Class SubgraphProbabilityFunction
java.lang.Object
org.tweetyproject.math.probability.ProbabilityFunction<DungTheory>
org.tweetyproject.arg.prob.lotteries.SubgraphProbabilityFunction
- All Implemented Interfaces:
Map<DungTheory,
Probability>
Represents a probability function on the subgraphs of some Dung theory.
- Author:
- Matthias Thimm
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Nested Class Summary
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Constructor Summary
ConstructorDescriptionCreates a new uniform probability function for the given theory, i.e. -
Method Summary
Modifier and TypeMethodDescriptiongetAcceptanceProbability
(Division d, Semantics semantics) Returns the probability of the given division being acceptable wrt.getAcceptanceProbability
(LdoFormula f, Semantics semantics) Returns the probability of the given formula being acceptable wrt.getAcceptanceProbability
(Extension ext, Semantics semantics) Returns the probability of the given set of arguments being acceptable wrt.getAcceptanceProbability
(Argument arg, Semantics semantics) Returns the probability of the given argument being acceptable wrt.Returns the epistemic probability of the given argument, i.e.Returns the epistemic probability of the given attack, i.e.Returns the theory of this probability function.Updates this probability function with the given extension, i.e.roughUpdate
(DungTheory theory) Updates this probability function with the given theory using "rough redistribution", cf.simpleUpdate
(DungTheory theory) Updates this probability function with the given theory using "simple redistribution", cf.stickyUpdate
(DungTheory theory, double stickyCoefficient) Updates this probability function with the given theory using "sticky redistribution", cf.Methods inherited from class org.tweetyproject.math.probability.ProbabilityFunction
clear, containsKey, containsValue, convexCombination, convexCombination, entrySet, equals, get, getProbabilityVector, getProbabilityVectorAsDoubles, getUniformDistribution, hashCode, isEmpty, isNormalized, keySet, linearCombination, normalize, probability, probability, put, putAll, remove, sample, sample, size, toString, values
Methods inherited from interface java.util.Map
compute, computeIfAbsent, computeIfPresent, forEach, getOrDefault, merge, putIfAbsent, remove, replace, replace, replaceAll
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Constructor Details
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SubgraphProbabilityFunction
Creates a new uniform probability function for the given theory, i.e. all sub graphs of the given theory receive the same probability.- Parameters:
theory
- some theory.
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Method Details
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getTheory
Returns the theory of this probability function.- Returns:
- the theory of this probability function.
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getEpistemicProbability
Returns the epistemic probability of the given argument, i.e. the probability that the given argument is present in some randomly sampled sub graph.- Parameters:
arg
- some argument- Returns:
- a probability
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getEpistemicProbability
Returns the epistemic probability of the given attack, i.e. the probability that the given attack is present in some randomly sampled sub graph.- Parameters:
att
- some attack- Returns:
- a probability
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getAcceptanceProbability
Returns the probability of the given formula being acceptable wrt. the given semantics and this probability function, i.e. the sum of the probabilities of all sub-graphs that are dividers of the given formula.- Parameters:
f
- some formulasemantics
- some semantics.- Returns:
- a probability
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getAcceptanceProbability
Returns the probability of the given division being acceptable wrt. the given semantics and this probability function, i.e. the sum of the probabilities of all sub-graphs that are dividers of the given division.- Parameters:
d
- some divisionsemantics
- some semantics.- Returns:
- a probability
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getAcceptanceProbability
Returns the probability of the given argument being acceptable wrt. the given semantics and this probability functions. This is equivalent to the probability of the division ({arg},{}).- Parameters:
arg
- some argumentsemantics
- some semantics.- Returns:
- a probability
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getAcceptanceProbability
Returns the probability of the given set of arguments being acceptable wrt. the given semantics and this probability functions. This is equivalent to the probability of the division (ext,{}).- Parameters:
ext
- some set of argumentssemantics
- some semantics.- Returns:
- a probability
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naiveUpdate
Updates this probability function with the given extension, i.e. all theories that do not contain the given arguments get probability zero. Afterwards the function is normalized.- Parameters:
e
- some extension- Returns:
- the updated probability function
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simpleUpdate
Updates this probability function with the given theory using "simple redistribution", cf. [Hunter, Thimm, 2015].- Parameters:
theory
- some abstract theory- Returns:
- the updated probability function
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stickyUpdate
Updates this probability function with the given theory using "sticky redistribution", cf. [Hunter, Thimm, 2015].- Parameters:
theory
- some abstract theorystickyCoefficient
- the sticky coefficient- Returns:
- the updated probability function
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roughUpdate
Updates this probability function with the given theory using "rough redistribution", cf. [Hunter, Thimm, 2015].- Parameters:
theory
- some abstract theory- Returns:
- the updated probability function
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