Class

org.apache.spark.mllib.feature

IDF

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class IDF extends AnyRef

Inverse document frequency (IDF). The standard formulation is used: idf = log((m + 1) / (d(t) + 1)), where m is the total number of documents and d(t) is the number of documents that contain term t.

This implementation supports filtering out terms which do not appear in a minimum number of documents (controlled by the variable minDocFreq). For terms that are not in at least minDocFreq documents, the IDF is found as 0, resulting in TF-IDFs of 0.

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

  1. new IDF()

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    @Since( "1.1.0" )

  2. new IDF(minDocFreq: Int)

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    minDocFreq

    minimum of documents in which a term should appear for filtering

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    @Since( "1.2.0" )

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

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  2. final def ##(): Int

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  9. def fit(dataset: JavaRDD[Vector]): IDFModel

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    Computes the inverse document frequency.

    Computes the inverse document frequency.

    dataset

    a JavaRDD of term frequency vectors

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    @Since( "1.1.0" )

  10. def fit(dataset: RDD[Vector]): IDFModel

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    Computes the inverse document frequency.

    Computes the inverse document frequency.

    dataset

    an RDD of term frequency vectors

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    @Since( "1.1.0" )

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

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  13. final def isInstanceOf[T0]: Boolean

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  14. val minDocFreq: Int

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    minimum of documents in which a term should appear for filtering

    minimum of documents in which a term should appear for filtering

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    @Since( "1.2.0" )

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