cc.factorie.directed

EMInferencer

class EMInferencer[V <: variable.Var, W <: DiscreteVar, M <: model.Model] extends AnyRef

The expectation-maximization method of inference. maximizing is the collection of variables that will be maximized. meanField contains the variables for which to get expectations. You can provide your own Maximizer; other wise an instance of the default MaximizeSuite is provided.

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Instance Constructors

  1. new EMInferencer(maximizing: Iterable[V], marginalizing: Iterable[W], model: M, expecter: Infer[Iterable[W], M], maximizer: Infer[Iterable[V], M])

Value Members

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

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

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  3. final def ##(): Int

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

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

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  6. final def asInstanceOf[T0]: T0

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  7. def clone(): AnyRef

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  8. def eStep(): Unit

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

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  10. def equals(arg0: Any): Boolean

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  11. val expecter: Infer[Iterable[W], M]

  12. def finalize(): Unit

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

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  14. def hashCode(): Int

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  15. final def isInstanceOf[T0]: Boolean

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  16. def mStep(): Unit

  17. val marginalizing: Iterable[W]

  18. val maximizer: Infer[Iterable[V], M]

  19. val maximizing: Iterable[V]

  20. val model: M

  21. final def ne(arg0: AnyRef): Boolean

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  22. final def notify(): Unit

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  23. final def notifyAll(): Unit

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  24. def process(): Unit

  25. def process(iterations: Int): Unit

  26. var summary: Summary

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

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  28. def toString(): String

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  29. final def wait(): Unit

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

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

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