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 SimilarityLiveIndexWriterConfig.similaritySimilarityto use when encoding norms.Methods in org.apache.lucene.index that return SimilarityModifier and TypeMethodDescriptionIndexWriterConfig.getSimilarity()LiveIndexWriterConfig.getSimilarity()Expert: returns theSimilarityimplementation used by thisIndexWriter.Methods in org.apache.lucene.index with parameters of type SimilarityModifier and TypeMethodDescriptionIndexWriterConfig.setSimilarity(Similarity similarity) Expert: set theSimilarityimplementation used by this IndexWriter. -
Uses of Similarity in org.apache.lucene.search
Methods in org.apache.lucene.search that return SimilarityModifier and TypeMethodDescriptionstatic SimilarityIndexSearcher.getDefaultSimilarity()Expert: returns a default Similarity instance.IndexSearcher.getSimilarity()Expert: Get theSimilarityto use to compute scores.Methods in org.apache.lucene.search with parameters of type SimilarityModifier and TypeMethodDescriptionvoidIndexSearcher.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 TypeClassDescriptionclassAxiomatic approaches for IR.classF1EXP 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 freqclassF1LOG 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 freqclassF2EXP 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 freqclassF2EXP 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 freqclassF3EXP 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 scoresclassF3EXP 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 scoresclassBM25 Similarity.classSimple similarity that gives terms a score that is equal to their query boost.classExpert: Historical scoring implementation.classImplements the Divergence from Independence (DFI) model based on Chi-square statistics (i.e., standardized Chi-squared distance from independence in term frequency tf).classImplements the divergence from randomness (DFR) framework introduced in Gianni Amati and Cornelis Joost Van Rijsbergen.classProvides a framework for the family of information-based models, as described in Stéphane Clinchant and Eric Gaussier.classBayesian smoothing using Dirichlet priors as implemented in the Indri Search engine (http://www.lemurproject.org/indri.php).classBayesian smoothing using Dirichlet priors.classLanguage model based on the Jelinek-Mercer smoothing method.classAbstract superclass for language modeling Similarities.classImplements the CombSUM method for combining evidence from multiple similarity values described in: Joseph A.classProvides the ability to use a differentSimilarityfor different fields.classSimilarity that returns the raw TF as score.classA subclass ofSimilaritythat provides a simplified API for its descendants.classImplementation ofSimilaritywith the Vector Space Model.Fields in org.apache.lucene.search.similarities declared as SimilarityModifier and TypeFieldDescriptionprotected final Similarity[]MultiSimilarity.simsthe 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.