public abstract class Similarity extends Object
Constructor and Description 

Similarity() 
Modifier and Type  Method and Description 

abstract float 
coord(int overlap,
int maxOverlap)
Computes a score factor based on the fraction of all query terms that a
document contains.

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

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

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

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

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).

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

abstract float 
lengthNorm(String fieldName,
int numTokens)
Computes the normalization value for a field given the total number of
terms contained in a field.

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.

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

abstract float 
sloppyFreq(int distance)
Computes the amount of a sloppy phrase match, based on an edit distance.

abstract float 
tf(float freq)
Computes a score factor based on a term or phrase's frequency in a
document.

float 
tf(int freq)
Computes a score factor based on a term or phrase's frequency in a
document.

public Similarity()
public static void setDefault(Similarity similarity)
public static Similarity getDefault()
This is initially an instance of DefaultSimilarity
.
public static float decodeNorm(byte b)
encodeNorm(float)
public abstract float lengthNorm(String fieldName, int numTokens)
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 IndexWriter.addDocument(Document)
and stored then using
{#encodeNorm(float)}. Thus they have limited precision, and documents
must be reindexed if this method is altered.
fieldName
 the name of the fieldnumTokens
 the total number of tokens contained in fields named
fieldName of doc.Field.setBoost(float)
public abstract float queryNorm(float sumOfSquaredWeights)
This does not affect ranking, but rather just attempts to make scores from different queries comparable.
sumOfSquaredWeights
 the sum of the squares of query term weightspublic static byte encodeNorm(float f)
The encoding uses a fivebit exponent and threebit 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.
Field.setBoost(float)
public float tf(int freq)
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)
.
freq
 the frequency of a term within a documentpublic abstract float sloppyFreq(int distance)
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.
distance
 the edit distance of this sloppy phrase matchPhraseQuery.setSlop(int)
public abstract float tf(float freq)
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.
freq
 the frequency of a term within a documentpublic float idf(Term term, Searcher searcher) throws IOException
The default implementation is:
return idf(searcher.docFreq(term), searcher.maxDoc());Note that
Searchable.maxDoc()
is used instead of
IndexReader.numDocs()
because it is proportional to
Searchable.docFreq(Term)
, i.e., when one is inaccurate,
so is the other, and in the same direction.term
 the term in questionsearcher
 the document collection being searchedIOException
public float idf(Collection terms, Searcher searcher) throws IOException
The default implementation sums the idf(Term,Searcher)
factor
for each term in the phrase.
terms
 the terms in the phrasesearcher
 the document collection being searchedIOException
public abstract float idf(int docFreq, int numDocs)
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.
docFreq
 the number of documents which contain the termnumDocs
 the total number of documents in the collectionpublic abstract float coord(int overlap, int maxOverlap)
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.
overlap
 the number of query terms matched in the documentmaxOverlap
 the total number of terms in the query