com.cra.figaro.algorithm.factored.beliefpropagation

ProbEvidenceBeliefPropagation

trait ProbEvidenceBeliefPropagation extends ProbabilisticBeliefPropagation

Linear Supertypes
ProbabilisticBeliefPropagation, BeliefPropagation[Double], FactoredAlgorithm[Double], Algorithm, AnyRef, Any
Known Subclasses
ProbQueryBeliefPropagation
Ordering
  1. Alphabetic
  2. By inheritance
Inherited
  1. ProbEvidenceBeliefPropagation
  2. ProbabilisticBeliefPropagation
  3. BeliefPropagation
  4. FactoredAlgorithm
  5. Algorithm
  6. AnyRef
  7. Any
  1. Hide All
  2. Show all
Learn more about member selection
Visibility
  1. Public
  2. All

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
    Definition Classes
    Algorithm

  4. abstract def doResume(): Unit

    Attributes
    protected
    Definition Classes
    Algorithm

  5. abstract def doStart(): Unit

    Attributes
    protected
    Definition Classes
    Algorithm

  6. abstract def doStop(): Unit

    Attributes
    protected
    Definition Classes
    Algorithm

  7. abstract val factorGraph: FactorGraph[Double]

    Attributes
    protected[com.cra.figaro]
    Definition Classes
    BeliefPropagation

  8. abstract val semiring: DivideableSemiRing[Double]

    Since BP uses division to compute messages, the semiring has to have a division function defined

    Since BP uses division to compute messages, the semiring has to have a division function defined

    Definition Classes
    BeliefPropagation → FactoredAlgorithm

  9. abstract val targetElements: List[Element[_]]

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

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

    Definition Classes
    BeliefPropagation

  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
    BeliefPropagation → FactoredAlgorithm

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

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

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

    Definition Classes
    BeliefPropagation

  9. 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

  10. def clone(): AnyRef

    Attributes
    protected[java.lang]
    Definition Classes
    AnyRef
    Annotations
    @throws( ... )

  11. def computeEvidence(): Double

  12. 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
    BeliefPropagation

  13. def entropy(probFactor: Factor[Double], logFactor: Factor[Double]): Double

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

    Definition Classes
    AnyRef

  15. def equals(arg0: Any): Boolean

    Definition Classes
    AnyRef → Any

  16. def finalize(): Unit

    Attributes
    protected[java.lang]
    Definition Classes
    AnyRef
    Annotations
    @throws( classOf[java.lang.Throwable] )

  17. def getBeliefsForElement[T](target: Element[T]): List[(Double, T)]

    Get the belief for an element

    Get the belief for an element

    Attributes
    protected[com.cra.figaro]
    Definition Classes
    ProbabilisticBeliefPropagation

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

    Definition Classes
    AnyRef → Any

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

    Returns the factors needed for BP.

    Returns the factors needed for BP. Since BP operates on a complete factor graph, factors are created for all elements in the universe.

    Definition Classes
    ProbabilisticBeliefPropagation → FactoredAlgorithm

  20. def getFinalFactorForElement[T](target: Element[T]): Factor[Double]

    Get the final factor for an element

    Get the final factor for an element

    Definition Classes
    ProbabilisticBeliefPropagation

  21. 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

  22. def hashCode(): Int

    Definition Classes
    AnyRef → Any

  23. 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
    BeliefPropagation → Algorithm

  24. def isActive: Boolean

    Definition Classes
    Algorithm

  25. final def isInstanceOf[T0]: Boolean

    Definition Classes
    Any

  26. 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

  27. def logFcn: (Double) ⇒ Double

  28. def mutualInformation(joint: Factor[Double], marginals: Iterable[Factor[Double]]): Double

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

    Definition Classes
    AnyRef

  30. def newMessage(source: Node, target: Node): Factor[Double]

    Attributes
    protected[com.cra.figaro]
    Definition Classes
    ProbabilisticBeliefPropagation → BeliefPropagation

  31. def normalize(factor: Factor[Double]): Factor[Double]

    Normalize a factor

    Normalize a factor

    Definition Classes
    ProbabilisticBeliefPropagation

  32. final def notify(): Unit

    Definition Classes
    AnyRef

  33. final def notifyAll(): Unit

    Definition Classes
    AnyRef

  34. def probFcn: (Double) ⇒ Double

  35. 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

  36. 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.

    Definition Classes
    BeliefPropagation

  37. 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

  38. 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
    BeliefPropagation

  39. 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

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

    Definition Classes
    AnyRef

  41. def toString(): String

    Definition Classes
    AnyRef → Any

  42. final def wait(): Unit

    Definition Classes
    AnyRef
    Annotations
    @throws( ... )

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

    Definition Classes
    AnyRef
    Annotations
    @throws( ... )

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

    Definition Classes
    AnyRef
    Annotations
    @throws( ... )

Inherited from ProbabilisticBeliefPropagation

Inherited from BeliefPropagation[Double]

Inherited from FactoredAlgorithm[Double]

Inherited from Algorithm

Inherited from AnyRef

Inherited from Any

Ungrouped