Class

com.johnsnowlabs.nlp.annotators.pos.perceptron

AveragedPerceptron

Related Doc: package perceptron

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class AveragedPerceptron extends WritableAnnotatorComponent

Specific model for PerceptronApproach

Linear Supertypes
WritableAnnotatorComponent, Serializable, Serializable, AnyRef, Any
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Instance Constructors

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

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    tags

    Holds all unique tags based on training

    taggedWordBook

    Contains non ambiguous words and their tags

    featuresWeight

    Contains prediction information based on context frequencies

    lastIteration

    Contains information on how many iterations have run for weighting

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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    Attributes
    protected[java.lang]
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    AnyRef
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    @throws( ... )

  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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    Attributes
    protected[java.lang]
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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]]

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  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: List[(String, Int)]): String

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  17. def serialize: SerializedAnnotatorComponent[AveragedPerceptron]

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    serializes this approach to be writable into disk

    serializes this approach to be writable into disk

    Definition Classes
    AveragedPerceptron → WritableAnnotatorComponent

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

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  19. def toString(): String

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  20. def update(truth: String, guess: String, features: Map[String, Int]): Unit

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    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

  21. final def wait(): Unit

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    @throws( ... )

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

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    @throws( ... )

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

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    @throws( ... )

Inherited from WritableAnnotatorComponent

Inherited from Serializable

Inherited from Serializable

Inherited from AnyRef

Inherited from Any

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