t

com.cra.figaro.algorithm.factored.beliefpropagation

OneTimeProbEvidenceBeliefPropagation

trait OneTimeProbEvidenceBeliefPropagation extends OneTimeProbabilisticBeliefPropagation with OneTimeProbEvidence with ProbEvidenceBeliefPropagation

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Inherited
  1. OneTimeProbEvidenceBeliefPropagation
  2. ProbEvidenceBeliefPropagation
  3. OneTimeProbEvidence
  4. ProbEvidenceAlgorithm
  5. OneTimeProbabilisticBeliefPropagation
  6. OneTime
  7. ProbabilisticBeliefPropagation
  8. BeliefPropagation
  9. FactoredAlgorithm
  10. Algorithm
  11. AnyRef
  12. Any
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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 iterations: Int
  4. abstract val semiring: LogConvertibleSemiRing[Double]

    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
  5. 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
  6. abstract val universe: Universe
    Definition Classes
    ProbEvidenceAlgorithm

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. def additionalEvidenceAlgorithm(evidence: List[NamedEvidence[_]]): ProbEvidenceAlgorithm

    The algorithm used to compute the probability of additional evidence, as created by probAdditionalEvidence.

    The algorithm used to compute the probability of additional evidence, as created by probAdditionalEvidence. This algorithm can be different to the one defined in this class. (For example, a one-time algorithm can use an anytime algorithm for additional evidence.)

    Definition Classes
    OneTimeProbEvidenceBeliefPropagationProbEvidenceAlgorithm
  6. final def asInstanceOf[T0]: T0
    Definition Classes
    Any
  7. 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
  8. def cleanUp(): Unit

    Removes the evidence provided in the constructor from the universe.

    Removes the evidence provided in the constructor from the universe.

    Definition Classes
    ProbEvidenceAlgorithmAlgorithm
  9. def clone(): AnyRef
    Attributes
    protected[java.lang]
    Definition Classes
    AnyRef
    Annotations
    @throws( ... )
  10. def computedResult(): Double

    Compute the evidence of the model.

    Compute the evidence of the model. Returns the probability of evidence on the model. This assumes that BP has already been run on this algorithm instance.

    Definition Classes
    ProbEvidenceBeliefPropagationProbEvidenceAlgorithm
  11. def convertFactors(factors: List[Factor[Double]]): List[Factor[Double]]
    Attributes
    protected
    Definition Classes
    ProbabilisticBeliefPropagation
  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. val denominator: Double
    Definition Classes
    ProbEvidenceAlgorithm
  14. def doKill(): Unit
    Attributes
    protected[com.cra.figaro.algorithm]
    Definition Classes
    OneTimeAlgorithm
  15. def doResume(): Unit
    Attributes
    protected[com.cra.figaro.algorithm]
    Definition Classes
    OneTimeAlgorithm
  16. def doStart(): Unit
    Attributes
    protected[com.cra.figaro.algorithm]
    Definition Classes
    OneTimeAlgorithm
  17. def doStop(): Unit
    Attributes
    protected[com.cra.figaro.algorithm]
    Definition Classes
    OneTimeAlgorithm
  18. final def eq(arg0: AnyRef): Boolean
    Definition Classes
    AnyRef
  19. def equals(arg0: Any): Boolean
    Definition Classes
    AnyRef → Any
  20. val evidence: List[NamedEvidence[_]]
    Definition Classes
    ProbEvidenceAlgorithm
  21. val factorGraph: FactorGraph[Double]
    Attributes
    protected[com.cra.figaro]
    Definition Classes
    BeliefPropagation
  22. def factorToBeliefs[T](factor: Factor[Double]): List[Tuple2[Double, _]]
    Attributes
    protected[com.cra.figaro]
    Definition Classes
    ProbabilisticBeliefPropagation
  23. def finalize(): Unit
    Attributes
    protected[java.lang]
    Definition Classes
    AnyRef
    Annotations
    @throws( classOf[java.lang.Throwable] )
  24. def findNodeForElement[T](target: Element[T]): Node
    Attributes
    protected[com.cra.figaro]
    Definition Classes
    ProbabilisticBeliefPropagation
  25. 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
  26. final def getClass(): Class[_]
    Definition Classes
    AnyRef → Any
  27. 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
    ProbabilisticBeliefPropagationFactoredAlgorithm
  28. 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
  29. 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
  30. def getNewMessageFactorToVar(fn: FactorNode, vn: VariableNode): Factor[Double]
    Attributes
    protected
    Definition Classes
    BeliefPropagation
  31. def getNewMessageVarToFactor(vn: VariableNode, fn: FactorNode): Factor[Double]
    Attributes
    protected
    Definition Classes
    BeliefPropagation
  32. def hashCode(): Int
    Definition Classes
    AnyRef → Any
  33. def initialize(): Unit

    Since probability of evidence algorithms introduce additional evidence (namely, their evidence argument), into an existing universe, a mechanism must be provided for introducing the evidence when the algorithm begins and cleaning it up at the end.

    Since probability of evidence algorithms introduce additional evidence (namely, their evidence argument), into an existing universe, a mechanism must be provided for introducing the evidence when the algorithm begins and cleaning it up at the end. This is achieved with the initialize method, called when the algorithm starts, and the cleanUp method, called when the algorithm is killed.

    Definition Classes
    ProbEvidenceAlgorithmAlgorithm
  34. def isActive: Boolean
    Definition Classes
    Algorithm
  35. final def isInstanceOf[T0]: Boolean
    Definition Classes
    Any
  36. 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
  37. def logProbEvidence: Double

    The computed log probability of evidence.

    The computed log probability of evidence.

    Definition Classes
    ProbEvidenceAlgorithm
  38. def logSpaceSemiring(): LogConvertibleSemiRing[Double]

    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
    Definition Classes
    BeliefPropagation
  39. final def ne(arg0: AnyRef): Boolean
    Definition Classes
    AnyRef
  40. def newMessage(source: Node, target: Node): Factor[Double]
    Attributes
    protected[com.cra.figaro]
    Definition Classes
    ProbabilisticBeliefPropagationBeliefPropagation
  41. def normalize(factor: Factor[Double]): Factor[Double]

    Normalize a factor.

    Normalize a factor.

    Definition Classes
    ProbabilisticBeliefPropagation
  42. final def notify(): Unit
    Definition Classes
    AnyRef
  43. final def notifyAll(): Unit
    Definition Classes
    AnyRef
  44. def probAdditionalEvidence(evidence: List[NamedEvidence[_]]): ProbEvidenceAlgorithm

    Returns an algorithm to compute the probability of the additional evidence provided.

    Returns an algorithm to compute the probability of the additional evidence provided.

    Definition Classes
    ProbEvidenceAlgorithm
  45. def probEvidence: Double

    The computed probability of evidence.

    The computed probability of evidence.

    Definition Classes
    ProbEvidenceAlgorithm
  46. def probabilityOfEvidence(): Double

    Returns the probability of evidence of the universe on which the algorithm operates.

    Returns the probability of evidence of the universe on which the algorithm operates. Throws AlgorithmInactiveException if the algorithm is not active.

    Definition Classes
    OneTimeProbEvidence
  47. 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
  48. def run(): Unit

    Run the algorithm, performing its computation to completion.

    Run the algorithm, performing its computation to completion.

    Definition Classes
    OneTimeProbabilisticBeliefPropagationOneTime
  49. 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
  50. 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
  51. 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
  52. 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
  53. final def synchronized[T0](arg0: ⇒ T0): T0
    Definition Classes
    AnyRef
  54. def toString(): String
    Definition Classes
    AnyRef → Any
  55. final def wait(): Unit
    Definition Classes
    AnyRef
    Annotations
    @throws( ... )
  56. final def wait(arg0: Long, arg1: Int): Unit
    Definition Classes
    AnyRef
    Annotations
    @throws( ... )
  57. final def wait(arg0: Long): Unit
    Definition Classes
    AnyRef
    Annotations
    @throws( ... )

Inherited from OneTimeProbEvidence

Inherited from ProbEvidenceAlgorithm

Inherited from OneTime

Inherited from BeliefPropagation[Double]

Inherited from FactoredAlgorithm[Double]

Inherited from Algorithm

Inherited from AnyRef

Inherited from Any

Ungrouped