https://lucene.apache.org/lucene-parent/lucene-core: Apache Lucene Java Core (The Apache Software Foundation) 
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package org.apache.lucene.search.similarities;


import java.util.ArrayList;
import java.util.List;

import org.apache.lucene.search.Explanation;
import org.apache.lucene.search.similarities.Normalization.NoNormalization;


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 
    
    2. 
    
  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. 
    
  3. Normalization: Term frequency normalization 
    Any supported DFR normalization (listed in DFRSimilarity)
    
    
    4. 



See Also:
@lucene.experimental
/**
 * 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.
 * <p>The retrieval function is of the form <em>RSV(q, d) = &sum;
 * -x<sup>q</sup><sub>w</sub> log Prob(X<sub>w</sub> &ge;
 * t<sup>d</sup><sub>w</sub> | &lambda;<sub>w</sub>)</em>, where
 * <ul>
 *   <li><em>x<sup>q</sup><sub>w</sub></em> is the query boost;</li>
 *   <li><em>X<sub>w</sub></em> is a random variable that counts the occurrences
 *   of word <em>w</em>;</li>
 *   <li><em>t<sup>d</sup><sub>w</sub></em> is the normalized term frequency;</li>
 *   <li><em>&lambda;<sub>w</sub></em> is a parameter.</li>
 * </ul>
 * <p>The framework described in the paper has many similarities to the DFR
 * framework (see {@link DFRSimilarity}). It is possible that the two
 * Similarities will be merged at one point.</p>
 * <p>To construct an IBSimilarity, you must specify the implementations for 
 * all three components of the Information-Based model.
 * <ol>
 *     <li>{@link Distribution}: Probabilistic distribution used to
 *         model term occurrence
 *         <ul>
 *             <li>{@link DistributionLL}: Log-logistic</li>
 *             <li>{@link DistributionLL}: Smoothed power-law</li>
 *         </ul>
 *     </li>
 *     <li>{@link Lambda}: &lambda;<sub>w</sub> parameter of the
 *         probability distribution
 *         <ul>
 *             <li>{@link LambdaDF}: <code>N<sub>w</sub>/N</code> or average
 *                 number of documents where w occurs</li>
 *             <li>{@link LambdaTTF}: <code>F<sub>w</sub>/N</code> or
 *                 average number of occurrences of w in the collection</li>
 *         </ul>
 *     </li>
 *     <li>{@link Normalization}: Term frequency normalization 
 *         <blockquote>Any supported DFR normalization (listed in
 *                      {@link DFRSimilarity})</blockquote>
 *     </li>
 * </ol>
 * @see DFRSimilarity
 * @lucene.experimental 
 */
public class IBSimilarity extends SimilarityBase {
  
The probabilistic distribution used to model term occurrence. 
/** The probabilistic distribution used to model term occurrence. */
  protected final Distribution distribution;
  
The lambda (λw) parameter. 
/** The <em>lambda (&lambda;<sub>w</sub>)</em> parameter. */
  protected final Lambda lambda;
  
The term frequency normalization. 
/** The term frequency normalization. */
  protected final Normalization normalization;
  
  
Creates IBSimilarity from the three components.


Note that null values are not allowed: if you want no normalization, instead pass NoNormalization. 
Params:
  • distribution – probabilistic distribution modeling term occurrence
  • lambda – distribution's λw parameter
  • normalization – term frequency normalization
/**
   * Creates IBSimilarity from the three components.
   * <p>
   * Note that <code>null</code> values are not allowed:
   * if you want no normalization, instead pass 
   * {@link NoNormalization}.
   * @param distribution probabilistic distribution modeling term occurrence
   * @param lambda distribution's &lambda;<sub>w</sub> parameter
   * @param normalization term frequency normalization
   */
  public IBSimilarity(Distribution distribution,
                      Lambda lambda,
                      Normalization normalization) {
    this.distribution = distribution;
    this.lambda = lambda;
    this.normalization = normalization;
  }
  
  @Override
  protected double score(BasicStats stats, double freq, double docLen) {
    return stats.getBoost() *
        distribution.score(
            stats,
            normalization.tfn(stats, freq, docLen),
            lambda.lambda(stats));
  }

  @Override
  protected void explain(
      List<Explanation> subs, BasicStats stats, double freq, double docLen) {
    if (stats.getBoost() != 1.0d) {
      subs.add(Explanation.match((float)stats.getBoost(), "boost, query boost"));
    }
    Explanation normExpl = normalization.explain(stats, freq, docLen);
    Explanation lambdaExpl = lambda.explain(stats);
    subs.add(normExpl);
    subs.add(lambdaExpl);
    subs.add(distribution.explain(stats, normExpl.getValue().floatValue(), lambdaExpl.getValue().floatValue()));
  }

  @Override
  protected Explanation explain(
      BasicStats stats, Explanation freq, double docLen) {
    List<Explanation> subs = new ArrayList<>();
    explain(subs, stats, freq.getValue().doubleValue(), docLen);

    return Explanation.match(
        (float) score(stats, freq.getValue().doubleValue(), docLen),
        "score(" + getClass().getSimpleName() + ", freq=" +
            freq.getValue() +"), computed as boost * " +
            "distribution.score(stats, normalization.tfn(stats, freq," +
            " docLen), lambda.lambda(stats)) from:",
        subs);
  }

  
  
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. 
/**
   * The name of IB methods follow the pattern
   * {@code 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 {@link Lambda} classes.
   */
  @Override
  public String toString() {
    return "IB " + distribution.toString() + "-" + lambda.toString()
                 + normalization.toString();
  }
  
  
Returns the distribution

/**
   * Returns the distribution
   */
  public Distribution getDistribution() {
    return distribution;
  }
  
  
Returns the distribution's lambda parameter

/**
   * Returns the distribution's lambda parameter
   */
  public Lambda getLambda() {
    return lambda;
  }

  
Returns the term frequency normalization

/**
   * Returns the term frequency normalization
   */
  public Normalization getNormalization() {
    return normalization;
  }
}