com.cra.figaro.algorithm.learning

ExpectationMaximizationWithFactors

class ExpectationMaximizationWithFactors extends ExpectationMaximization

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.

Linear Supertypes
ExpectationMaximization, ParameterLearner, Algorithm, AnyRef, Any
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Inherited
  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)

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. var 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

    Definition Classes
    ExpectationMaximization

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

    Attributes
    protected
    Definition Classes
    ExpectationMaximizationWithFactors → ExpectationMaximization

  10. def doKill(): Unit

    Attributes
    protected
    Definition Classes
    ExpectationMaximization → Algorithm

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

    Attributes
    protected
    Definition Classes
    ExpectationMaximization

  12. def doResume(): Unit

    Attributes
    protected
    Definition Classes
    ExpectationMaximization → Algorithm

  13. def doStart(): Unit

    Attributes
    protected
    Definition Classes
    ExpectationMaximization → Algorithm

  14. def doStop(): Unit

    Attributes
    protected
    Definition Classes
    ExpectationMaximization → Algorithm

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

    Attributes
    protected

  30. 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

  31. 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

  32. 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

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

    Definition Classes
    ExpectationMaximization

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

    Definition Classes
    AnyRef

  35. val terminationCriteria: () ⇒ EMTerminationCriteria

    Definition Classes
    ExpectationMaximization

  36. def toString(): String

    Definition Classes
    AnyRef → Any

  37. final def wait(): Unit

    Definition Classes
    AnyRef
    Annotations
    @throws( ... )

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

    Definition Classes
    AnyRef
    Annotations
    @throws( ... )

  39. final def wait(arg0: Long): Unit

    Definition Classes
    AnyRef
    Annotations
    @throws( ... )

Inherited from ExpectationMaximization

Inherited from ParameterLearner

Inherited from Algorithm

Inherited from AnyRef

Inherited from Any

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