ml.combust.mleap.core.feature

HashingTermFrequencyModel

case class HashingTermFrequencyModel(numFeatures: Int = 1.<<(18), binary: Boolean = false) extends Product with Serializable

Class for hashing token frequencies into a vector.

Source adapted from: Apache Spark Utils and HashingTF, see NOTICE for contributors

numFeatures

size of feature vector to hash into

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@SparkCode()
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Serializable, Serializable, Product, Equals, AnyRef, Any
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Instance Constructors

  1. new HashingTermFrequencyModel(numFeatures: Int = 1.<<(18), binary: Boolean = false)

    numFeatures

    size of feature vector to hash into

Value Members

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

    Definition Classes
    AnyRef

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

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  3. final def ##(): Int

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    AnyRef → Any

  4. final def ==(arg0: AnyRef): Boolean

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

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  6. def apply(document: Iterable[_]): Vector

  7. final def asInstanceOf[T0]: T0

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  8. val binary: Boolean

  9. def clone(): AnyRef

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    protected[java.lang]
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  10. final def eq(arg0: AnyRef): Boolean

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  11. def finalize(): Unit

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

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  13. def indexOf(term: Any): Int

  14. final def isInstanceOf[T0]: Boolean

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

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  16. def nonNegativeMod(x: Int, mod: Int): Int

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    @SparkCode()

  17. final def notify(): Unit

    Definition Classes
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  18. final def notifyAll(): Unit

    Definition Classes
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  19. val numFeatures: Int

    size of feature vector to hash into

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

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  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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