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## 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.ExactSimScorer, Similarity.SimWeight, Similarity.SloppySimScorer`

Field Summary
`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
`IBSimilarity(Distribution distribution, Lambda lambda, Normalization normalization)` Creates IBSimilarity from the three components.

Method Summary
`protected void` | `explain(Explanation expl, BasicStats stats, int doc, float freq, float 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 float` | `score(BasicStats stats, float freq, float 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, computeWeight, decodeNormValue, encodeNormValue, exactSimScorer, explain, fillBasicStats, getDiscountOverlaps, log2, newStats, setDiscountOverlaps, sloppySimScorer`

Methods inherited from class org.apache.lucene.search.similarities.Similarity
`coord, queryNorm`

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 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(Explanation expl,
                           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:
    `expl` - the explanation to extend with details.
    `stats` - the corpus level statistics.
    `doc` - the document id.
    `freq` - the term frequency.
    `docLen` - the document length.

### 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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