org.apache.lucene.search.similarities

## Class LMDirichletSimilarity

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

-     public class LMDirichletSimilarity
      extends LMSimilarity

  Bayesian smoothing using Dirichlet priors. From Chengxiang Zhai and John Lafferty. 2001. A study of smoothing methods for language models applied to Ad Hoc information retrieval. In Proceedings of the 24th annual international ACM SIGIR conference on Research and development in information retrieval (SIGIR '01). ACM, New York, NY, USA, 334-342.

  The formula as defined the paper assigns a negative score to documents that contain the term, but with fewer occurrences than predicted by the collection language model. The Lucene implementation returns `0` for such documents.

  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.LMSimilarity

      `LMSimilarity.CollectionModel, LMSimilarity.DefaultCollectionModel, LMSimilarity.LMStats`

    - ### 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.LMSimilarity

      `collectionModel`

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

      `discountOverlaps`

  - ### Constructor Summary

    Constructors
    Constructor and Description
    `LMDirichletSimilarity()` Instantiates the similarity with the default μ value of 2000.
    `LMDirichletSimilarity(float mu)` Instantiates the similarity with the provided μ parameter.
    `LMDirichletSimilarity(LMSimilarity.CollectionModel collectionModel)` Instantiates the similarity with the default μ value of 2000.
    `LMDirichletSimilarity(LMSimilarity.CollectionModel collectionModel, float mu)` Instantiates the similarity with the provided μ parameter.

  - ### 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.
    `float` | `getMu()` Returns the μ parameter.
    `String` | `getName()` Returns the name of the LM method.
    `protected double` | `score(BasicStats stats, double freq, double docLen)` Scores the document `doc`.

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

      `fillBasicStats, newStats, toString`

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

      `computeNorm, getDiscountOverlaps, log2, scorer, setDiscountOverlaps`

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

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

- - ### Constructor Detail

    - #### LMDirichletSimilarity

          public LMDirichletSimilarity(LMSimilarity.CollectionModel collectionModel,
                                       float mu)

      Instantiates the similarity with the provided μ parameter.

    - #### LMDirichletSimilarity

          public LMDirichletSimilarity(float mu)

      Instantiates the similarity with the provided μ parameter.

    - #### LMDirichletSimilarity

          public LMDirichletSimilarity(LMSimilarity.CollectionModel collectionModel)

      Instantiates the similarity with the default μ value of 2000.

    - #### LMDirichletSimilarity

          public LMDirichletSimilarity()

      Instantiates the similarity with the default μ value of 2000.

  - ### 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 `LMSimilarity`
      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.

    - #### getMu

          public float getMu()

      Returns the μ parameter.

    - #### getName

          public String getName()

      Description copied from class: `LMSimilarity`
      Returns the name of the LM method. The values of the parameters should be included as well.

      Used in `LMSimilarity.toString()`

      .
      Specified by:
        `getName` in class `LMSimilarity`

Copyright © 2000-2020 Apache Software Foundation. All Rights Reserved.