com.cra.figaro.algorithm.learning

ExpectationMaximization

class 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. It uses an factored inference algorithm, SufficientStatisticsVariableElimination, to produce the estimate of the sufficient statistics. This class can be extended with a different expectation or maximization algorithm; see the code for details.

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ParameterLearner, Algorithm, AnyRef, Any
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Instance Constructors

  1. new ExpectationMaximization(universe: Universe, targetParameters: Parameter[_]*)(numberOfIterations: Int)

Value Members

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

    Definition Classes
    AnyRef

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

    Definition Classes
    Any

  3. final def ##(): Int

    Definition Classes
    AnyRef → Any

  4. final def ==(arg0: AnyRef): Boolean

    Definition Classes
    AnyRef

  5. final def ==(arg0: Any): Boolean

    Definition Classes
    Any

  6. var active: Boolean

    Attributes
    protected
    Definition Classes
    Algorithm

  7. final def asInstanceOf[T0]: T0

    Definition Classes
    Any

  8. 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

  9. def clone(): AnyRef

    Attributes
    protected[java.lang]
    Definition Classes
    AnyRef
    Annotations
    @throws( ... )

  10. def doExpectationStep(): Map[Parameter[_], Seq[Double]]

    Attributes
    protected

  11. def doKill(): Unit

    Attributes
    protected
    Definition Classes
    ExpectationMaximization → Algorithm

  12. def doMaximizationStep(parameterMapping: Map[Parameter[_], Seq[Double]]): Unit

    Attributes
    protected

  13. def doResume(): Unit

    Attributes
    protected
    Definition Classes
    ExpectationMaximization → Algorithm

  14. def doStart(): Unit

    Attributes
    protected
    Definition Classes
    ExpectationMaximization → Algorithm

  15. def doStop(): Unit

    Attributes
    protected
    Definition Classes
    ExpectationMaximization → Algorithm

  16. def em(): Unit

    Attributes
    protected

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

    Definition Classes
    AnyRef

  18. def equals(arg0: Any): Boolean

    Definition Classes
    AnyRef → Any

  19. def finalize(): Unit

    Attributes
    protected[java.lang]
    Definition Classes
    AnyRef
    Annotations
    @throws( classOf[java.lang.Throwable] )

  20. final def getClass(): Class[_]

    Definition Classes
    AnyRef → Any

  21. def hashCode(): Int

    Definition Classes
    AnyRef → Any

  22. 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

  23. def isActive: Boolean

    Definition Classes
    Algorithm

  24. final def isInstanceOf[T0]: Boolean

    Definition Classes
    Any

  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. final def ne(arg0: AnyRef): Boolean

    Definition Classes
    AnyRef

  27. final def notify(): Unit

    Definition Classes
    AnyRef

  28. final def notifyAll(): Unit

    Definition Classes
    AnyRef

  29. val numberOfIterations: Int

  30. val paramMap: Map[Parameter[_], Seq[Double]]

    Attributes
    protected

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

    Definition Classes
    AnyRef

  35. def toString(): String

    Definition Classes
    AnyRef → Any

  36. final def wait(): Unit

    Definition Classes
    AnyRef
    Annotations
    @throws( ... )

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

    Definition Classes
    AnyRef
    Annotations
    @throws( ... )

  38. 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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