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Similarity.javaAPI DocApache Lucene 1.911607Mon Feb 20 09:20:04 GMT 2006org.apache.lucene.search

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
#setDefault(Similarity)
see
IndexWriter#setSimilarity(Similarity)
see
Searcher#setSimilarity(Similarity)

Fields Summary
private static Similarity
defaultImpl
The Similarity implementation used by default.
private static final float[]
NORM_TABLE
Cache of decoded bytes.
Constructors Summary
Methods Summary
public abstract floatcoord(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.

param
overlap the number of query terms matched in the document
param
maxOverlap the total number of terms in the query
return
a score factor based on term overlap with the query

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

see
#encodeNorm(float)


   
    for (int i = 0; i < 256; i++)
      NORM_TABLE[i] = SmallFloat.byte315ToFloat((byte)i);
  
    return NORM_TABLE[b & 0xFF];  // & 0xFF maps negative bytes to positive above 127
  
public static byteencodeNorm(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
Field#setBoost(float)
see
SmallFloat

    return SmallFloat.floatToByte315(f);
  
public static org.apache.lucene.search.SimilaritygetDefault()
Return the default Similarity implementation used by indexing and search code.

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

see
Searcher#setSimilarity(Similarity)
see
IndexWriter#setSimilarity(Similarity)

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

see
#encodeNorm(float)

    return NORM_TABLE;
  
public floatidf(org.apache.lucene.index.Term term, org.apache.lucene.search.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.

param
term the term in question
param
searcher the document collection being searched
return
a score factor for the term

    return idf(searcher.docFreq(term), searcher.maxDoc());
  
public floatidf(java.util.Collection terms, org.apache.lucene.search.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.

param
terms the terms in the phrase
param
searcher the document collection being searched
return
a score factor for the phrase

    float idf = 0.0f;
    Iterator i = terms.iterator();
    while (i.hasNext()) {
      idf += idf((Term)i.next(), searcher);
    }
    return idf;
  
public 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). 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.

param
docFreq the number of documents which contain the term
param
numDocs the total number of documents in the collection
return
a score factor based on the term's document frequency

public abstract floatlengthNorm(java.lang.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.

param
fieldName the name of the field
param
numTokens the total number of tokens contained in fields named fieldName of doc.
return
a normalization factor for hits on this field of this document
see
Field#setBoost(float)

public abstract floatqueryNorm(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.

param
sumOfSquaredWeights the sum of the squares of query term weights
return
a normalization factor for query weights

public static voidsetDefault(org.apache.lucene.search.Similarity similarity)
Set the default Similarity implementation used by indexing and search code.

see
Searcher#setSimilarity(Similarity)
see
IndexWriter#setSimilarity(Similarity)


                    
       
    Similarity.defaultImpl = similarity;
  
public abstract floatsloppyFreq(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.

see
PhraseQuery#setSlop(int)
param
distance the edit distance of this sloppy phrase match
return
the frequency increment for this match

public abstract floattf(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.

param
freq the frequency of a term within a document
return
a score factor based on a term's within-document frequency

public floattf(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)}.

param
freq the frequency of a term within a document
return
a score factor based on a term's within-document frequency

    return tf((float)freq);