org.apache.lucene.search.similarities

## Class DFRSimilarity

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

-     public class DFRSimilarity
      extends SimilarityBase

  Implements the divergence from randomness (DFR) framework introduced in Gianni Amati and Cornelis Joost Van Rijsbergen. 2002. Probabilistic models of information retrieval based on measuring the divergence from randomness. ACM Trans. Inf. Syst. 20, 4 (October 2002), 357-389.

  The DFR scoring formula is composed of three separate components: the basic model, the aftereffect and an additional normalization component, represented by the classes `BasicModel`, `AfterEffect` and `Normalization`, respectively. The names of these classes were chosen to match the names of their counterparts in the Terrier IR engine.

  To construct a DFRSimilarity, you must specify the implementations for all three components of DFR:

  1. `BasicModel`: Basic model of information content:
     - `BasicModelBE`: Limiting form of Bose-Einstein
     - `BasicModelG`: Geometric approximation of Bose-Einstein
     - `BasicModelP`: Poisson approximation of the Binomial
     - `BasicModelD`: Divergence approximation of the Binomial
     - `BasicModelIn`: Inverse document frequency
     - `BasicModelIne`: Inverse expected document frequency [mixture of Poisson and IDF]
     - `BasicModelIF`: Inverse term frequency [approximation of I(ne)]
  2. `AfterEffect`: First normalization of information gain:
     - `AfterEffectL`: Laplace's law of succession
     - `AfterEffectB`: Ratio of two Bernoulli processes
     - `AfterEffect.NoAfterEffect`: no first normalization
  3. `Normalization`: Second (length) normalization:
     - `NormalizationH1`: Uniform distribution of term frequency
     - `NormalizationH2`: term frequency density inversely related to length
     - `NormalizationH3`: term frequency normalization provided by Dirichlet prior
     - `NormalizationZ`: term frequency normalization provided by a Zipfian relation
     - `Normalization.NoNormalization`: no second normalization

  Note that qtf, the multiplicity of term-occurrence in the query, is not handled by this implementation.

  See Also:
    `BasicModel`, `AfterEffect`, `Normalization`
  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, Similarity.SimWeight`

  - ### Field Summary

    Fields
    Modifier and Type | Field and Description
    --- | ---
    `protected AfterEffect` | `afterEffect` The first normalization of the information content.
    `protected BasicModel` | `basicModel` The basic model for information content.
    `protected Normalization` | `normalization` The term frequency normalization.

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

      `discountOverlaps`

  - ### Constructor Summary

    Constructors
    Constructor and Description
    `DFRSimilarity(BasicModel basicModel, AfterEffect afterEffect, Normalization normalization)` Creates DFRSimilarity from the three components.

  - ### Method Summary

    All Methods Instance Methods Concrete Methods
    Modifier and Type | Method and Description
    --- | ---
    `protected void` | `explain(List<Explanation> subs, BasicStats stats, int doc, float freq, float docLen)` Subclasses should implement this method to explain the score.
    `AfterEffect` | `getAfterEffect()` Returns the first normalization
    `BasicModel` | `getBasicModel()` Returns the basic model of information content
    `Normalization` | `getNormalization()` Returns the second normalization
    `protected float` | `score(BasicStats stats, float freq, float 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, computeWeight, explain, fillBasicStats, getDiscountOverlaps, log2, newStats, setDiscountOverlaps, simScorer`

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

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

- - ### Field Detail

    - #### basicModel

          protected final BasicModel basicModel

      The basic model for information content.

    - #### afterEffect

          protected final AfterEffect afterEffect

      The first normalization of the information content.

    - #### normalization

          protected final Normalization normalization

      The term frequency normalization.

  - ### Constructor Detail

    - #### DFRSimilarity

          public DFRSimilarity(BasicModel basicModel,
                               AfterEffect afterEffect,
                               Normalization normalization)

      Creates DFRSimilarity from the three components.

      Note that `null` values are not allowed: if you want no normalization or after-effect, instead pass `Normalization.NoNormalization` or `AfterEffect.NoAfterEffect` respectively.

      Parameters:
        `basicModel` - Basic model of information content
        `afterEffect` - First normalization of information gain
        `normalization` - Second (length) normalization

  - ### Method Detail

    - #### score

          protected float score(BasicStats stats,
                                float freq,
                                float 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.

    - #### explain

          protected void explain(List<Explanation> subs,
                                 BasicStats stats,
                                 int doc,
                                 float freq,
                                 float docLen)

      Description copied from class: `SimilarityBase`
      Subclasses should implement this method to explain the score. `expl` already contains the score, the name of the class and the doc id, as well as the term frequency and its explanation; subclasses can add additional clauses to explain details of their scoring formulae.

      The default implementation does nothing.

      Overrides:
        `explain` in class `SimilarityBase`
      Parameters:
        `subs` - the list of details of the explanation to extend
        `stats` - the corpus level statistics.
        `doc` - the document id.
        `freq` - the term frequency.
        `docLen` - the document length.

    - #### 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`

    - #### getBasicModel

          public BasicModel getBasicModel()

      Returns the basic model of information content

    - #### getAfterEffect

          public AfterEffect getAfterEffect()

      Returns the first normalization

    - #### getNormalization

          public Normalization getNormalization()

      Returns the second normalization

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