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 * Licensed to the Apache Software Foundation (ASF) under one or more
 * contributor license agreements.  See the NOTICE file distributed with
 * this work for additional information regarding copyright ownership.
 * The ASF licenses this file to You under the Apache License, Version 2.0
 * (the "License"); you may not use this file except in compliance with
 * the License.  You may obtain a copy of the License at
 *
 *     http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 */
package org.apache.lucene.search.similarities;


import org.apache.lucene.search.Explanation;

import static org.apache.lucene.search.similarities.SimilarityBase.log2;

Geometric as limiting form of the Bose-Einstein model. The formula used in Lucene differs slightly from the one in the original paper: F is increased by 1 and N is increased by F. 
@lucene.experimental
/**
 * Geometric as limiting form of the Bose-Einstein model.  The formula used in Lucene differs
 * slightly from the one in the original paper: {@code F} is increased by {@code 1}
 * and {@code N} is increased by {@code F}.
 * @lucene.experimental
 */
public class BasicModelG extends BasicModel {
  
  
Sole constructor: parameter-free /** Sole constructor: parameter-free */
  public BasicModelG() {}

  @Override
  public final double score(BasicStats stats, double tfn, double aeTimes1pTfn) {
    // just like in BE, approximation only holds true when F << N, so we use lambda = F / (N + F)
    double F = stats.getTotalTermFreq() + 1;
    double N = stats.getNumberOfDocuments();
    double lambda = F / (N + F);
    // -log(1 / (lambda + 1)) -> log(lambda + 1)
    double A = log2(lambda + 1);
    double B = log2((1 + lambda) / lambda);
    
    // basic model G should return (A + B * tfn)
    // which we rewrite to B * (1 + tfn) - (B - A)
    // so that it can be combined with the after effect while still guaranteeing
    // that the result is non-decreasing with tfn since B >= A
    
    return (B - (B - A) / (1 + tfn)) * aeTimes1pTfn;
  }

  @Override
  public Explanation explain(BasicStats stats, double tfn, double aeTimes1pTfn) {
    double F = stats.getTotalTermFreq() + 1;
    double N = stats.getNumberOfDocuments();
    double lambda = F / (N + F);
    Explanation explLambda = Explanation.match((float) lambda,
        "lambda, computed as F / (N + F) from:",
        Explanation.match((float) F,
            "F, total number of occurrences of term across all docs + 1"),
        Explanation.match((float) N,
            "N, total number of documents with field"));

    return Explanation.match(
        (float) (score(stats, tfn, aeTimes1pTfn) * (1 + tfn) / aeTimes1pTfn),
        getClass().getSimpleName() + ", computed as " +
            "log2(lambda + 1) + tfn * log2((1 + lambda) / lambda) from:",
        Explanation.match((float) tfn, "tfn, normalized term frequency"),
        explLambda);
  }

  @Override
  public String toString() {
    return "G";
  }
}