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
Ordering
  1. Alphabetic
  2. By Inheritance
Inherited
  1. SufficientStatisticsVariableElimination
  2. VariableElimination
  3. OneTime
  4. FactoredAlgorithm
  5. Algorithm
  6. AnyRef
  7. Any
  1. Hide All
  2. Show All
Visibility
  1. Public
  2. All

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: 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
    SufficientStatisticsVariableEliminationAlgorithm
  7. def clone(): AnyRef
    Attributes
    protected[java.lang]
    Definition Classes
    AnyRef
    Annotations
    @throws( ... )
  8. 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
  9. val 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
  10. val dependentAlgorithm: (Universe, List[NamedEvidence[_]]) ⇒ () ⇒ Double
  11. val dependentUniverses: List[(Universe, List[NamedEvidence[_]])]
  12. def doElimination(allFactors: List[Factor[(Double, Map[Parameter[_], Seq[Double]])]], targetVariables: Seq[Variable[_]]): Unit
    Attributes
    protected
    Definition Classes
    VariableElimination
  13. def doKill(): Unit
    Attributes
    protected[com.cra.figaro.algorithm]
    Definition Classes
    OneTimeAlgorithm
  14. def doResume(): Unit
    Attributes
    protected[com.cra.figaro.algorithm]
    Definition Classes
    OneTimeAlgorithm
  15. def doStart(): Unit
    Attributes
    protected[com.cra.figaro.algorithm]
    Definition Classes
    OneTimeAlgorithm
  16. def doStop(): Unit
    Attributes
    protected[com.cra.figaro.algorithm]
    Definition Classes
    OneTimeAlgorithm
  17. def eliminateInOrder(order: List[Variable[_]], factors: MultiSet[Factor[(Double, Map[Parameter[_], Seq[Double]])]], map: FactorMap[(Double, Map[Parameter[_], Seq[Double]])]): MultiSet[Factor[(Double, Map[Parameter[_], Seq[Double]])]]
    Attributes
    protected
    Definition Classes
    VariableElimination
  18. final def eq(arg0: AnyRef): Boolean
    Definition Classes
    AnyRef
  19. def equals(arg0: Any): Boolean
    Definition Classes
    AnyRef → Any
  20. def finalize(): Unit
    Attributes
    protected[java.lang]
    Definition Classes
    AnyRef
    Annotations
    @throws( classOf[java.lang.Throwable] )
  21. def finish(factorsAfterElimination: MultiSet[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
  22. final def getClass(): Class[_]
    Definition Classes
    AnyRef → Any
  23. 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
  24. 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
  25. def getSufficientStatisticsForAllParameters: Map[Parameter[_], Seq[Double]]

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

  26. def hashCode(): Int
    Definition Classes
    AnyRef → Any
  27. def initialFactorMap(factors: Traversable[Factor[(Double, Map[Parameter[_], Seq[Double]])]]): FactorMap[(Double, Map[Parameter[_], Seq[Double]])]
    Attributes
    protected
    Definition Classes
    VariableElimination
  28. 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
  29. def isActive: Boolean
    Definition Classes
    Algorithm
  30. final def isInstanceOf[T0]: Boolean
    Definition Classes
    Any
  31. 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
  32. final def ne(arg0: AnyRef): Boolean
    Definition Classes
    AnyRef
  33. final def notify(): Unit
    Definition Classes
    AnyRef
  34. final def notifyAll(): Unit
    Definition Classes
    AnyRef
  35. val recordingFactors: List[Factor[_]]
    Attributes
    protected
    Definition Classes
    VariableElimination
  36. 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
  37. def run(): Unit

    Run the algorithm, performing its computation to completion.

    Run the algorithm, performing its computation to completion.

    Definition Classes
    VariableEliminationOneTime
  38. val semiring: SufficientStatisticsSemiring
  39. val showTiming: Boolean

    No timing information enabled for this algorithm.

    No timing information enabled for this algorithm.

    Definition Classes
    SufficientStatisticsVariableEliminationVariableElimination
  40. 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
  41. 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
  42. val statFactor: SufficientStatisticsFactor
    Attributes
    protected
  43. 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
  44. final def synchronized[T0](arg0: ⇒ T0): T0
    Definition Classes
    AnyRef
  45. val targetElements: List[Element[_]]

    Empty for this algorithm.

    Empty for this algorithm.

    Definition Classes
    SufficientStatisticsVariableEliminationVariableElimination
  46. val targetFactors: Map[Element[_], Factor[(Double, Map[Parameter[_], Seq[Double]])]]
    Attributes
    protected[com.cra.figaro]
    Definition Classes
    VariableElimination
  47. def toString(): String
    Definition Classes
    AnyRef → Any
  48. 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 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