Class MultiSimilarity
- java.lang.Object
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- org.apache.lucene.search.similarities.Similarity
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- org.apache.lucene.search.similarities.MultiSimilarity
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public class MultiSimilarity extends Similarity
Implements the CombSUM method for combining evidence from multiple similarity values described in: Joseph A. Shaw, Edward A. Fox. In Text REtrieval Conference (1993), pp. 243-252- WARNING: This API is experimental and might change in incompatible ways in the next release.
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Nested Class Summary
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Nested classes/interfaces inherited from class org.apache.lucene.search.similarities.Similarity
Similarity.SimScorer
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Field Summary
Fields Modifier and Type Field Description protected Similarity[]simsthe sub-similarities used to create the combined score
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Constructor Summary
Constructors Constructor Description MultiSimilarity(Similarity[] sims)Creates a MultiSimilarity which will sum the scores of the providedsims.
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Method Summary
All Methods Instance Methods Concrete Methods Modifier and Type Method Description longcomputeNorm(FieldInvertState state)Computes the normalization value for a field, given the accumulated state of term processing for this field (seeFieldInvertState).Similarity.SimScorerscorer(float boost, CollectionStatistics collectionStats, TermStatistics... termStats)Compute any collection-level weight (e.g.
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Field Detail
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sims
protected final Similarity[] sims
the sub-similarities used to create the combined score
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Constructor Detail
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MultiSimilarity
public MultiSimilarity(Similarity[] sims)
Creates a MultiSimilarity which will sum the scores of the providedsims.
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Method Detail
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computeNorm
public long computeNorm(FieldInvertState state)
Description copied from class:SimilarityComputes the normalization value for a field, given the accumulated state of term processing for this field (seeFieldInvertState).Matches in longer fields are less precise, so implementations of this method usually set smaller values when
state.getLength()is large, and larger values whenstate.getLength()is small.Note that for a given term-document frequency, greater unsigned norms must produce scores that are lower or equal, ie. for two encoded norms
n1andn2so thatLong.compareUnsigned(n1, n2) > 0thenSimScorer.score(freq, n1) <= SimScorer.score(freq, n2)for any legalfreq.0is not a legal norm, so1is the norm that produces the highest scores.- Specified by:
computeNormin classSimilarity- Parameters:
state- current processing state for this field- Returns:
- computed norm value
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scorer
public Similarity.SimScorer scorer(float boost, CollectionStatistics collectionStats, TermStatistics... termStats)
Description copied from class:SimilarityCompute any collection-level weight (e.g. IDF, average document length, etc) needed for scoring a query.- Specified by:
scorerin classSimilarity- Parameters:
boost- a multiplicative factor to apply to the produces scorescollectionStats- collection-level statistics, such as the number of tokens in the collection.termStats- term-level statistics, such as the document frequency of a term across the collection.- Returns:
- SimWeight object with the information this Similarity needs to score a query.
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