Class MultiSimilarity
java.lang.Object
org.apache.lucene.search.similarities.Similarity
org.apache.lucene.search.similarities.MultiSimilarity
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
Nested classes/interfaces inherited from class org.apache.lucene.search.similarities.Similarity
Similarity.SimScorer
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Field Summary
Modifier and TypeFieldDescriptionprotected final Similarity[]
the sub-similarities used to create the combined score -
Constructor Summary
ConstructorDescriptionMultiSimilarity
(Similarity[] sims) Creates a MultiSimilarity which will sum the scores of the providedsims
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Method Summary
Modifier and TypeMethodDescriptionlong
computeNorm
(FieldInvertState state) Computes the normalization value for a field, given the accumulated state of term processing for this field (seeFieldInvertState
).scorer
(float boost, CollectionStatistics collectionStats, TermStatistics... termStats) Compute any collection-level weight (e.g.
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Field Details
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sims
the sub-similarities used to create the combined score
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Constructor Details
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MultiSimilarity
Creates a MultiSimilarity which will sum the scores of the providedsims
.
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Method Details
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computeNorm
Description copied from class:Similarity
Computes 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
n1
andn2
so thatLong.compareUnsigned(n1, n2) > 0
thenSimScorer.score(freq, n1) <= SimScorer.score(freq, n2)
for any legalfreq
.0
is not a legal norm, so1
is the norm that produces the highest scores.- Specified by:
computeNorm
in 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:Similarity
Compute any collection-level weight (e.g. IDF, average document length, etc) needed for scoring a query.- Specified by:
scorer
in 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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