trait AugmentableInference[Datum, Augment] extends Inference[Datum]
AugmentableInference is an epic.framework.Inference that can support injecting additional information into the structure computation. This can include prior information over the structure (useful for EP or other Bayesian inference) or loss-augmentation.
- Datum
the kind of thing to do inference on
- Augment
the extra piece of information we can use to do inference
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abstract
type
Marginal <: framework.Marginal
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abstract
type
Scorer
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Abstract Value Members
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abstract
def
baseAugment(v: Datum): Augment
The "no prior information" augment.
The "no prior information" augment. Used if nothing is passed in.
- abstract def goldMarginal(scorer: Scorer, v: Datum, aug: Augment): Marginal
- abstract def marginal(scorer: Scorer, v: Datum, aug: Augment): Marginal
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abstract
def
scorer(v: Datum): Scorer
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def
!=(arg0: Any): Boolean
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finalize(): Unit
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def
forTesting: AugmentableInference[Datum, Augment]
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final
def
getClass(): Class[_]
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def
goldMarginal(scorer: Scorer, v: Datum): Marginal
Produces the "gold marginal" which is the marginal conditioned on the output label/structure itself.
Produces the "gold marginal" which is the marginal conditioned on the output label/structure itself.
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the example
- returns
gold marginal
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hashCode(): Int
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final
def
isInstanceOf[T0]: Boolean
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def
marginal(scorer: Scorer, v: Datum): Marginal
Produces the "guess marginal" which is the marginal conditioned on only the input data
Produces the "guess marginal" which is the marginal conditioned on only the input data
- v
the example
- returns
gold marginal
- Definition Classes
- AugmentableInference → Inference
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def
marginal(v: Datum): Marginal
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final
def
ne(arg0: AnyRef): Boolean
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def
notify(): Unit
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