org.allenai.nlpstack.parse.poly.fsm

FSMTrainingVector

case class FSMTrainingVector(task: ClassificationTask, transition: StateTransition, feature: StateFeature, state: State) extends Product with Serializable

A TrainingVector is a triple of the form (task, featureVector, transition), where task is the ClassificationTask associated with the feature vector (featureVector), and transition is the correct classification of the feature vector.

These labeled feature vectors are used to train classifiers.

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Instance Constructors

  1. new FSMTrainingVector(task: ClassificationTask, transition: StateTransition, feature: StateFeature, state: State)

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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  7. def clone(): AnyRef

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

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  9. val feature: StateFeature

  10. lazy val featureVector: FeatureVector

  11. def finalize(): Unit

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

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  13. final def isInstanceOf[T0]: Boolean

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

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  15. final def notify(): Unit

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  16. final def notifyAll(): Unit

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  17. val state: State

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

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  19. val task: ClassificationTask

  20. val transition: StateTransition

  21. final def wait(): Unit

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

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

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