Documentation  › org.apache.lucene.search  › Similarity
 
 


  Similarity
  public abstract

  Inherits From:   Object
  Conforms To:   none
  Declared In:   org.apache.lucene.search


Class Description
 
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 tf(t in d) * idf(t) * getBoost(t.field in d) * lengthNorm(t.field in d)  * coord(q,d) * queryNorm(q)
t in q


Class Variables
 
None declared in this class.


Instance Variables
 
None declared in this class.


Constructors
 
Similarity
public Similarity( )

No description available for this constructor.


Class Methods
 
decodeNorm
public static float decodeNorm( byte b )

Decodes a normalization factor stored in an index.


encodeNorm
public static byte encodeNorm( float f )

Encodes a normalization factor for storage in an index.

The encoding uses a five-bit exponent and three-bit mantissa, 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.


getDefault
public static Similarity getDefault( )

Return the default Similarity implementation used by indexing and search code.

This is initially an instance of DefaultSimilarity.


setDefault
public static void setDefault( Similarity similarity )

Set the default Similarity implementation used by indexing and search code.



Instance Methods
 
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 implemenations 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.


idf
public float idf( Collection terms, Searcher searcher ) throws IOException

Computes a score factor for a phrase.

The default implementation sums the idf(Term,Searcher) factor for each term in 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 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 implemenations of this method usually return larger values for rare terms, and smaller values for common terms.


idf
public float idf( Term term, Searcher searcher ) throws IOException

Computes a score factor for a simple term.

The default implementation is:

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


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 implemenations of this method usually return smaller values when numTokens is large, and larger values when numTokens is small.

That these values are computed under addDocument(Document) and stored then using {#encodeNorm(float)}. Thus they have limited precision, and documents must be re-indexed if this method is altered.


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.


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 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.


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 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 implemenations of this method usually return larger values when freq is large, and smaller values when freq is small.


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 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 tf(float).



Known Subclasses
 
DefaultSimilarity



 
 
  dydoc
  3/9/05