t

com.cra.figaro.algorithm.learning

OnlineExpectationMaximization

trait OnlineExpectationMaximization extends Online with ExpectationMaximization

An EM algorithm which learns parameters incrementally

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Inherited
  1. OnlineExpectationMaximization
  2. ExpectationMaximization
  3. ParameterLearner
  4. Online
  5. Algorithm
  6. AnyRef
  7. Any
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Visibility
  1. Public
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Abstract Value Members

  1. abstract def doExpectationStep(): Map[Parameter[_], Seq[Double]]
    Attributes
    protected
    Definition Classes
    ExpectationMaximization
  2. abstract val initial: Universe
    Definition Classes
    OnlineExpectationMaximizationOnline
  3. abstract val targetParameters: Seq[Parameter[_]]
    Definition Classes
    ExpectationMaximization
  4. abstract val terminationCriteria: () ⇒ EMTerminationCriteria
    Definition Classes
    ExpectationMaximization
  5. abstract val transition: () ⇒ Universe
    Definition Classes
    OnlineExpectationMaximizationOnline

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. val currentUniverse: Universe
    Attributes
    protected
  9. val debug: Boolean
    Definition Classes
    ExpectationMaximization
  10. def doKill(): Unit
    Attributes
    protected[com.cra.figaro.algorithm]
    Definition Classes
    ExpectationMaximizationAlgorithm
  11. def doMaximizationStep(parameterMapping: Map[Parameter[_], Seq[Double]]): Unit
    Attributes
    protected
    Definition Classes
    ExpectationMaximization
  12. def doResume(): Unit
    Attributes
    protected[com.cra.figaro.algorithm]
    Definition Classes
    ExpectationMaximizationAlgorithm
  13. def doStart(): Unit
  14. def doStop(): Unit
    Attributes
    protected[com.cra.figaro.algorithm]
    Definition Classes
    ExpectationMaximizationAlgorithm
  15. def em(): Unit
    Attributes
    protected
    Definition Classes
    ExpectationMaximization
  16. final def eq(arg0: AnyRef): Boolean
    Definition Classes
    AnyRef
  17. def equals(arg0: Any): Boolean
    Definition Classes
    AnyRef → Any
  18. def finalize(): Unit
    Attributes
    protected[java.lang]
    Definition Classes
    AnyRef
    Annotations
    @throws( classOf[java.lang.Throwable] )
  19. final def getClass(): Class[_]
    Definition Classes
    AnyRef → Any
  20. def hashCode(): Int
    Definition Classes
    AnyRef → Any
  21. 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
  22. def isActive: Boolean
    Definition Classes
    Algorithm
  23. final def isInstanceOf[T0]: Boolean
    Definition Classes
    Any
  24. def iteration(): Unit
    Definition Classes
    ExpectationMaximization
  25. 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
  26. val lastIterationStatistics: Map[Parameter[_], Seq[Double]]
    Attributes
    protected
  27. final def ne(arg0: AnyRef): Boolean
    Definition Classes
    AnyRef
  28. final def notify(): Unit
    Definition Classes
    AnyRef
  29. final def notifyAll(): Unit
    Definition Classes
    AnyRef
  30. val paramMap: Map[Parameter[_], Seq[Double]]
    Attributes
    protected
    Definition Classes
    ExpectationMaximization
  31. 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
  32. 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
  33. 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
  34. val sufficientStatistics: Map[Parameter[_], Seq[Double]]
    Definition Classes
    ExpectationMaximization
  35. final def synchronized[T0](arg0: ⇒ T0): T0
    Definition Classes
    AnyRef
  36. def toString(): String
    Definition Classes
    AnyRef → Any
  37. def update(evidence: Seq[NamedEvidence[_]] = Seq()): Unit

    Observe new evidence and perform one expectation step and one maximization step

    Observe new evidence and perform one expectation step and one maximization step

    Definition Classes
    OnlineExpectationMaximizationOnline
  38. final def wait(): Unit
    Definition Classes
    AnyRef
    Annotations
    @throws( ... )
  39. final def wait(arg0: Long, arg1: Int): Unit
    Definition Classes
    AnyRef
    Annotations
    @throws( ... )
  40. final def wait(arg0: Long): Unit
    Definition Classes
    AnyRef
    Annotations
    @throws( ... )

Inherited from ExpectationMaximization

Inherited from ParameterLearner

Inherited from Online

Inherited from Algorithm

Inherited from AnyRef

Inherited from Any

Ungrouped