t

com.cra.figaro.algorithm.learning

ExpectationMaximization

trait ExpectationMaximization extends Algorithm with ParameterLearner

Expectation maximization iteratively produces an estimate of sufficient statistics for learnable parameters, then maximizes the parameters according to the estimate. This trait can be extended with a different expectation or maximization algorithm; see the code for details.

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  2. ParameterLearner
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Abstract Value Members

  1. abstract def doExpectationStep(): Map[Parameter[_], Seq[Double]]
    Attributes
    protected
  2. abstract val targetParameters: Seq[Parameter[_]]
  3. abstract val terminationCriteria: () ⇒ EMTerminationCriteria

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 debug: Boolean
  9. def doKill(): Unit
    Attributes
    protected[com.cra.figaro.algorithm]
    Definition Classes
    ExpectationMaximizationAlgorithm
  10. def doMaximizationStep(parameterMapping: Map[Parameter[_], Seq[Double]]): Unit
    Attributes
    protected
  11. def doResume(): Unit
    Attributes
    protected[com.cra.figaro.algorithm]
    Definition Classes
    ExpectationMaximizationAlgorithm
  12. def doStart(): Unit
    Attributes
    protected[com.cra.figaro.algorithm]
    Definition Classes
    ExpectationMaximizationAlgorithm
  13. def doStop(): Unit
    Attributes
    protected[com.cra.figaro.algorithm]
    Definition Classes
    ExpectationMaximizationAlgorithm
  14. def em(): Unit
    Attributes
    protected
  15. final def eq(arg0: AnyRef): Boolean
    Definition Classes
    AnyRef
  16. def equals(arg0: Any): Boolean
    Definition Classes
    AnyRef → Any
  17. def finalize(): Unit
    Attributes
    protected[java.lang]
    Definition Classes
    AnyRef
    Annotations
    @throws( classOf[java.lang.Throwable] )
  18. final def getClass(): Class[_]
    Definition Classes
    AnyRef → Any
  19. def hashCode(): Int
    Definition Classes
    AnyRef → Any
  20. 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
  21. def isActive: Boolean
    Definition Classes
    Algorithm
  22. final def isInstanceOf[T0]: Boolean
    Definition Classes
    Any
  23. def iteration(): Unit
  24. 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
  25. final def ne(arg0: AnyRef): Boolean
    Definition Classes
    AnyRef
  26. final def notify(): Unit
    Definition Classes
    AnyRef
  27. final def notifyAll(): Unit
    Definition Classes
    AnyRef
  28. val paramMap: Map[Parameter[_], Seq[Double]]
    Attributes
    protected
  29. 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
  30. 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
  31. 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
  32. val sufficientStatistics: Map[Parameter[_], Seq[Double]]
  33. final def synchronized[T0](arg0: ⇒ T0): T0
    Definition Classes
    AnyRef
  34. def toString(): String
    Definition Classes
    AnyRef → Any
  35. final def wait(): Unit
    Definition Classes
    AnyRef
    Annotations
    @throws( ... )
  36. final def wait(arg0: Long, arg1: Int): Unit
    Definition Classes
    AnyRef
    Annotations
    @throws( ... )
  37. final def wait(arg0: Long): Unit
    Definition Classes
    AnyRef
    Annotations
    @throws( ... )

Inherited from ParameterLearner

Inherited from Algorithm

Inherited from AnyRef

Inherited from Any

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