Class/Object

org.apache.spark.mllib.feature

HashingTF

Related Docs: object HashingTF | package feature

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class HashingTF extends Serializable

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

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@Since( "1.1.0" )
Source
HashingTF.scala
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Instance Constructors

  1. new HashingTF()

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    @Since( "1.1.0" )
  2. new HashingTF(numFeatures: Int)

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    numFeatures

    number of features (default: 220)

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

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  10. def hashCode(): Int

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  11. def indexOf(term: Any): Int

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    Returns the index of the input term.

    Returns the index of the input term.

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    @Since( "1.1.0" )
  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. val numFeatures: Int

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    number of features (default: 220)

  17. def setBinary(value: Boolean): HashingTF.this.type

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    If true, term frequency vector will be binary such that non-zero term counts will be set to 1 (default: false)

    If true, term frequency vector will be binary such that non-zero term counts will be set to 1 (default: false)

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    @Since( "2.0.0" )
  18. def setHashAlgorithm(value: String): HashingTF.this.type

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    Set the hash algorithm used when mapping term to integer.

    Set the hash algorithm used when mapping term to integer. (default: murmur3)

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    @Since( "2.0.0" )
  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]

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

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

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    @Since( "1.1.0" )
  22. def transform[D <: Iterable[_]](dataset: RDD[D]): RDD[Vector]

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    Transforms the input document to term frequency vectors.

    Transforms the input document to term frequency vectors.

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    @Since( "1.1.0" )
  23. def transform(document: Iterable[_]): Vector

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

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

    Annotations
    @Since( "1.1.0" )
  24. def transform(document: Iterable[_]): Vector

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    Transforms the input document into a sparse term frequency vector.

    Transforms the input document into a sparse term frequency vector.

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    @Since( "1.1.0" )
  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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