org.apache.lucene.search

Class Similarity

public abstract class Similarity extends Object implements Serializable

Expert: Scoring API.

Subclasses implement search scoring.

The score of query q for document d is defined in terms of these methods as follows:

score(q,d) =
Σ ( {@link #tf(int) tf}(t in d) * {@link #idf(Term,Searcher) idf}(t)^2 * {@link Query#getBoost getBoost}(t in q) * {@link Field#getBoost getBoost}(t.field in d) * {@link #lengthNorm(String,int) lengthNorm}(t.field in d) )  * {@link #coord(int,int) coord}(q,d) * {@link #queryNorm(float) queryNorm}(sumOfSqaredWeights)
t in q

where

sumOfSqaredWeights =
Σ ( {@link #idf(Term,Searcher) idf}(t) * {@link Query#getBoost getBoost}(t in q) )^2
t in q

Note that the above formula is motivated by the cosine-distance or dot-product between document and query vector, which is implemented by {@link DefaultSimilarity}.

See Also: setDefault setSimilarity setSimilarity

Method Summary
abstract floatcoord(int overlap, int maxOverlap)
Computes a score factor based on the fraction of all query terms that a document contains.
static floatdecodeNorm(byte b)
Decodes a normalization factor stored in an index.
static byteencodeNorm(float f)
Encodes a normalization factor for storage in an index.
static SimilaritygetDefault()
Return the default Similarity implementation used by indexing and search code.
static float[]getNormDecoder()
Returns a table for decoding normalization bytes.
floatidf(Term term, Searcher searcher)
Computes a score factor for a simple term.
floatidf(Collection terms, Searcher searcher)
Computes a score factor for a phrase.
abstract floatidf(int docFreq, int numDocs)
Computes a score factor based on a term's document frequency (the number of documents which contain the term).
abstract floatlengthNorm(String fieldName, int numTokens)
Computes the normalization value for a field given the total number of terms contained in a field.
abstract floatqueryNorm(float sumOfSquaredWeights)
Computes the normalization value for a query given the sum of the squared weights of each of the query terms.
static voidsetDefault(Similarity similarity)
Set the default Similarity implementation used by indexing and search code.
abstract floatsloppyFreq(int distance)
Computes the amount of a sloppy phrase match, based on an edit distance.
floattf(int freq)
Computes a score factor based on a term or phrase's frequency in a document.
abstract floattf(float freq)
Computes a score factor based on a term or phrase's frequency in a document.

Method Detail

coord

public abstract float coord(int overlap, int maxOverlap)
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.

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

decodeNorm

public static float decodeNorm(byte b)
Decodes a normalization factor stored in an index.

See Also: Similarity

encodeNorm

public static byte encodeNorm(float f)
Encodes a normalization factor for storage in an index.

The encoding uses a three-bit mantissa, a five-bit exponent, and the zero-exponent point at 15, thus representing values from around 7x10^9 to 2x10^-9 with about one significant decimal digit of accuracy. Zero is also represented. Negative numbers are rounded up to zero. Values too large to represent are rounded down to the largest representable value. Positive values too small to represent are rounded up to the smallest positive representable value.

See Also: Field SmallFloat

getDefault

public static Similarity getDefault()
Return the default Similarity implementation used by indexing and search code.

This is initially an instance of {@link DefaultSimilarity}.

See Also: setSimilarity setSimilarity

getNormDecoder

public static float[] getNormDecoder()
Returns a table for decoding normalization bytes.

See Also: Similarity

idf

public float idf(Term term, Searcher searcher)
Computes a score factor for a simple term.

The default implementation is:

   return idf(searcher.docFreq(term), searcher.maxDoc());
 
Note that {@link Searcher#maxDoc()} is used instead of {@link IndexReader#numDocs()} because it is proportional to {@link Searcher#docFreq(Term)} , i.e., when one is inaccurate, so is the other, and in the same direction.

Parameters: term the term in question searcher the document collection being searched

Returns: a score factor for the term

idf

public float idf(Collection terms, Searcher searcher)
Computes a score factor for a phrase.

The default implementation sums the {@link #idf(Term,Searcher)} factor for each term in the phrase.

Parameters: terms the terms in the phrase searcher the document collection being searched

Returns: a score factor for the phrase

idf

public abstract 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). This value is multiplied by the {@link #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.

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

lengthNorm

public abstract float lengthNorm(String fieldName, int numTokens)
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 {@link IndexWriter#addDocument(org.apache.lucene.document.Document)} and stored then using {@link #encodeNorm(float)}. Thus they have limited precision, and documents must be re-indexed if this method is altered.

Parameters: fieldName the name of the field numTokens 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: Field

queryNorm

public abstract float queryNorm(float sumOfSquaredWeights)
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.

Parameters: sumOfSquaredWeights the sum of the squares of query term weights

Returns: a normalization factor for query weights

setDefault

public static void setDefault(Similarity similarity)
Set the default Similarity implementation used by indexing and search code.

See Also: setSimilarity setSimilarity

sloppyFreq

public abstract float sloppyFreq(int distance)
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 {@link #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.

Parameters: distance the edit distance of this sloppy phrase match

Returns: the frequency increment for this match

See Also: PhraseQuery

tf

public float tf(int freq)
Computes a score factor based on a term or phrase's frequency in a document. This value is multiplied by the {@link #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.

The default implementation calls {@link #tf(float)}.

Parameters: freq the frequency of a term within a document

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

tf

public abstract float tf(float freq)
Computes a score factor based on a term or phrase's frequency in a document. This value is multiplied by the {@link #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.

Parameters: freq the frequency of a term within a document

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

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