org.encog.ml.hmm.alog
Class ForwardBackwardCalculator

java.lang.Object
  org.encog.ml.hmm.alog.ForwardBackwardCalculator

Direct Known Subclasses:

ForwardBackwardScaledCalculator

public class ForwardBackwardCalculator
extends Object

The forward-backward algorithm is an inference algorithm for hidden Markov models which computes the posterior marginals of all hidden state variables given a sequence of observations.

Nested Class Summary

static class

ForwardBackwardCalculator.Computation

Field Summary

protected double[][]	alpha Alpha matrix.
protected double[][]	beta Beta matrix.
protected double	probability Probability.

Constructor Summary

protected	ForwardBackwardCalculator() Construct an empty object.
	ForwardBackwardCalculator(MLDataSet oseq, HiddenMarkovModel hmm) Construct the forward/backward calculator.
	ForwardBackwardCalculator(MLDataSet oseq, HiddenMarkovModel hmm, EnumSet<ForwardBackwardCalculator.Computation> flags) Construct the object.

Method Summary

double	alphaElement(int t, int i) Alpha element.
double	betaElement(int t, int i) Beta element, best element.
protected void	computeAlpha(HiddenMarkovModel hmm, MLDataSet oseq) Compute alpha.
protected void	computeAlphaInit(HiddenMarkovModel hmm, MLDataPair o, int i) Compute the alpha init.
protected void	computeAlphaStep(HiddenMarkovModel hmm, MLDataPair o, int t, int j) Compute the alpha step.
protected void	computeBeta(HiddenMarkovModel hmm, MLDataSet oseq) Compute the beta step.
protected void	computeBetaStep(HiddenMarkovModel hmm, MLDataPair o, int t, int i) Compute the beta step.
double	probability()

Methods inherited from class java.lang.Object

clone, equals, finalize, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait

Field Detail

alpha

protected double[][] alpha

Alpha matrix.

beta

protected double[][] beta

Beta matrix.

probability

protected double probability

Probability.

Constructor Detail

ForwardBackwardCalculator

protected ForwardBackwardCalculator()

Construct an empty object.

ForwardBackwardCalculator

public ForwardBackwardCalculator(MLDataSet oseq,
                                 HiddenMarkovModel hmm)

Construct the forward/backward calculator.

Parameters:

oseq - The sequence to use.

hmm - THe hidden markov model to use.

ForwardBackwardCalculator

public ForwardBackwardCalculator(MLDataSet oseq,
                                 HiddenMarkovModel hmm,
                                 EnumSet<ForwardBackwardCalculator.Computation> flags)

Construct the object.

Parameters:

oseq - The sequence.

hmm - The hidden markov model to use.

flags - Flags, alpha or beta.

Method Detail

alphaElement

public double alphaElement(int t,
                           int i)

Alpha element.

Parameters:

t - The row.

i - The column.

Returns:

The element.

betaElement

public double betaElement(int t,
                          int i)

Beta element, best element.

Parameters:

t - From.

i - To.

Returns:

The element.

computeAlpha

protected void computeAlpha(HiddenMarkovModel hmm,
                            MLDataSet oseq)

Compute alpha.

Parameters:

hmm - The hidden markov model.

oseq - The sequence.

computeAlphaInit

protected void computeAlphaInit(HiddenMarkovModel hmm,
                                MLDataPair o,
                                int i)

Compute the alpha init.

Parameters:

hmm - THe hidden markov model.

o - The element.

i - The state.

computeAlphaStep

protected void computeAlphaStep(HiddenMarkovModel hmm,
                                MLDataPair o,
                                int t,
                                int j)

Compute the alpha step.

Parameters:

hmm - The hidden markov model.

o - The sequence element.

t - The alpha step.

j - Thr column.

computeBeta

protected void computeBeta(HiddenMarkovModel hmm,
                           MLDataSet oseq)

Compute the beta step.

Parameters:

hmm - The hidden markov model.

oseq - The sequence.

computeBetaStep

protected void computeBetaStep(HiddenMarkovModel hmm,
                               MLDataPair o,
                               int t,
                               int i)

Compute the beta step.

Parameters:

hmm - The hidden markov model.

o - THe data par to compute.

t - THe matrix row.

i - THe matrix column.

probability

public double probability()

Returns:

The probability.