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c

com.cra.figaro.algorithm.lazyfactored

LazyVariableElimination

class LazyVariableElimination extends FactoredAlgorithm[Double] with LazyAlgorithm

Algorithm that lazily performs variable elimination. This algorithm is a lazy algorithm that can be run to any depth. Given a depth, it expands the model up to that depth and creates factors for the expanded elements. It also creates factors that capture the effect of parts of the model that have not been expanded on the query targets. These factors are used to compute lower or upper bounds on the queries. Then it uses ordinary variable elimination to solve these factors.

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  1. LazyVariableElimination
  2. LazyAlgorithm
  3. FactoredAlgorithm
  4. Algorithm
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Instance Constructors

  1. new LazyVariableElimination(targetElements: Element[_]*)(implicit universe: Universe)

Value Members

  1. final def !=(arg0: Any): Boolean
    Definition Classes
    AnyRef → Any
  2. final def ##(): Int
    Definition Classes
    AnyRef → Any
  3. final def ==(arg0: Any): Boolean
    Definition Classes
    AnyRef → Any
  4. val active: Boolean
    Attributes
    protected
    Definition Classes
    Algorithm
  5. final def asInstanceOf[T0]: T0
    Definition Classes
    Any
  6. 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
  7. def clone(): AnyRef
    Attributes
    protected[java.lang]
    Definition Classes
    AnyRef
    Annotations
    @throws( ... )
  8. 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.

  9. var currentResult: Factor[(Double, Double)]
  10. var debug: Boolean
  11. val dependentAlgorithm: Null
  12. val dependentUniverses: List[Nothing]
  13. val depth: Int

    The current depth to which the algorithm should be run.

    The current depth to which the algorithm should be run.

    Definition Classes
    LazyAlgorithm
  14. def doElimination(allFactors: List[Factor[Double]], targetVariables: Seq[Variable[_]]): Set[Factor[Double]]
    Attributes
    protected
  15. def doKill(): Unit

    Kill the algorithm.

    Kill the algorithm.

    Definition Classes
    LazyAlgorithmAlgorithm
  16. def doResume(): Unit

    Resume the algorithm by increasing the depth and running again.

    Resume the algorithm by increasing the depth and running again.

    Definition Classes
    LazyAlgorithmAlgorithm
  17. def doStart(): Unit

    Start the algorithm.

    Start the algorithm. This will run the algorithm to one depth.

    Definition Classes
    LazyAlgorithmAlgorithm
  18. def doStop(): Unit

    Stop the algorithm.

    Stop the algorithm.

    Definition Classes
    LazyAlgorithmAlgorithm
  19. 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.

  20. final def eq(arg0: AnyRef): Boolean
    Definition Classes
    AnyRef
  21. def equals(arg0: Any): Boolean
    Definition Classes
    AnyRef → Any
  22. def finalize(): Unit
    Attributes
    protected[java.lang]
    Definition Classes
    AnyRef
    Annotations
    @throws( classOf[java.lang.Throwable] )
  23. def finishNoBounds(factorsAfterElimination: Set[Factor[Double]]): Factor[(Double, Double)]

    Postprocess the factors produced by eliminating variables, assuming the entire model has been expanded so the lower and upper bounds are the same.

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

    Postprocess the factors produced by eliminating variables, when the lower and upper bounds may be different.

    Postprocess the factors produced by eliminating variables, when the lower and upper bounds may be different.

    lowerFactors

    the factors produced with the upperBounds flag = false

    upperFactors

    the factors produced with the upperBounds flag = true

  25. final def getClass(): Class[_]
    Definition Classes
    AnyRef → Any
  26. def getFactors(neededElements: List[Element[_]], targetElements: List[Element[_]], upperBounds: Boolean = false): List[Factor[Double]]

    Create the necessary factors.

    Create the necessary factors.

    neededElements

    elements that have been expanded that need factors created

    targetElements

    query targets

    upperBounds

    flag indicating whether lower (false) or upper (true) bounds should be computed for unexpanded parts of the model

    Definition Classes
    LazyVariableEliminationFactoredAlgorithm
  27. def getNeededElements(starterElements: List[Element[_]], depth: Int, parameterized: Boolean = false): (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
  28. def hashCode(): Int
    Definition Classes
    AnyRef → Any
  29. 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
  30. def isActive: Boolean
    Definition Classes
    Algorithm
  31. final def isInstanceOf[T0]: Boolean
    Definition Classes
    Any
  32. 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
  33. final def ne(arg0: AnyRef): Boolean
    Definition Classes
    AnyRef
  34. final def notify(): Unit
    Definition Classes
    AnyRef
  35. final def notifyAll(): Unit
    Definition Classes
    AnyRef
  36. def probabilityBounds[T](target: Element[_], value: T): (Double, Double)

    Returns the lower and upper bounds of the probability of the target.

  37. def pump(): Unit

    Increase the depth and run the algorithm again.

    Increase the depth and run the algorithm again.

    Definition Classes
    LazyAlgorithm
  38. var recordingFactors: List[Factor[_]]
    Attributes
    protected
  39. 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
  40. def run(depth: Int): Unit

    Run the algorithm to the given depth.

    Run the algorithm to the given depth.

    Definition Classes
    LazyVariableEliminationLazyAlgorithm
  41. val semiring: SumProductSemiring
  42. var showTiming: Boolean
  43. 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
  44. 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
  45. final def synchronized[T0](arg0: ⇒ T0): T0
    Definition Classes
    AnyRef
  46. var targetFactors: Map[Element[_], Factor[(Double, Double)]]
  47. def toString(): String
    Definition Classes
    AnyRef → Any
  48. implicit val universe: Universe
  49. final def wait(): Unit
    Definition Classes
    AnyRef
    Annotations
    @throws( ... )
  50. final def wait(arg0: Long, arg1: Int): Unit
    Definition Classes
    AnyRef
    Annotations
    @throws( ... )
  51. final def wait(arg0: Long): Unit
    Definition Classes
    AnyRef
    Annotations
    @throws( ... )

Inherited from LazyAlgorithm

Inherited from FactoredAlgorithm[Double]

Inherited from Algorithm

Inherited from AnyRef

Inherited from Any

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