Class

com.cra.figaro.algorithm.structured.strategy.solve

VEGibbsStrategy

Related Doc: package solve

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class VEGibbsStrategy extends SolvingStrategy

A solving strategy that chooses between VE and Gibbs based on a score of the elminiation order

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  2. SolvingStrategy
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Instance Constructors

  1. new VEGibbsStrategy(scoreThreshold: Double, numSamples: Int, burnIn: Int, interval: Int, blockToSampler: BlockSamplerCreator)

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Value Members

  1. final def !=(arg0: Any): Boolean

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  2. final def ##(): Int

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  3. final def ==(arg0: Any): Boolean

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  4. final def asInstanceOf[T0]: T0

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  5. val blockToSampler: BlockSamplerCreator

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  6. val burnIn: Int

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  7. def clone(): AnyRef

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    protected[java.lang]
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  8. final def eq(arg0: AnyRef): Boolean

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  9. def equals(arg0: Any): Boolean

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  10. def finalize(): Unit

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    @throws( classOf[java.lang.Throwable] )

  11. final def getClass(): Class[_]

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  12. def hashCode(): Int

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  13. val interval: Int

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  14. final def isInstanceOf[T0]: Boolean

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  15. final def ne(arg0: AnyRef): Boolean

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  16. final def notify(): Unit

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  17. final def notifyAll(): Unit

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  18. val numSamples: Int

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  19. val scoreThreshold: Double

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  20. def solve(problem: Problem, toEliminate: Set[Variable[_]], toPreserve: Set[Variable[_]], factors: List[Factor[Double]]): (List[Factor[Double]], Map[Variable[_], Factor[_]])

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    Solve the given problem with the indicated preserve and eliminate variables, and return a list of factors representing the joint distribution over the preserved variables.

    Solve the given problem with the indicated preserve and eliminate variables, and return a list of factors representing the joint distribution over the preserved variables.

    Definition Classes
    VEGibbsStrategy → SolvingStrategy

  21. final def synchronized[T0](arg0: ⇒ T0): T0

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  22. def toString(): String

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  23. final def wait(): Unit

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  24. final def wait(arg0: Long, arg1: Int): Unit

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  25. final def wait(arg0: Long): Unit

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Inherited from SolvingStrategy

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