Uses of Class
org.apache.lucene.search.similarities.Similarity
Packages that use Similarity
Package
Description
Code to maintain and access indices.
Code to search indices.
This package contains the various ranking models that can be used in Lucene.
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Uses of Similarity in org.apache.lucene.index
Fields in org.apache.lucene.index declared as SimilarityModifier and TypeFieldDescriptionprotected Similarity
LiveIndexWriterConfig.similarity
Similarity
to use when encoding norms.Methods in org.apache.lucene.index that return SimilarityModifier and TypeMethodDescriptionIndexWriterConfig.getSimilarity()
LiveIndexWriterConfig.getSimilarity()
Expert: returns theSimilarity
implementation used by thisIndexWriter
.Methods in org.apache.lucene.index with parameters of type SimilarityModifier and TypeMethodDescriptionIndexWriterConfig.setSimilarity
(Similarity similarity) Expert: set theSimilarity
implementation used by this IndexWriter. -
Uses of Similarity in org.apache.lucene.search
Methods in org.apache.lucene.search that return SimilarityModifier and TypeMethodDescriptionstatic Similarity
IndexSearcher.getDefaultSimilarity()
Expert: returns a default Similarity instance.IndexSearcher.getSimilarity()
Expert: Get theSimilarity
to use to compute scores.Methods in org.apache.lucene.search with parameters of type SimilarityModifier and TypeMethodDescriptionvoid
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.similaritiesModifier and TypeClassDescriptionclass
Axiomatic approaches for IR.class
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 freqclass
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 freqclass
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 freqclass
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 freqclass
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 scoresclass
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 scoresclass
BM25 Similarity.class
Simple similarity that gives terms a score that is equal to their query boost.class
Expert: Historical scoring implementation.class
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
Implements the divergence from randomness (DFR) framework introduced in Gianni Amati and Cornelis Joost Van Rijsbergen.class
Provides a framework for the family of information-based models, as described in Stéphane Clinchant and Eric Gaussier.class
Bayesian smoothing using Dirichlet priors as implemented in the Indri Search engine (http://www.lemurproject.org/indri.php).class
Bayesian smoothing using Dirichlet priors.class
Language model based on the Jelinek-Mercer smoothing method.class
Abstract superclass for language modeling Similarities.class
Implements the CombSUM method for combining evidence from multiple similarity values described in: Joseph A.class
Provides the ability to use a differentSimilarity
for different fields.class
A subclass ofSimilarity
that provides a simplified API for its descendants.class
Implementation ofSimilarity
with the Vector Space Model.Fields in org.apache.lucene.search.similarities declared as SimilarityModifier and TypeFieldDescriptionprotected final Similarity[]
MultiSimilarity.sims
the sub-similarities used to create the combined scoreMethods in org.apache.lucene.search.similarities that return SimilarityConstructors in org.apache.lucene.search.similarities with parameters of type SimilarityModifierConstructorDescriptionMultiSimilarity
(Similarity[] sims) Creates a MultiSimilarity which will sum the scores of the providedsims
.