cc.factorie.optimize

SemiSupervisedLikelihoodExample

class SemiSupervisedLikelihoodExample[A <: Iterable[variable.Var], B <: model.Model] extends Example

Maximum likelihood in one semi supervised setting. It does constrained inference and maximizes the likelihood of it when compared with unconstrained inference. This can implement Generalized Expectation and other algorithms.

A

The type of the labels

B

The type of the model

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Instance Constructors

  1. new SemiSupervisedLikelihoodExample(labels: A, model: B, inferConstrained: Infer[A, B], inferUnconstrained: Infer[A, B])

    labels

    The labels

    model

    The model

    inferConstrained

    Inference with constraints

    inferUnconstrained

    Inference without constraints. Parameters will be learned to match these two inferences.

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. def accumulateValueAndGradient(value: DoubleAccumulator, gradient: WeightsMapAccumulator): Unit

    Put objective value and gradient into the accumulators.

    Put objective value and gradient into the accumulators. Either argument can be null if they don't need to be computed.

    value

    Accumulator to hold value

    gradient

    Accumulator to hold gradient

    Definition Classes
    SemiSupervisedLikelihoodExample → Example

  7. final def asInstanceOf[T0]: T0

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  8. def clone(): AnyRef

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

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  10. def equals(arg0: Any): Boolean

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  11. def finalize(): Unit

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

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  13. def hashCode(): Int

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  14. final def isInstanceOf[T0]: Boolean

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

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  16. final def notify(): Unit

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  17. final def notifyAll(): Unit

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

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  19. def toString(): String

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  20. final def wait(): Unit

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

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

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