Class PerFieldSimilarityWrapper
Similarity
for different fields.
Subclasses should implement get(String)
to return an appropriate Similarity (for
example, using field-specific parameter values) for the field.
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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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Constructor Summary
Constructors -
Method Summary
Modifier and TypeMethodDescriptionfinal long
computeNorm
(FieldInvertState state) Computes the normalization value for a field at index-time.abstract Similarity
Returns aSimilarity
for scoring a field.final Similarity.SimScorer
scorer
(float boost, CollectionStatistics collectionStats, TermStatistics... termStats) Compute any collection-level weight (e.g.Methods inherited from class org.apache.lucene.search.similarities.Similarity
getDiscountOverlaps
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Constructor Details
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PerFieldSimilarityWrapper
public PerFieldSimilarityWrapper()Sole constructor. (For invocation by subclass constructors, typically implicit.)
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Method Details
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computeNorm
Description copied from class:Similarity
Computes the normalization value for a field at index-time.The default implementation uses
SmallFloat.intToByte4(int)
to encode the number of terms as a single byte.WARNING: The default implementation is used by Lucene's supplied Similarity classes, which means you can change the Similarity at runtime without reindexing. If you override this method, you'll need to re-index documents for it to take effect.
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.- Overrides:
computeNorm
in classSimilarity
- Parameters:
state
- accumulated state of term processing for this field- Returns:
- computed norm value
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scorer
public final 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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get
Returns aSimilarity
for scoring a field.
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