com.cra.figaro.experimental.particlebp

ParticleBeliefPropagation

trait ParticleBeliefPropagation extends FactoredAlgorithm[Double] with InnerBPHandler

Doc needed

Linear Supertypes
InnerBPHandler, FactoredAlgorithm[Double], Algorithm, AnyRef, Any
Known Subclasses
AnytimeParticleBeliefPropagation, OneTimeParticleBeliefPropagation, ProbQueryParticleBeliefPropagation
Ordering
  1. Alphabetic
  2. By inheritance
Inherited
  1. ParticleBeliefPropagation
  2. InnerBPHandler
  3. FactoredAlgorithm
  4. Algorithm
  5. AnyRef
  6. Any
  1. Hide All
  2. Show all
Learn more about member selection
Visibility
  1. Public
  2. All

Abstract Value Members

  1. abstract def createBP(targets: List[Element[_]], depth: Int = Int.MaxValue, upperBounds: Boolean = false): Unit

    Instantiates the appropriate BP algorithm for the current time step.

    Instantiates the appropriate BP algorithm for the current time step.

    Attributes
    protected
    Definition Classes
    InnerBPHandler

  2. abstract val densityEstimator: DensityEstimator

    The density estimator that will estimate the density of a particle.

    The density estimator that will estimate the density of a particle. used for resampling.

  3. 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

  4. 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

  5. abstract def doKill(): Unit

    Attributes
    protected
    Definition Classes
    Algorithm

  6. abstract def doResume(): Unit

    Attributes
    protected
    Definition Classes
    Algorithm

  7. abstract def doStart(): Unit

    Attributes
    protected
    Definition Classes
    Algorithm

  8. abstract def doStop(): Unit

    Attributes
    protected
    Definition Classes
    Algorithm

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

    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

  10. abstract val pbpSampler: ParticleGenerator

    A particle generator to generate particles and do resampling.

  11. abstract def runBP(): Unit

    Runs the BP algorithm at the current time step.

    Runs the BP algorithm at the current time step.

    Attributes
    protected
    Definition Classes
    InnerBPHandler

  12. 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
    ParticleBeliefPropagation → FactoredAlgorithm

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

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

  14. 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
    ParticleBeliefPropagation → FactoredAlgorithm

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. var active: Boolean

    Attributes
    protected
    Definition Classes
    Algorithm

  5. final def asInstanceOf[T0]: T0

    Definition Classes
    Any

  6. var bp: ProbQueryBeliefPropagation

    BP algorithm associated with this time step.

    BP algorithm associated with this time step.

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

  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. var currentUniverse: Universe

    Universe associated with this algorithm.

    Universe associated with this algorithm.

    Attributes
    protected
    Definition Classes
    InnerBPHandler

  10. 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.

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

    Definition Classes
    AnyRef

  12. def equals(arg0: Any): Boolean

    Definition Classes
    AnyRef → Any

  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): (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 hashCode(): Int

    Definition Classes
    AnyRef → Any

  17. 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

  18. def isActive: Boolean

    Definition Classes
    Algorithm

  19. final def isInstanceOf[T0]: Boolean

    Definition Classes
    Any

  20. 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

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

    Definition Classes
    AnyRef

  22. final def notify(): Unit

    Definition Classes
    AnyRef

  23. final def notifyAll(): Unit

    Definition Classes
    AnyRef

  24. val preserveUnchangedGraph: Boolean

    Variable that if set to true, will preserve parts of the factor graph that cannot change during resampling.

    Variable that if set to true, will preserve parts of the factor graph that cannot change during resampling. This will preserve the messages in those parts of the factor graph. This feature is experimental and not guaranteed to work currently. Default is false.

  25. def proposalEstimator(beliefs: List[Tuple2[Double, _]]): Double

  26. 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

  27. def runStep(): Unit

    Runs this particle belief propagation algorithm for one iteration.

    Runs this particle belief propagation algorithm for one iteration. An iteration here is one iteration of the outer loop. This means that the inner BP loop may run several iterations.

  28. 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

  29. def starterElements: List[Element[_]]

    Elements towards which queries are directed.

    Elements towards which queries are directed. By default, these are the target elements.

  30. 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

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

    Definition Classes
    AnyRef

  32. def toString(): String

    Definition Classes
    AnyRef → Any

  33. final def wait(): Unit

    Definition Classes
    AnyRef
    Annotations
    @throws( ... )

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

    Definition Classes
    AnyRef
    Annotations
    @throws( ... )

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

    Definition Classes
    AnyRef
    Annotations
    @throws( ... )

Inherited from InnerBPHandler

Inherited from FactoredAlgorithm[Double]

Inherited from Algorithm

Inherited from AnyRef

Inherited from Any

Ungrouped