c

com.johnsnowlabs.nlp.annotators.pos.perceptron

TrainingPerceptronLegacy

class TrainingPerceptronLegacy extends Serializable

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  1. TrainingPerceptronLegacy
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Instance Constructors

  1. new TrainingPerceptronLegacy(tags: Array[String], taggedWordBook: Map[String, String], featuresWeight: Map[String, Map[String, Double]], lastIteration: Int = 0)

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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  6. final def eq(arg0: AnyRef): Boolean
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  7. def equals(arg0: Any): Boolean
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  8. def finalize(): Unit
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    @throws( classOf[java.lang.Throwable] )
  9. final def getClass(): Class[_]
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  10. def getWeights: Map[String, Map[String, Double]]

  11. def hashCode(): Int
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  12. final def isInstanceOf[T0]: Boolean
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  13. final def ne(arg0: AnyRef): Boolean
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  14. final def notify(): Unit
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  15. final def notifyAll(): Unit
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  16. def predict(features: Map[String, Int]): String
  17. final def synchronized[T0](arg0: ⇒ T0): T0
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  18. def toString(): String
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  19. def update(truth: String, guess: String, features: Map[String, Int]): Unit

    This is model learning tweaking during training, in-place Uses mutable collections since this runs per word, not per iteration Hence, performance is needed, without risk as long as this is a non parallel training running outside spark

  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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