- WARNING: This API is experimental and might change in incompatible ways in the next release.
Nested Class Summary
Method SummaryModifier and TypeMethodDescription
longComputes the normalization value for a field, given the accumulated state of term processing for this field (see
FieldInvertState).Compute any collection-level weight (e.g.
simsprotected final Similarity simsthe sub-similarities used to create the combined score
(Similarity sims)Creates a MultiSimilarity which will sum the scores of the provided
computeNormpublic 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 (see
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
Note that for a given term-document frequency, greater unsigned norms must produce scores that are lower or equal, ie. for two encoded norms
Long.compareUnsigned(n1, n2) > 0then
SimScorer.score(freq, n1) <= SimScorer.score(freq, n2)for any legal
0is not a legal norm, so
1is the norm that produces the highest scores.
scorerpublic 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:
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.
- SimWeight object with the information this Similarity needs to score a query.