com.cra.figaro.algorithm.factored

SufficientStatisticsVariableElimination

class SufficientStatisticsVariableElimination extends VariableElimination[(Double, Map[Parameter[_], Seq[Double]])]

Variable elimination for sufficient statistics factors. The final factor resulting from variable elimination contains a mapping of parameters to sufficient statistics vectors which can be used to maximize parameter values.

Linear Supertypes
VariableElimination[(Double, Map[Parameter[_], Seq[Double]])], OneTime, FactoredAlgorithm[(Double, Map[Parameter[_], Seq[Double]])], Algorithm, AnyRef, Any
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  1. SufficientStatisticsVariableElimination
  2. VariableElimination
  3. OneTime
  4. FactoredAlgorithm
  5. Algorithm
  6. AnyRef
  7. Any
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Instance Constructors

  1. new SufficientStatisticsVariableElimination(parameterMap: Map[Parameter[_], Seq[Double]], universe: Universe)

    parameterMap

    A map of parameters to their sufficient statistics.

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
    SufficientStatisticsVariableEliminationAlgorithm
  9. def clone(): AnyRef

    Attributes
    protected[java.lang]
    Definition Classes
    AnyRef
    Annotations
    @throws( ... )
  10. val comparator: Option[((Double, Map[Parameter[_], Seq[Double]]), (Double, Map[Parameter[_], Seq[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.

    Definition Classes
    VariableElimination
  11. var debug: Boolean

    By default, implementations that inherit this trait have no debug information.

    By default, implementations that inherit this trait have no debug information. Override this if you want a debugging option.

    Definition Classes
    VariableElimination
  12. val dependentAlgorithm: (Universe, List[NamedEvidence[_]]) ⇒ () ⇒ Double

    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
    SufficientStatisticsVariableEliminationFactoredAlgorithm
  13. val dependentUniverses: List[(Universe, List[NamedEvidence[_]])]

    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
    SufficientStatisticsVariableEliminationFactoredAlgorithm
  14. def doElimination(allFactors: List[Factor[(Double, Map[Parameter[_], Seq[Double]])]], targetVariables: Seq[Variable[_]]): Unit

    Attributes
    protected
    Definition Classes
    VariableElimination
  15. def doKill(): Unit

    Attributes
    protected
    Definition Classes
    OneTimeAlgorithm
  16. def doResume(): Unit

    Attributes
    protected
    Definition Classes
    OneTimeAlgorithm
  17. def doStart(): Unit

    Attributes
    protected
    Definition Classes
    OneTimeAlgorithm
  18. def doStop(): Unit

    Attributes
    protected
    Definition Classes
    OneTimeAlgorithm
  19. def eliminationOrder(factors: Traversable[Factor[(Double, Map[Parameter[_], Seq[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.

    Definition Classes
    VariableElimination
  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 finish(factorsAfterElimination: Set[Factor[(Double, Map[Parameter[_], Seq[Double]])]], eliminationOrder: List[Variable[_]]): Unit

    All implementation of variable elimination must specify what to do after variables have been eliminated.

    All implementation of variable elimination must specify what to do after variables have been eliminated.

    Definition Classes
    SufficientStatisticsVariableEliminationVariableElimination
  24. final def getClass(): Class[_]

    Definition Classes
    AnyRef → Any
  25. def getFactors(neededElements: List[Element[_]], targetElements: List[Element[_]], upper: Boolean = false): List[Factor[(Double, Map[Parameter[_], Seq[Double]])]]

    Particular implementations of probability of evidence algorithms must define the following method.

    Particular implementations of probability of evidence algorithms must define the following method.

    Definition Classes
    SufficientStatisticsVariableEliminationFactoredAlgorithm
  26. 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
  27. def getSufficientStatisticsForAllParameters: Map[Parameter[_], Seq[Double]]

    Returns a mapping of parameters to sufficient statistics resulting from elimination of the factors.

  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. var recordingFactors: List[Factor[_]]

    Attributes
    protected
    Definition Classes
    VariableElimination
  37. 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
  38. def run(): Unit

    Run the algorithm, performing its computation to completion.

    Run the algorithm, performing its computation to completion.

    Definition Classes
    VariableEliminationOneTime
  39. val semiring: SufficientStatisticsSemiring

    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
    SufficientStatisticsVariableEliminationFactoredAlgorithm
  40. val showTiming: Boolean

    No timing information enabled for this algorithm.

    No timing information enabled for this algorithm.

    Definition Classes
    SufficientStatisticsVariableEliminationVariableElimination
  41. 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
  42. def starterElements: List[Element[_]]

    Elements towards which queries are directed.

    Elements towards which queries are directed. By default, these are the target elements. This is overridden by DecisionVariableElimination, where it also includes utility variables.

    Definition Classes
    SufficientStatisticsVariableEliminationVariableElimination
  43. val statFactor: SufficientStatisticsFactor

    Attributes
    protected
  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. val targetElements: List[Element[_]]

    Empty for this algorithm.

    Empty for this algorithm.

    Definition Classes
    SufficientStatisticsVariableEliminationVariableElimination
  47. var targetFactors: Map[Element[_], Factor[(Double, Map[Parameter[_], Seq[Double]])]]

    Attributes
    protected[com.cra.figaro]
    Definition Classes
    VariableElimination
  48. def toString(): String

    Definition Classes
    AnyRef → Any
  49. 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
    SufficientStatisticsVariableEliminationVariableEliminationFactoredAlgorithm
  50. final def wait(): Unit

    Definition Classes
    AnyRef
    Annotations
    @throws( ... )
  51. final def wait(arg0: Long, arg1: Int): Unit

    Definition Classes
    AnyRef
    Annotations
    @throws( ... )
  52. final def wait(arg0: Long): Unit

    Definition Classes
    AnyRef
    Annotations
    @throws( ... )

Inherited from VariableElimination[(Double, Map[Parameter[_], Seq[Double]])]

Inherited from OneTime

Inherited from FactoredAlgorithm[(Double, Map[Parameter[_], Seq[Double]])]

Inherited from Algorithm

Inherited from AnyRef

Inherited from Any

Ungrouped