org.apache.spark.mllib.feature

HashingTF

class HashingTF extends Serializable

:: Experimental :: Maps a sequence of terms to their term frequencies using the hashing trick.

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@Experimental()
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Instance Constructors

  1. new HashingTF()

  2. new HashingTF(numFeatures: Int)

    numFeatures

    number of features (default: 220)

Value Members

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

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

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  3. final def ##(): Int

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

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

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  6. final def asInstanceOf[T0]: T0

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  7. def clone(): AnyRef

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

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  9. def equals(arg0: Any): Boolean

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  10. def finalize(): Unit

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

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  12. def hashCode(): Int

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  13. def indexOf(term: Any): Int

    Returns the index of the input term.

  14. final def isInstanceOf[T0]: Boolean

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

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  16. final def notify(): Unit

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  17. final def notifyAll(): Unit

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  18. val numFeatures: Int

    number of features (default: 220)

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

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  20. def toString(): String

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  21. def transform[D <: Iterable[_]](dataset: JavaRDD[D]): JavaRDD[Vector]

    Transforms the input document to term frequency vectors (Java version).

  22. def transform[D <: Iterable[_]](dataset: RDD[D]): RDD[Vector]

    Transforms the input document to term frequency vectors.

  23. def transform(document: Iterable[_]): Vector

    Transforms the input document into a sparse term frequency vector (Java version).

  24. def transform(document: Iterable[_]): Vector

    Transforms the input document into a sparse term frequency vector.

  25. final def wait(): Unit

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

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

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