Class

com.cra.figaro.algorithm.learning

ExpectationMaximizationWithFactors

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class ExpectationMaximizationWithFactors extends ExpectationMaximization

An EM algorithm which learns parameters using a factored algorithm

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  1. ExpectationMaximizationWithFactors
  2. ExpectationMaximization
  3. ParameterLearner
  4. Algorithm
  5. AnyRef
  6. Any
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Instance Constructors

  1. new ExpectationMaximizationWithFactors(universe: Universe, targetParameters: Parameter[_]*)(terminationCriteria: () ⇒ EMTerminationCriteria)

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Value Members

  1. final def !=(arg0: Any): Boolean

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    Definition Classes
    AnyRef → Any
  2. final def ##(): Int

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    Definition Classes
    AnyRef → Any
  3. final def ==(arg0: Any): Boolean

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    Definition Classes
    AnyRef → Any
  4. var active: Boolean

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    Attributes
    protected
    Definition Classes
    Algorithm
  5. final def asInstanceOf[T0]: T0

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    Definition Classes
    Any
  6. def cleanUp(): Unit

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    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

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    Attributes
    protected[java.lang]
    Definition Classes
    AnyRef
    Annotations
    @throws( ... )
  8. var debug: Boolean

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    Definition Classes
    ExpectationMaximization
  9. def doExpectationStep(): Map[Parameter[_], Seq[Double]]

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    Attributes
    protected
    Definition Classes
    ExpectationMaximizationWithFactorsExpectationMaximization
  10. def doKill(): Unit

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    Attributes
    protected[com.cra.figaro.algorithm]
    Definition Classes
    ExpectationMaximizationAlgorithm
  11. def doMaximizationStep(parameterMapping: Map[Parameter[_], Seq[Double]]): Unit

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    Attributes
    protected
    Definition Classes
    ExpectationMaximization
  12. def doResume(): Unit

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    Attributes
    protected[com.cra.figaro.algorithm]
    Definition Classes
    ExpectationMaximizationAlgorithm
  13. def doStart(): Unit

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    Attributes
    protected[com.cra.figaro.algorithm]
    Definition Classes
    ExpectationMaximizationAlgorithm
  14. def doStop(): Unit

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    Attributes
    protected[com.cra.figaro.algorithm]
    Definition Classes
    ExpectationMaximizationAlgorithm
  15. def em(): Unit

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    Attributes
    protected
    Definition Classes
    ExpectationMaximization
  16. final def eq(arg0: AnyRef): Boolean

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    Definition Classes
    AnyRef
  17. def equals(arg0: Any): Boolean

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    Definition Classes
    AnyRef → Any
  18. def finalize(): Unit

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    protected[java.lang]
    Definition Classes
    AnyRef
    Annotations
    @throws( classOf[java.lang.Throwable] )
  19. final def getClass(): Class[_]

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    Definition Classes
    AnyRef → Any
  20. def hashCode(): Int

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    Definition Classes
    AnyRef → Any
  21. def initialize(): Unit

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    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

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    Definition Classes
    Algorithm
  23. final def isInstanceOf[T0]: Boolean

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    Definition Classes
    Any
  24. def iteration(): Unit

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    Definition Classes
    ExpectationMaximization
  25. def kill(): Unit

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    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. final def ne(arg0: AnyRef): Boolean

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    Definition Classes
    AnyRef
  27. final def notify(): Unit

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    Definition Classes
    AnyRef
  28. final def notifyAll(): Unit

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    Definition Classes
    AnyRef
  29. val paramMap: Map[Parameter[_], Seq[Double]]

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    Attributes
    protected
    Definition Classes
    ExpectationMaximization
  30. def resume(): Unit

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    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
  31. def start(): Unit

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    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
  32. def stop(): Unit

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    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
  33. var sufficientStatistics: Map[Parameter[_], Seq[Double]]

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    Definition Classes
    ExpectationMaximization
  34. final def synchronized[T0](arg0: ⇒ T0): T0

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    Definition Classes
    AnyRef
  35. val targetParameters: Parameter[_]*

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  36. val terminationCriteria: () ⇒ EMTerminationCriteria

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  37. def toString(): String

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    Definition Classes
    AnyRef → Any
  38. val universe: Universe

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  39. final def wait(): Unit

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

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    Definition Classes
    AnyRef
    Annotations
    @throws( ... )
  41. final def wait(arg0: Long): Unit

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    Definition Classes
    AnyRef
    Annotations
    @throws( ... )

Inherited from ExpectationMaximization

Inherited from ParameterLearner

Inherited from Algorithm

Inherited from AnyRef

Inherited from Any

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