## Uses of Class
org.apache.lucene.search.similarities.Similarity

Packages that use Similarity
org.apache.lucene.index: Code to maintain and access indices.
org.apache.lucene.search: Code to search indices.
org.apache.lucene.search.similarities: This package contains the various ranking models that can be used in Lucene.
org.apache.lucene.search.spans: The calculus of spans.

### Uses of Similarity in org.apache.lucene.index

Fields in org.apache.lucene.index declared as Similarity
`protected Similarity` LiveIndexWriterConfig.`similarity` `Similarity` to use when encoding norms.

Methods in org.apache.lucene.index that return Similarity
`Similarity` LiveIndexWriterConfig.`getSimilarity()` Expert: returns the `Similarity` implementation used by this `IndexWriter`.
`Similarity` IndexWriterConfig.`getSimilarity()`

Methods in org.apache.lucene.index with parameters of type Similarity
`IndexWriterConfig` IndexWriterConfig.`setSimilarity(Similarity similarity)` Expert: set the `Similarity` implementation used by this IndexWriter.

### Uses of Similarity in org.apache.lucene.search

Methods in org.apache.lucene.search that return Similarity
`static Similarity` IndexSearcher.`getDefaultSimilarity()` Expert: returns a default Similarity instance.
`Similarity` IndexSearcher.`getSimilarity()` Expert: Get the `Similarity` to use to compute scores.

Methods in org.apache.lucene.search with parameters of type Similarity
`void` IndexSearcher.`setSimilarity(Similarity similarity)` Expert: Set the Similarity implementation used by this IndexSearcher.

### Uses of Similarity in org.apache.lucene.search.similarities

Subclasses of Similarity in org.apache.lucene.search.similarities
`class` `Axiomatic` Axiomatic approaches for IR.
`class` `AxiomaticF1EXP` F1EXP is defined as Sum(tf(term_doc_freq)*ln(docLen)*IDF(term)) where IDF(t) = pow((N+1)/df(t), k) N=total num of docs, df=doc freq
`class` `AxiomaticF1LOG` F1LOG is defined as Sum(tf(term_doc_freq)*ln(docLen)*IDF(term)) where IDF(t) = ln((N+1)/df(t)) N=total num of docs, df=doc freq
`class` `AxiomaticF2EXP` F2EXP is defined as Sum(tfln(term_doc_freq, docLen)*IDF(term)) where IDF(t) = pow((N+1)/df(t), k) N=total num of docs, df=doc freq
`class` `AxiomaticF2LOG` F2EXP is defined as Sum(tfln(term_doc_freq, docLen)*IDF(term)) where IDF(t) = ln((N+1)/df(t)) N=total num of docs, df=doc freq
`class` `AxiomaticF3EXP` F3EXP is defined as Sum(tf(term_doc_freq)*IDF(term)-gamma(docLen, queryLen)) where IDF(t) = pow((N+1)/df(t), k) N=total num of docs, df=doc freq gamma(docLen, queryLen) = (docLen-queryLen)*queryLen*s/avdl NOTE: the gamma function of this similarity creates negative scores
`class` `AxiomaticF3LOG` F3EXP is defined as Sum(tf(term_doc_freq)*IDF(term)-gamma(docLen, queryLen)) where IDF(t) = ln((N+1)/df(t)) N=total num of docs, df=doc freq gamma(docLen, queryLen) = (docLen-queryLen)*queryLen*s/avdl NOTE: the gamma function of this similarity creates negative scores
`class` `BM25Similarity` BM25 Similarity.
`class` `BooleanSimilarity` Simple similarity that gives terms a score that is equal to their query boost.
`class` `ClassicSimilarity` Expert: Historical scoring implementation.
`class` `DFISimilarity` Implements the Divergence from Independence (DFI) model based on Chi-square statistics (i.e., standardized Chi-squared distance from independence in term frequency tf).
`class` `DFRSimilarity` Implements the divergence from randomness (DFR) framework introduced in Gianni Amati and Cornelis Joost Van Rijsbergen.
`class` `IBSimilarity` Provides a framework for the family of information-based models, as described in Stéphane Clinchant and Eric Gaussier.
`class` `LMDirichletSimilarity` Bayesian smoothing using Dirichlet priors.
`class` `LMJelinekMercerSimilarity` Language model based on the Jelinek-Mercer smoothing method.
`class` `LMSimilarity` Abstract superclass for language modeling Similarities.
`class` `MultiSimilarity` Implements the CombSUM method for combining evidence from multiple similarity values described in: Joseph A.
`class` `PerFieldSimilarityWrapper` Provides the ability to use a different `Similarity` for different fields.
`class` `SimilarityBase` A subclass of `Similarity` that provides a simplified API for its descendants.
`class` `TFIDFSimilarity` Implementation of `Similarity` with the Vector Space Model.

Fields in org.apache.lucene.search.similarities declared as Similarity
`protected Similarity[]` MultiSimilarity.`sims` the sub-similarities used to create the combined score

Methods in org.apache.lucene.search.similarities that return Similarity
`abstract Similarity` PerFieldSimilarityWrapper.`get(String name)` Returns a `Similarity` for scoring a field.

Constructors in org.apache.lucene.search.similarities with parameters of type Similarity
Constructor and Description
`MultiSimilarity(Similarity[] sims)` Creates a MultiSimilarity which will sum the scores of the provided `sims`.

### Uses of Similarity in org.apache.lucene.search.spans

Fields in org.apache.lucene.search.spans declared as Similarity
`protected Similarity` SpanWeight.`similarity`