public class SubgraphProbabilityFunction extends ProbabilityFunction<DungTheory>
Modifier and Type | Field and Description |
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private DungTheory |
theory
The theory of this probability function.
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Constructor and Description |
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SubgraphProbabilityFunction(DungTheory theory)
Creates a new uniform probability function for the given theory, i.e.
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Modifier and Type | Method and Description |
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Probability |
getAcceptanceProbability(Argument arg,
Semantics semantics)
Returns the probability of the given argument being acceptable wrt.
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Probability |
getAcceptanceProbability(Division d,
Semantics semantics)
Returns the probability of the given division being acceptable wrt.
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Probability |
getAcceptanceProbability(Extension ext,
Semantics semantics)
Returns the probability of the given set of arguments being acceptable wrt.
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Probability |
getAcceptanceProbability(LdoFormula f,
Semantics semantics)
Returns the probability of the given formula being acceptable wrt.
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Probability |
getEpistemicProbability(Argument arg)
Returns the epistemic probability of the given argument, i.e.
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Probability |
getEpistemicProbability(Attack att)
Returns the epistemic probability of the given attack, i.e.
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DungTheory |
getTheory()
Returns the theory of this probability function.
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SubgraphProbabilityFunction |
naiveUpdate(Extension e)
Updates this probability function with the given extension, i.e.
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SubgraphProbabilityFunction |
roughUpdate(DungTheory theory)
Updates this probability function with the given theory using
"rough redistribution", cf.
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SubgraphProbabilityFunction |
simpleUpdate(DungTheory theory)
Updates this probability function with the given theory using
"simple redistribution", cf.
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SubgraphProbabilityFunction |
stickyUpdate(DungTheory theory,
double stickyCoefficient)
Updates this probability function with the given theory using
"sticky redistribution", cf.
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private java.util.Set<Attack> |
superGraphs(DungTheory g,
DungTheory gp,
DungTheory c)
Computes Super(G,G′,Ci) = {(α,β) ∈ Arcs(G) | (α ∈ Nodes(G′) and β ∈ Nodes(Ci))
or (α ∈ Nodes(Ci) and β ∈ Nodes(G′))
or (α ∈ Nodes(Ci) and β ∈ Nodes(Ci))
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clear, containsKey, containsValue, convexCombination, convexCombination, entrySet, equals, get, getProbabilityVector, getProbabilityVectorAsDoubles, getUniformDistribution, hashCode, isEmpty, isNormalized, keySet, linearCombination, normalize, normalize, probability, probability, put, putAll, remove, sample, sample, size, toString, values
private DungTheory theory
public SubgraphProbabilityFunction(DungTheory theory)
theory
- some theory.public DungTheory getTheory()
public Probability getEpistemicProbability(Argument arg)
arg
- some argumentpublic Probability getEpistemicProbability(Attack att)
arg
- some argumentpublic Probability getAcceptanceProbability(LdoFormula f, Semantics semantics)
f
- some formulasemantics
- some semantics.public Probability getAcceptanceProbability(Division d, Semantics semantics)
d
- some divisionsemantics
- some semantics.public Probability getAcceptanceProbability(Argument arg, Semantics semantics)
arg
- some argumentsemantics
- some semantics.public Probability getAcceptanceProbability(Extension ext, Semantics semantics)
ext
- some set of argumentssemantics
- some semantics.public SubgraphProbabilityFunction naiveUpdate(Extension e)
e
- some extensionpublic SubgraphProbabilityFunction simpleUpdate(DungTheory theory)
theory
- some abstract theorypublic SubgraphProbabilityFunction stickyUpdate(DungTheory theory, double stickyCoefficient)
theory
- some abstract theorystickyCoefficient
- the sticky coefficientpublic SubgraphProbabilityFunction roughUpdate(DungTheory theory)
theory
- some abstract theoryprivate java.util.Set<Attack> superGraphs(DungTheory g, DungTheory gp, DungTheory c)