welch learning algorithm. This algorithm finds a HMM that models a set of observation sequences.
Constructor Summary | |
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BaumWelchLearner()
Initializes a Baum-Welch instance. |
Method Summary | ||
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protected double[][] |
estimateGamma(double[][][] xi,
ForwardBackwardCalculator fbc)
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protected
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estimateXi(java.util.List<? extends O> sequence,
ForwardBackwardCalculator fbc,
Hmm<O> hmm)
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protected
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generateForwardBackwardCalculator(java.util.List<? extends O> sequence,
Hmm<O> hmm)
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int |
getNbIterations()
Returns the number of iterations performed by the learn(be.ac.ulg.montefiore.run.jahmm.Hmm method. |
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iterate(Hmm<O> hmm,
java.util.List<? extends java.util.List<? extends O>> sequences)
Performs one iteration of the Baum-Welch algorithm. |
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learn(Hmm<O> initialHmm,
java.util.List<? extends java.util.List<? extends O>> sequences)
Does a fixed number of iterations (see getNbIterations() ) of the
Baum-Welch algorithm. |
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void |
setNbIterations(int nb)
Sets the number of iterations performed by the learn(be.ac.ulg.montefiore.run.jahmm.Hmm method. |
Methods inherited from class java.lang.Object |
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clone, equals, finalize, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait |
Constructor Detail |
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public BaumWelchLearner()
Method Detail |
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public <O extends Observation> Hmm<O> iterate(Hmm<O> hmm, java.util.List<? extends java.util.List<? extends O>> sequences)
hmm
- A previously estimated HMM.sequences
- The observation sequences on which the learning is
based. Each sequence must have a length higher or equal to
2.
protected <O extends Observation> ForwardBackwardCalculator generateForwardBackwardCalculator(java.util.List<? extends O> sequence, Hmm<O> hmm)
public <O extends Observation> Hmm<O> learn(Hmm<O> initialHmm, java.util.List<? extends java.util.List<? extends O>> sequences)
getNbIterations()
) of the
Baum-Welch algorithm.
initialHmm
- An initial estimation of the expected HMM. This
estimate is critical as the Baum-Welch algorithm only find
local minima of its likelihood function.sequences
- The observation sequences on which the learning is
based. Each sequence must have a length higher or equal to 2.
protected <O extends Observation> double[][][] estimateXi(java.util.List<? extends O> sequence, ForwardBackwardCalculator fbc, Hmm<O> hmm)
protected double[][] estimateGamma(double[][][] xi, ForwardBackwardCalculator fbc)
public int getNbIterations()
learn(be.ac.ulg.montefiore.run.jahmm.Hmm, java.util.List extends java.util.List extends O>>)
method.
public void setNbIterations(int nb)
learn(be.ac.ulg.montefiore.run.jahmm.Hmm, java.util.List extends java.util.List extends O>>)
method.
nb
- The (positive) number of iterations to perform.
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