org.apache.lucene.search.similarities

## Class DFISimilarity

- java.lang.Object
- - org.apache.lucene.search.similarities.Similarity
  - - org.apache.lucene.search.similarities.SimilarityBase
    - - org.apache.lucene.search.similarities.DFISimilarity

-     public class DFISimilarity
      extends SimilarityBase

  Implements the Divergence from Independence (DFI) model based on Chi-square statistics (i.e., standardized Chi-squared distance from independence in term frequency tf).

  DFI is both parameter-free and non-parametric:

  - parameter-free: it does not require any parameter tuning or training.
  - non-parametric: it does not make any assumptions about word frequency distributions on document collections.

  It is highly recommended not to remove stopwords (very common terms: the, of, and, to, a, in, for, is, on, that, etc) with this similarity.

  For more information see: A nonparametric term weighting method for information retrieval based on measuring the divergence from independence

  See Also:
    `IndependenceStandardized`, `IndependenceSaturated`, `IndependenceChiSquared`
  WARNING: This API is experimental and might change in incompatible ways in the next release.

- - ### Nested Class Summary

    - ### Nested classes/interfaces inherited from class org.apache.lucene.search.similarities.Similarity

      `Similarity.SimScorer`

  - ### Field Summary

    - ### Fields inherited from class org.apache.lucene.search.similarities.SimilarityBase

      `discountOverlaps`

  - ### Constructor Summary

    Constructors
    Constructor and Description
    `DFISimilarity(Independence independenceMeasure)` Create DFI with the specified divergence from independence measure

  - ### Method Summary

    All Methods Instance Methods Concrete Methods
    Modifier and Type | Method and Description
    --- | ---
    `protected Explanation` | `explain(BasicStats stats, Explanation freq, double docLen)` Explains the score.
    `Independence` | `getIndependence()` Returns the measure of independence
    `protected double` | `score(BasicStats stats, double freq, double docLen)` Scores the document `doc`.
    `String` | `toString()` Subclasses must override this method to return the name of the Similarity and preferably the values of parameters (if any) as well.

    - ### Methods inherited from class org.apache.lucene.search.similarities.SimilarityBase

      `computeNorm, explain, fillBasicStats, getDiscountOverlaps, log2, newStats, scorer, setDiscountOverlaps`

    - ### Methods inherited from class java.lang.Object

      `clone, equals, finalize, getClass, hashCode, notify, notifyAll, wait, wait, wait`

- - ### Constructor Detail

    - #### DFISimilarity

          public DFISimilarity(Independence independenceMeasure)

      Create DFI with the specified divergence from independence measure
      Parameters:
        `independenceMeasure` - measure of divergence from independence

  - ### Method Detail

    - #### score

          protected double score(BasicStats stats,
                                 double freq,
                                 double docLen)

      Description copied from class: `SimilarityBase`
      Scores the document `doc`.

      Subclasses must apply their scoring formula in this class.

      Specified by:
        `score` in class `SimilarityBase`
      Parameters:
        `stats` - the corpus level statistics.
        `freq` - the term frequency.
        `docLen` - the document length.
      Returns:
        the score.

    - #### getIndependence

          public Independence getIndependence()

      Returns the measure of independence

    - #### explain

          protected Explanation explain(BasicStats stats,
                                        Explanation freq,
                                        double docLen)

      Description copied from class: `SimilarityBase`
      Explains the score. The implementation here provides a basic explanation in the format score(name-of-similarity, doc=doc-id, freq=term-frequency), computed from:, and attaches the score (computed via the `SimilarityBase.score(BasicStats, double, double)` method) and the explanation for the term frequency. Subclasses content with this format may add additional details in `SimilarityBase.explain(List, BasicStats, double, double)`.
      Overrides:
        `explain` in class `SimilarityBase`
      Parameters:
        `stats` - the corpus level statistics.
        `freq` - the term frequency and its explanation.
        `docLen` - the document length.
      Returns:
        the explanation.

    - #### toString

          public String toString()

      Description copied from class: `SimilarityBase`
      Subclasses must override this method to return the name of the Similarity and preferably the values of parameters (if any) as well.
      Specified by:
        `toString` in class `SimilarityBase`

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