org.apache.lucene.search.similarities

## Class IBSimilarity

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

-     public class IBSimilarity
      extends SimilarityBase

  Provides a framework for the family of information-based models, as described in Stéphane Clinchant and Eric Gaussier. 2010. Information-based models for ad hoc IR. In Proceeding of the 33rd international ACM SIGIR conference on Research and development in information retrieval (SIGIR '10). ACM, New York, NY, USA, 234-241.

  The retrieval function is of the form RSV(q, d) = ∑ -xqw log Prob(Xw ≥ tdw | λw), where

  - xqw is the query boost;
  - Xw is a random variable that counts the occurrences of word w;
  - tdw is the normalized term frequency;
  - λw is a parameter.

  The framework described in the paper has many similarities to the DFR framework (see `DFRSimilarity`). It is possible that the two Similarities will be merged at one point.

  To construct an IBSimilarity, you must specify the implementations for all three components of the Information-Based model.

  1. `Distribution`: Probabilistic distribution used to model term occurrence
     - `DistributionLL`: Log-logistic
     - `DistributionLL`: Smoothed power-law
  2. `Lambda`: λw parameter of the probability distribution
     - `LambdaDF`: `Nw/N` or average number of documents where w occurs
     - `LambdaTTF`: `Fw/N` or average number of occurrences of w in the collection
  3. `Normalization`: Term frequency normalization

     > Any supported DFR normalization (listed in `DFRSimilarity`)

  See Also:
    `DFRSimilarity`
  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
    Modifier and Type | Field and Description
    --- | ---
    `protected Distribution` | `distribution` The probabilistic distribution used to model term occurrence.
    `protected Lambda` | `lambda` The lambda (λw) parameter.
    `protected Normalization` | `normalization` The term frequency normalization.

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

      `discountOverlaps`

  - ### Constructor Summary

    Constructors
    Constructor and Description
    `IBSimilarity(Distribution distribution, Lambda lambda, Normalization normalization)` Creates IBSimilarity from the three components.

  - ### 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.
    `protected void` | `explain(List<Explanation> subs, BasicStats stats, double freq, double docLen)` Subclasses should implement this method to explain the score.
    `Distribution` | `getDistribution()` Returns the distribution
    `Lambda` | `getLambda()` Returns the distribution's lambda parameter
    `Normalization` | `getNormalization()` Returns the term frequency normalization
    `protected double` | `score(BasicStats stats, double freq, double docLen)` Scores the document `doc`.
    `String` | `toString()` The name of IB methods follow the pattern `IB <distribution> <lambda><normalization>`.

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

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

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

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

- - ### Field Detail

    - #### distribution

          protected final Distribution distribution

      The probabilistic distribution used to model term occurrence.

    - #### lambda

          protected final Lambda lambda

      The lambda (λw) parameter.

    - #### normalization

          protected final Normalization normalization

      The term frequency normalization.

  - ### Constructor Detail

    - #### IBSimilarity

          public IBSimilarity(Distribution distribution,
                              Lambda lambda,
                              Normalization normalization)

      Creates IBSimilarity from the three components.

      Note that `null` values are not allowed: if you want no normalization, instead pass `Normalization.NoNormalization`.

      Parameters:
        `distribution` - probabilistic distribution modeling term occurrence
        `lambda` - distribution's λw parameter
        `normalization` - term frequency normalization

  - ### 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.

    - #### explain

          protected void explain(List<Explanation> subs,
                                 BasicStats stats,
                                 double freq,
                                 double 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.
        `freq` - the term frequency.
        `docLen` - the document length.

    - #### 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()

      The name of IB methods follow the pattern `IB <distribution> <lambda><normalization>`. The name of the distribution is the same as in the original paper; for the names of lambda parameters, refer to the javadoc of the `Lambda` classes.
      Specified by:
        `toString` in class `SimilarityBase`

    - #### getDistribution

          public Distribution getDistribution()

      Returns the distribution

    - #### getLambda

          public Lambda getLambda()

      Returns the distribution's lambda parameter

    - #### getNormalization

          public Normalization getNormalization()

      Returns the term frequency normalization

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