abstract class Filtering extends Algorithm

The general class of filtering algorithms. A filtering algorithm is provided with an initial model, represented by a universe encoding the probability distribution over the initial state, and a transition model, which maps a state to a universe encoding the probability distribution over the new state. An implementation of Filtering must implement the advanceTime, computeCurrentDistribution, and computeCurrentExpectation methods.

Querying and asserting evidence to a filtering algorithm are done using references. This is because references are stable over time, while the particular elements they refer to are not.

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Instance Constructors

  1. new Filtering(static: Universe = new Universe(), initial: Universe, transition: (Universe, Universe) ⇒ Universe)

    static

    A static universe that other universes may depend upon.

    initial

    The initial universe.

    transition

    A transition function from a universe at the old time step to a new.

Abstract Value Members

  1. abstract def advanceTime(evidence: Seq[NamedEvidence[_]]): Unit

    Advance the filtering one time step, conditioning on the given evidence at the new time point.

  2. abstract def computeCurrentDistribution[T](reference: Reference[T]): Stream[(Double, T)]

    Returns the distribution over the element referred to by the reference at the current time point.

    Returns the distribution over the element referred to by the reference at the current time point.

    Attributes
    protected
  3. abstract def computeCurrentExpectation[T](reference: Reference[T], function: (T) ⇒ Double): Double

    Returns the expectation of the element referred to by the reference under the given function at the current time point.

    Returns the expectation of the element referred to by the reference under the given function at the current time point.

    Attributes
    protected
  4. abstract def currentDistribution[T](reference: Reference[T]): Stream[(Double, T)]

    Returns the distribution over the element referred to by the reference at the current time point.

  5. abstract def currentExpectation[T](reference: Reference[T], function: (T) ⇒ Double): Double

    Returns the expectation of the element referred to by the reference under the given function at the current time point.

  6. abstract def currentProbability[T](reference: Reference[T], predicate: (T) ⇒ Boolean): Double

    Returns the probability that the element referred to by the reference satisfies the given predicate at the current time point.

  7. abstract def doKill(): Unit
    Attributes
    protected[com.cra.figaro.algorithm]
    Definition Classes
    Algorithm
  8. abstract def doResume(): Unit
    Attributes
    protected[com.cra.figaro.algorithm]
    Definition Classes
    Algorithm
  9. abstract def doStart(): Unit
    Attributes
    protected[com.cra.figaro.algorithm]
    Definition Classes
    Algorithm
  10. abstract def doStop(): Unit
    Attributes
    protected[com.cra.figaro.algorithm]
    Definition Classes
    Algorithm

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 cleanUp(): Unit

    Called when the algorithm is killed.

    Called when the algorithm is killed. By default, does nothing. Can be overridden.

    Definition Classes
    Algorithm
  7. def clone(): AnyRef
    Attributes
    protected[java.lang]
    Definition Classes
    AnyRef
    Annotations
    @throws( ... )
  8. def computeCurrentProbability[T](reference: Reference[T], predicate: (T) ⇒ Boolean): Double

    Returns the probability that the element referred to by the reference satisfies the given predicate at the current time point.

    Returns the probability that the element referred to by the reference satisfies the given predicate at the current time point.

    Attributes
    protected
  9. def currentProbability[T](reference: Reference[T], value: T): Double

    Returns the probability that the element referred to by the reference produces the given value at the current time point.

  10. final def eq(arg0: AnyRef): Boolean
    Definition Classes
    AnyRef
  11. def equals(arg0: Any): Boolean
    Definition Classes
    AnyRef → Any
  12. def finalize(): Unit
    Attributes
    protected[java.lang]
    Definition Classes
    AnyRef
    Annotations
    @throws( classOf[java.lang.Throwable] )
  13. final def getClass(): Class[_]
    Definition Classes
    AnyRef → Any
  14. def hashCode(): Int
    Definition Classes
    AnyRef → Any
  15. 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
  16. def isActive: Boolean
    Definition Classes
    Algorithm
  17. final def isInstanceOf[T0]: Boolean
    Definition Classes
    Any
  18. 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
  19. final def ne(arg0: AnyRef): Boolean
    Definition Classes
    AnyRef
  20. final def notify(): Unit
    Definition Classes
    AnyRef
  21. final def notifyAll(): Unit
    Definition Classes
    AnyRef
  22. 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
  23. 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
  24. 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
  25. final def synchronized[T0](arg0: ⇒ T0): T0
    Definition Classes
    AnyRef
  26. def toString(): String
    Definition Classes
    AnyRef → Any
  27. final def wait(): Unit
    Definition Classes
    AnyRef
    Annotations
    @throws( ... )
  28. final def wait(arg0: Long, arg1: Int): Unit
    Definition Classes
    AnyRef
    Annotations
    @throws( ... )
  29. final def wait(arg0: Long): Unit
    Definition Classes
    AnyRef
    Annotations
    @throws( ... )

Inherited from Algorithm

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