public class BooleanSimilarity extends SimilaritySimple similarity that gives terms a score that is equal to their query boost. This similarity is typically used with disabled norms since neither document statistics nor index statistics are used for scoring. That said, if norms are enabled, they will be computed the same way as
discounted overlapsso that the
Similaritycan be changed after the index has been created.
Nested Class Summary
Nested classes/interfaces inherited from class org.apache.lucene.search.similarities.Similarity
Constructors Constructor Description
All Methods Instance Methods Concrete Methods Modifier and Type Method Description
computeNorm(FieldInvertState state)Computes the normalization value for a field, given the accumulated state of term processing for this field (see
scorer(float boost, CollectionStatistics collectionStats, TermStatistics... termStats)Compute any collection-level weight (e.g.
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 (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.
- Specified by:
state- current processing state for this field
- computed norm value
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:
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.