case class ClassificationInference(featureIndex: Index[Feature], weights: DenseVector[Double]) extends Inference[SentenceDecisionInstance] with Product with Serializable
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type
Marginal = MLSentenceSegmenter.Marginal
- Definition Classes
- ClassificationInference → Inference
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type
Scorer = ClassificationInference
- Definition Classes
- ClassificationInference → Inference
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final
def
!=(arg0: Any): Boolean
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final
def
##(): Int
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final
def
==(arg0: Any): Boolean
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final
def
asInstanceOf[T0]: T0
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- def classify(features: Array[Feature]): Boolean
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def
clone(): AnyRef
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final
def
eq(arg0: AnyRef): Boolean
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- val featureIndex: Index[Feature]
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def
finalize(): Unit
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def
forTesting: Inference[SentenceDecisionInstance]
- Definition Classes
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final
def
getClass(): Class[_]
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def
goldMarginal(scorer: Scorer, v: SentenceDecisionInstance): 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.
- v
the example
- returns
gold marginal
- Definition Classes
- ClassificationInference → Inference
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final
def
isInstanceOf[T0]: Boolean
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def
marginal(scorer: Scorer, v: SentenceDecisionInstance): 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
- ClassificationInference → Inference
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def
marginal(v: SentenceDecisionInstance): Marginal
- Definition Classes
- Inference
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final
def
ne(arg0: AnyRef): Boolean
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final
def
notify(): Unit
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final
def
notifyAll(): Unit
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def
scorer(v: SentenceDecisionInstance): Scorer
- Definition Classes
- ClassificationInference → Inference
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final
def
synchronized[T0](arg0: ⇒ T0): T0
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final
def
wait(): Unit
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final
def
wait(arg0: Long, arg1: Int): Unit
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final
def
wait(arg0: Long): Unit
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- val weights: DenseVector[Double]