org.apache.lucene.search

Class SimilarityDelegator

java.lang.Object

org.apache.lucene.search.Similarity

org.apache.lucene.search.SimilarityDelegator

All Implemented Interfaces:

java.io.Serializable

public class SimilarityDelegator
extends Similarity

Expert: Delegating scoring implementation. Useful in Query.getSimilarity(Searcher) implementations, to override only certain methods of a Searcher's Similiarty implementation..

See Also:

Serialized Form

Constructor Summary

Constructors

Constructor and Description
SimilarityDelegator(Similarity delegee) Construct a Similarity that delegates all methods to another.

Method Summary

Modifier and Type	Method and Description
float	coord(int overlap, int maxOverlap) Computes a score factor based on the fraction of all query terms that a document contains.
float	idf(int docFreq, int numDocs) Computes a score factor based on a term's document frequency (the number of documents which contain the term).
float	lengthNorm(java.lang.String fieldName, int numTerms) Computes the normalization value for a field given the total number of terms contained in a field.
float	queryNorm(float sumOfSquaredWeights) Computes the normalization value for a query given the sum of the squared weights of each of the query terms.
float	sloppyFreq(int distance) Computes the amount of a sloppy phrase match, based on an edit distance.
float	tf(float freq) Computes a score factor based on a term or phrase's frequency in a document.

Methods inherited from class org.apache.lucene.search.Similarity

decodeNorm, encodeNorm, getDefault, getNormDecoder, idf, idf, scorePayload, setDefault, tf

Methods inherited from class java.lang.Object

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

Constructor Detail

SimilarityDelegator

public SimilarityDelegator(Similarity delegee)

Construct a Similarity that delegates all methods to another.

Parameters:

delegee - the Similarity implementation to delegate to

Method Detail

lengthNorm

public float lengthNorm(java.lang.String fieldName,
                        int numTerms)

Description copied from class: Similarity

Computes the normalization value for a field given the total number of terms contained in a field. These values, together with field boosts, are stored in an index and multipled into scores for hits on each field by the search code.

Matches in longer fields are less precise, so implementations of this method usually return smaller values when numTokens is large, and larger values when numTokens is small.

That these values are computed under IndexWriter.addDocument(org.apache.lucene.document.Document) and stored then using Similarity.encodeNorm(float). Thus they have limited precision, and documents must be re-indexed if this method is altered.

Specified by:

lengthNorm in class Similarity

Parameters:

fieldName - the name of the field

numTerms - the total number of tokens contained in fields named fieldName of doc.

Returns:

a normalization factor for hits on this field of this document

See Also:

AbstractField.setBoost(float)

queryNorm

public float queryNorm(float sumOfSquaredWeights)

Description copied from class: Similarity

Computes the normalization value for a query given the sum of the squared weights of each of the query terms. This value is then multipled into the weight of each query term.

This does not affect ranking, but rather just attempts to make scores from different queries comparable.

Specified by:

queryNorm in class Similarity

Parameters:

sumOfSquaredWeights - the sum of the squares of query term weights

Returns:

a normalization factor for query weights

tf

public float tf(float freq)

Description copied from class: Similarity

Computes a score factor based on a term or phrase's frequency in a document. This value is multiplied by the Similarity.idf(Term, Searcher) factor for each term in the query and these products are then summed to form the initial score for a document.

Terms and phrases repeated in a document indicate the topic of the document, so implementations of this method usually return larger values when freq is large, and smaller values when freq is small.

Specified by:

tf in class Similarity

Parameters:

freq - the frequency of a term within a document

Returns:

a score factor based on a term's within-document frequency

sloppyFreq

public float sloppyFreq(int distance)

Description copied from class: Similarity

Computes the amount of a sloppy phrase match, based on an edit distance. This value is summed for each sloppy phrase match in a document to form the frequency that is passed to Similarity.tf(float).

A phrase match with a small edit distance to a document passage more closely matches the document, so implementations of this method usually return larger values when the edit distance is small and smaller values when it is large.

Specified by:

sloppyFreq in class Similarity

Parameters:

distance - the edit distance of this sloppy phrase match

Returns:

the frequency increment for this match

See Also:

PhraseQuery.setSlop(int)

idf

public float idf(int docFreq,
                 int numDocs)

Description copied from class: Similarity

Computes a score factor based on a term's document frequency (the number of documents which contain the term). This value is multiplied by the Similarity.tf(int) factor for each term in the query and these products are then summed to form the initial score for a document.

Terms that occur in fewer documents are better indicators of topic, so implementations of this method usually return larger values for rare terms, and smaller values for common terms.

Specified by:

idf in class Similarity

Parameters:

docFreq - the number of documents which contain the term

numDocs - the total number of documents in the collection

Returns:

a score factor based on the term's document frequency

coord

public float coord(int overlap,
                   int maxOverlap)

Description copied from class: Similarity

Computes a score factor based on the fraction of all query terms that a document contains. This value is multiplied into scores.

The presence of a large portion of the query terms indicates a better match with the query, so implementations of this method usually return larger values when the ratio between these parameters is large and smaller values when the ratio between them is small.

Specified by:

coord in class Similarity

Parameters:

overlap - the number of query terms matched in the document

maxOverlap - the total number of terms in the query

Returns:

a score factor based on term overlap with the query