Package  Description 

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.

Modifier and Type  Field and Description 

protected Similarity 
LiveIndexWriterConfig.similarity
Similarity to use when encoding norms. 
Modifier and Type  Method and Description 

Similarity 
IndexWriterConfig.getSimilarity() 
Similarity 
LiveIndexWriterConfig.getSimilarity()
Expert: returns the
Similarity implementation used by this
IndexWriter . 
Modifier and Type  Method and Description 

IndexWriterConfig 
IndexWriterConfig.setSimilarity(Similarity similarity)
Expert: set the
Similarity implementation used by this IndexWriter. 
Modifier and Type  Method and Description 

static Similarity 
IndexSearcher.getDefaultSimilarity()
Expert: returns a default Similarity instance.

Similarity 
IndexSearcher.getSimilarity()
Expert: Get the
Similarity to use to compute scores. 
Modifier and Type  Method and Description 

void 
IndexSearcher.setSimilarity(Similarity similarity)
Expert: Set the Similarity implementation used by this IndexSearcher.

Modifier and Type  Class and Description 

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) = (docLenqueryLen)*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) = (docLenqueryLen)*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 Chisquare statistics
(i.e., standardized Chisquared 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 informationbased models, as described
in Stéphane Clinchant and Eric Gaussier.

class 
IndriDirichletSimilarity
Bayesian smoothing using Dirichlet priors as implemented in the Indri Search engine
(http://www.lemurproject.org/indri.php).

class 
LMDirichletSimilarity
Bayesian smoothing using Dirichlet priors.

class 
LMJelinekMercerSimilarity
Language model based on the JelinekMercer 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. 
Modifier and Type  Field and Description 

protected Similarity[] 
MultiSimilarity.sims
the subsimilarities used to create the combined score

Modifier and Type  Method and Description 

abstract Similarity 
PerFieldSimilarityWrapper.get(String name)
Returns a
Similarity for scoring a field. 
Constructor and Description 

MultiSimilarity(Similarity[] sims)
Creates a MultiSimilarity which will sum the scores
of the provided
sims . 
Modifier and Type  Field and Description 

protected Similarity 
SpanWeight.similarity 
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