Class BoaEbnaModel
java.lang.Object
com.knezevic.edaf.v3.models.discrete.BoaEbnaModel
Sparse Bayesian-network model used by BOA/EBNA-style discrete EDAs.
Variables are ordered by entropy and each node chooses up to
maxParents predecessors with highest mutual information. The resulting
directed acyclic graph defines:
p(x) = Π_i p(x_i | Pa(x_i))with Laplace-smoothed conditional probability tables.
Compared to tree models, this representation captures higher-order local structure while keeping sampling exact and deterministic given RNG stream.
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Constructor Summary
Constructors -
Method Summary
Modifier and TypeMethodDescriptionExposes diagnostics (entropy, covariance condition number, etc.).voidfit(List<Individual<BitString>> selected, Representation<BitString> representation, RngStream rng) Fits model parameters from selected individuals.name()Model identifier used in configuration and listing commands.sample(int count, Representation<BitString> representation, Problem<BitString> problem, ConstraintHandling<BitString> constraintHandling, RngStream rng) Samples new genotypes from model.
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Constructor Details
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BoaEbnaModel
public BoaEbnaModel(int maxParents, double smoothing) - Parameters:
maxParents- upper bound on parent count per nodesmoothing- Laplace smoothing for CPT estimation
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Method Details
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name
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fit
public void fit(List<Individual<BitString>> selected, Representation<BitString> representation, RngStream rng) Description copied from interface:ModelFits model parameters from selected individuals. -
sample
public List<BitString> sample(int count, Representation<BitString> representation, Problem<BitString> problem, ConstraintHandling<BitString> constraintHandling, RngStream rng) Description copied from interface:ModelSamples new genotypes from model. -
diagnostics
Description copied from interface:ModelExposes diagnostics (entropy, covariance condition number, etc.).- Specified by:
diagnosticsin interfaceModel<BitString>
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