Package org.elasticsearch.index.similarity

Class NonNegativeScoresSimilarity

java.lang.Object

org.apache.lucene.search.similarities.Similarity

org.elasticsearch.index.similarity.NonNegativeScoresSimilarity

public final class NonNegativeScoresSimilarity
extends Similarity

A Similarity that rejects negative scores. This class exists so that users get an error instead of silently corrupt top hits. It should be applied to any custom or scripted similarity.

Nested Class Summary

Nested classes/interfaces inherited from class org.apache.lucene.search.similarities.Similarity

Similarity.SimScorer

Constructor Summary

Constructors

Constructor	Description
NonNegativeScoresSimilarity(Similarity in)

Method Summary

Modifier and Type	Method	Description
long	computeNorm(FieldInvertState state)	Computes the normalization value for a field, given the accumulated state of term processing for this field (see FieldInvertState).
Similarity	getDelegate()
Similarity.SimScorer	scorer(float boost, CollectionStatistics collectionStats, TermStatistics... termStats)	Compute any collection-level weight (e.g.

Methods inherited from class java.lang.Object

clone, equals, finalize, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait

Constructor Detail

NonNegativeScoresSimilarity

public NonNegativeScoresSimilarity(Similarity in)

Method Detail

getDelegate

public Similarity getDelegate()

computeNorm

public long computeNorm(FieldInvertState state)

Description copied from class: Similarity

Computes the normalization value for a field, given the accumulated state of term processing for this field (see FieldInvertState).

Matches in longer fields are less precise, so implementations of this method usually set smaller values when state.getLength() is large, and larger values when state.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 and n2 so that Long.compareUnsigned(n1, n2) > 0 then SimScorer.score(freq, n1) <= SimScorer.score(freq, n2) for any legal freq.

0 is not a legal norm, so 1 is the norm that produces the highest scores.

Specified by:

computeNorm in class Similarity

Parameters:

state - current processing state for this field

Returns:

computed norm value

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 class Similarity

Parameters:

boost - a multiplicative factor to apply to the produces scores

collectionStats - 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.