org.apache.lucene.search
Class Similarity
- Serializable
public abstract class Similarity
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:
where
sumOfSqaredWeights =
|
Σ |
( idf (t) *
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
DefaultSimilarity
.
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.
|
static float[] | getNormDecoder() - Returns a table for decoding normalization bytes.
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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.
|
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.
overlap
- the number of query terms matched in the documentmaxOverlap
- the total number of terms in the query
- 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.
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.
getDefault
public static Similarity getDefault()
Return the default Similarity implementation used by indexing and search
code.
This is initially an instance of
DefaultSimilarity
.
getNormDecoder
public static float[] getNormDecoder()
Returns a table for decoding normalization bytes.
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.
terms
- the terms in the phrasesearcher
- the document collection being searched
- 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
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.
docFreq
- the number of documents which contain the termnumDocs
- the total number of documents in the collection
- a score factor based on the term's document frequency
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
Searcher.maxDoc()
is used instead of
IndexReader.numDocs()
because it is proportional to
Searcher.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 searched
- a score factor for the term
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
IndexWriter.addDocument(Document)
and stored then using
encodeNorm(float)
. Thus they have limited precision, and documents
must be re-indexed if this method is altered.
fieldName
- the name of the fieldnumTokens
- the total number of tokens contained in fields named
fieldName of doc.
- a normalization factor for hits on this field of this document
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.
sumOfSquaredWeights
- the sum of the squares of query term weights
- a normalization factor for query weights
setDefault
public static void setDefault(Similarity similarity)
Set the default Similarity implementation used by indexing and search
code.
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.
distance
- the edit distance of this sloppy phrase match
- the frequency increment for this match
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 implementations 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 document
- a score factor based on a term's within-document frequency
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)
.
freq
- the frequency of a term within a document
- a score factor based on a term's within-document frequency
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