org.apache.spark.mllib.feature

IDF

class IDF extends AnyRef

:: Experimental :: 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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Instance Constructors

  1. new IDF()

  2. new IDF(minDocFreq: Int)

    minDocFreq

    minimum of documents in which a term should appear for filtering

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

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

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

    Computes the inverse document frequency.

    Computes the inverse document frequency.

    dataset

    a JavaRDD of term frequency vectors

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

    Computes the inverse document frequency.

    Computes the inverse document frequency.

    dataset

    an RDD of term frequency vectors

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

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  14. def hashCode(): Int

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  15. final def isInstanceOf[T0]: Boolean

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  16. val minDocFreq: Int

    minimum of documents in which a term should appear for filtering

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

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