trait BeliefPropagation[T] extends FactoredAlgorithm[T]

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Abstract Value Members

  1. abstract 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
    FactoredAlgorithm
  2. abstract 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
    FactoredAlgorithm
  3. abstract def doKill(): Unit
    Attributes
    protected[com.cra.figaro.algorithm]
    Definition Classes
    Algorithm
  4. abstract def doResume(): Unit
    Attributes
    protected[com.cra.figaro.algorithm]
    Definition Classes
    Algorithm
  5. abstract def doStart(): Unit
    Attributes
    protected[com.cra.figaro.algorithm]
    Definition Classes
    Algorithm
  6. abstract def doStop(): Unit
    Attributes
    protected[com.cra.figaro.algorithm]
    Definition Classes
    Algorithm
  7. abstract def getFactors(neededElements: List[Element[_]], targetElements: List[Element[_]], upperBounds: Boolean = false): List[Factor[T]]

    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
    FactoredAlgorithm
  8. abstract val semiring: LogConvertibleSemiRing[T]

    Since BP uses division to compute messages, the semiring has to have a division function defined and must be log convertable.

    Since BP uses division to compute messages, the semiring has to have a division function defined and must be log convertable. Note that BP operates in log space and any semiring must be log convertible If you define a non-log semiring, it will automatically convert, and convert it back to normal space at the end If you define a log semiring, it won't convert to log or convert from log. In other words, it outputs the answer in the space specified by the semiring

    Definition Classes
    BeliefPropagationFactoredAlgorithm
  9. abstract val targetElements: List[Element[_]]

    Target elements that should not be eliminated but should be available for querying.

  10. abstract val universe: Universe

    The universe on which this belief propagation algorithm should be applied.

    The universe on which this belief propagation algorithm should be applied.

    Definition Classes
    BeliefPropagationFactoredAlgorithm

Concrete 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 belief(source: Node): Factor[T]

    Returns the product of all messages from a source node's neighbors to itself.

  7. 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
  8. def clone(): AnyRef
    Attributes
    protected[java.lang]
    Definition Classes
    AnyRef
    Annotations
    @throws( ... )
  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.

  10. final def eq(arg0: AnyRef): Boolean
    Definition Classes
    AnyRef
  11. def equals(arg0: Any): Boolean
    Definition Classes
    AnyRef → Any
  12. val factorGraph: FactorGraph[T]
    Attributes
    protected[com.cra.figaro]
  13. def finalize(): Unit
    Attributes
    protected[java.lang]
    Definition Classes
    AnyRef
    Annotations
    @throws( classOf[java.lang.Throwable] )
  14. final def getClass(): Class[_]
    Definition Classes
    AnyRef → Any
  15. 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
  16. def getNewMessageFactorToVar(fn: FactorNode, vn: VariableNode): Factor[T]
    Attributes
    protected
  17. def getNewMessageVarToFactor(vn: VariableNode, fn: FactorNode): Factor[T]
    Attributes
    protected
  18. def hashCode(): Int
    Definition Classes
    AnyRef → Any
  19. 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
    BeliefPropagationAlgorithm
  20. def isActive: Boolean
    Definition Classes
    Algorithm
  21. final def isInstanceOf[T0]: Boolean
    Definition Classes
    Any
  22. 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
  23. def logSpaceSemiring(): LogConvertibleSemiRing[T]

    Returns the log space version of the semiring (or the semiring if already in log space)

    Returns the log space version of the semiring (or the semiring if already in log space)

    Attributes
    protected
  24. final def ne(arg0: AnyRef): Boolean
    Definition Classes
    AnyRef
  25. def newMessage(source: Node, target: Node): Factor[T]
    Attributes
    protected[com.cra.figaro]
  26. final def notify(): Unit
    Definition Classes
    AnyRef
  27. final def notifyAll(): Unit
    Definition Classes
    AnyRef
  28. 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
  29. def runStep(): Unit

    Runs this belief propagation algorithm for one iteration.

    Runs this belief propagation algorithm for one iteration. An iteration consists of each node of the factor graph sending a message to each of its neighbors.

  30. 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
  31. 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.

  32. 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
  33. final def synchronized[T0](arg0: ⇒ T0): T0
    Definition Classes
    AnyRef
  34. def toString(): String
    Definition Classes
    AnyRef → Any
  35. final def wait(): Unit
    Definition Classes
    AnyRef
    Annotations
    @throws( ... )
  36. final def wait(arg0: Long, arg1: Int): Unit
    Definition Classes
    AnyRef
    Annotations
    @throws( ... )
  37. final def wait(arg0: Long): Unit
    Definition Classes
    AnyRef
    Annotations
    @throws( ... )

Inherited from FactoredAlgorithm[T]

Inherited from Algorithm

Inherited from AnyRef

Inherited from Any

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