Class

com.johnsnowlabs.nlp.annotators.parser.dep

Perceptron

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class Perceptron extends AnyRef

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Instance Constructors

  1. new Perceptron(numberOfClasses: Int)

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Type Members

  1. type ClassToWeightLearner = Map[ClassNum, WeightLearner]

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  2. type ClassVector = Vector[Score]

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  3. type TimeStamp = Int

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  4. case class WeightLearner(current: Int, total: Int, ts: TimeStamp) extends Product with Serializable

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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 average(w: WeightLearner): Double

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  6. def cleanLearning(): Unit

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  7. def clone(): AnyRef

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  8. def current(w: WeightLearner): Double

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  9. def dotProductScore(features: Map[Feature, Score], scoreMethod: (WeightLearner) ⇒ Double): ClassVector

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

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  11. def equals(arg0: Any): Boolean

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  12. def finalize(): Unit

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

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  14. def hashCode(): Int

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  15. final def isInstanceOf[T0]: Boolean

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  16. var learning: Map[String, Map[String, ClassToWeightLearner]]

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  17. def load(lines: Iterator[String]): Unit

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

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  19. final def notify(): Unit

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  20. final def notifyAll(): Unit

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  21. var numberOfClasses: Int

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  22. def predict(classNumVector: ClassVector): ClassNum

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  23. def score(features: Map[Feature, Score], scoreMethod: (WeightLearner) ⇒ Double): ClassVector

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  24. var seen: TimeStamp

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

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  26. def toString(): String

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  27. def update(truth: ClassNum, guess: ClassNum, features: Iterable[Feature]): Unit

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  28. final def wait(): Unit

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

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

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