LazyVariableElimination

Value Members

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

   Definition Classes

   AnyRef

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

   Definition Classes

   Any

3. final def ##(): Int

   Definition Classes

   AnyRef → Any

4. final def ==(arg0: AnyRef): Boolean

   Definition Classes

   AnyRef

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

   Definition Classes

   Any

6. var active: Boolean

   Attributes

   protected

   Definition Classes

   Algorithm

7. final def asInstanceOf[T0]: T0

   Definition Classes

   Any

8. def cleanUp(): Unit

   Called when the algorithm is killed.

   Called when the algorithm is killed. By default, does nothing. Can be overridden.

   Definition Classes

   Algorithm

9. def clone(): AnyRef

   Attributes

   protected[java.lang]

   Definition Classes

   AnyRef

   Annotations

   @throws( ... )

10. val comparator: Option[(Double, Double) ⇒ Boolean]

    Some variable elimination algorithms, such as computing the most probable explanation, record values of variables as they are eliminated.

    Some variable elimination algorithms, such as computing the most probable explanation, record values of variables as they are eliminated. Such values are stored in a factor that maps values of the other variables to a value of the eliminated variable. This factor is produced by finding the value of the variable that "maximizes" the entry associated with the value in the product factor resulting from eliminating this variable, for some maximization function. The recordingFunction determines which of two entries is greater according to the maximization function. It returns true iff the second entry is greater. The recording function is an option so that variable elimination algorithms that do not use it can ignore it.

11. var currentResult: Factor[(Double, Double)]

12. var debug: Boolean

13. val dependentAlgorithm: Null

    The algorithm to compute probability of specified evidence in a dependent universe.

    The algorithm to compute probability of specified evidence in a dependent universe. We use () => Double to represent this algorithm instead of an instance of ProbEvidenceAlgorithm. Typical usage is to return the result of ProbEvidenceAlgorithm.computeProbEvidence when invoked.

    Definition Classes

    LazyVariableElimination → FactoredAlgorithm

14. val dependentUniverses: List[Nothing]

    A list of universes that depend on this universe such that evidence on those universes should be taken into account in this universe.

    A list of universes that depend on this universe such that evidence on those universes should be taken into account in this universe.

    Definition Classes

    LazyVariableElimination → FactoredAlgorithm

15. var depth: Int

    Definition Classes

    LazyAlgorithm

16. def doElimination(allFactors: List[Factor[Double]], targetVariables: Seq[Variable[_]]): Set[Factor[Double]]

    Attributes

    protected

17. def doKill(): Unit

    Definition Classes

    LazyAlgorithm → Algorithm

18. def doResume(): Unit

    Definition Classes

    LazyAlgorithm → Algorithm

19. def doStart(): Unit

    Definition Classes

    LazyAlgorithm → Algorithm

20. def doStop(): Unit

    Definition Classes

    LazyAlgorithm → Algorithm

21. def eliminationOrder(allVars: Set[Variable[_]], factors: Traversable[Factor[Double]], toPreserve: Traversable[Variable[_]]): List[Variable[_]]

    Method for choosing the elimination order.

    Method for choosing the elimination order. The default order chooses first the variable that minimizes the number of extra factor entries that would be created when it is eliminated. Override this method if you want a different rule.

22. final def eq(arg0: AnyRef): Boolean

    Definition Classes

    AnyRef

23. def equals(arg0: Any): Boolean

    Definition Classes

    AnyRef → Any

24. def finalize(): Unit

    Attributes

    protected[java.lang]

    Definition Classes

    AnyRef

    Annotations

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

25. def finishNoBounds(factorsAfterElimination: Set[Factor[Double]]): Factor[(Double, Double)]

26. def finishWithBounds(lowerFactors: Set[Factor[Double]], upperFactors: Set[Factor[Double]]): Factor[(Double, Double)]

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

    Definition Classes

    AnyRef → Any

28. def getFactors(neededElements: List[Element[_]], targetElements: List[Element[_]], upperBounds: Boolean = false): List[Factor[Double]]

    All implementations of factored algorithms must specify a way to get the factors from the given universe and dependent universes.

    All implementations of factored algorithms must specify a way to get the factors from the given universe and dependent universes.

    Definition Classes

    LazyVariableElimination → FactoredAlgorithm

29. def getNeededElements(starterElements: List[Element[_]], depth: Int): (List[Element[_]], Boolean)

    Get the elements that are needed by the query target variables and the evidence variables.

    Get the elements that are needed by the query target variables and the evidence variables. Also compute the values of those variables to the given depth. Only get factors for elements that are actually used by the target variables. This is more efficient. Also, it avoids problems when values of unused elements have not been computed.

    In addition to getting all the needed elements, it determines if any of the conditioned, constrained, or dependent universe parent elements has * in its range. If any of these elements has * in its range, the lower and upper bounds of factors will be different, so we need to compute both. If they don't, we don't need to compute bounds.

    Definition Classes

    FactoredAlgorithm

30. def hashCode(): Int

    Definition Classes

    AnyRef → Any

31. def initialize(): Unit

    Called when the algorithm is started before running any steps.

    Called when the algorithm is started before running any steps. By default, does nothing. Can be overridden.

    Definition Classes

    Algorithm

32. def isActive: Boolean

    Definition Classes

    Algorithm

33. final def isInstanceOf[T0]: Boolean

    Definition Classes

    Any

34. def kill(): Unit

    Kill the algorithm so that it is inactive.

    Kill the algorithm so that it is inactive. It will no longer be able to provide answers.Throws AlgorithmInactiveException if the algorithm is not active.

    Definition Classes

    Algorithm

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

    Definition Classes

    AnyRef

36. final def notify(): Unit

    Definition Classes

    AnyRef

37. final def notifyAll(): Unit

    Definition Classes

    AnyRef

38. def probabilityBounds[T](target: Element[_], value: T): (Double, Double)

39. def pump(): Unit

    Definition Classes

    LazyAlgorithm

40. var recordingFactors: List[Factor[_]]

    Attributes

    protected

41. def resume(): Unit

    Resume the computation of the algorithm, if it has been stopped.

    Resume the computation of the algorithm, if it has been stopped. Throws AlgorithmInactiveException if the algorithm is not active.

    Definition Classes

    Algorithm

42. def run(depth: Int): Unit

    Definition Classes

    LazyVariableElimination → LazyAlgorithm

43. val semiring: SumProductSemiring.type

    The sum, product operations on the factor types and appropriate values for zero and one must be defined.

    The sum, product operations on the factor types and appropriate values for zero and one must be defined.

    Definition Classes

    LazyVariableElimination → FactoredAlgorithm

44. var showTiming: Boolean

45. def start(): Unit

    Start the algorithm and make it active.

    Start the algorithm and make it active. After it returns, the algorithm must be ready to provide answers. Throws AlgorithmActiveException if the algorithm is already active.

    Definition Classes

    Algorithm

46. def stop(): Unit

    Stop the algorithm from computing.

    Stop the algorithm from computing. The algorithm is still ready to provide answers after it returns. Throws AlgorithmInactiveException if the algorithm is not active.

    Definition Classes

    Algorithm

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

    Definition Classes

    AnyRef

48. var targetFactors: Map[Element[_], Factor[(Double, Double)]]

49. def toString(): String

    Definition Classes

    AnyRef → Any

50. implicit val universe: Universe

    The universe on which this variable elimination algorithm should be applied.

    The universe on which this variable elimination algorithm should be applied.

    Definition Classes

    LazyVariableElimination → FactoredAlgorithm

51. final def wait(): Unit

    Definition Classes

    AnyRef

    Annotations

    @throws( ... )

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

    Definition Classes

    AnyRef

    Annotations

    @throws( ... )

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

    Definition Classes

    AnyRef

    Annotations

    @throws( ... )