org.allenai.nlpstack.parse.poly.fsm

FSMTrainingVector

Related Doc: package fsm

case class FSMTrainingVector(task: ClassificationTask, transition: StateTransition, transitionSystem: TransitionSystem, 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, transitionSystem: TransitionSystem, state: State)

Value Members

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

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  2. final def ##(): Int

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

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  4. final def asInstanceOf[T0]: T0

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  5. def clone(): AnyRef

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

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  7. lazy val featureVector: FeatureVector

  8. def finalize(): Unit

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

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  10. final def isInstanceOf[T0]: Boolean

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

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  12. final def notify(): Unit

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  13. final def notifyAll(): Unit

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  14. val state: State

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

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  16. val task: ClassificationTask

  17. def toString(): String

    Definition Classes
    FSMTrainingVector → AnyRef → Any

  18. val transition: StateTransition

  19. val transitionSystem: TransitionSystem

  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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