@Deprecated public class SimilarityDelegator extends Similarity
Query.getSimilarity(Searcher)implementations, to override only certain methods of a Searcher's Similarity implementation..
|Constructor and Description|
|Modifier and Type||Method and Description|
Computes the normalization value for a field, given the accumulated state of term processing for this field (see
Computes a score factor based on the fraction of all query terms that a document contains.
Computes a score factor based on a term's document frequency (the number of documents which contain the term).
Computes the normalization value for a query given the sum of the squared weights of each of the query terms.
Calculate a scoring factor based on the data in the payload.
Computes the amount of a sloppy phrase match, based on an edit distance.
Computes a score factor based on a term or phrase's frequency in a document.
decodeNorm, decodeNormValue, encodeNorm, encodeNormValue, getDefault, getNormDecoder, idfExplain, idfExplain, idfExplain, lengthNorm, setDefault, tf
public SimilarityDelegator(Similarity delegee)
Similaritythat delegates all methods to another.
delegee- the Similarity implementation to delegate to
public float computeNorm(String fieldName, FieldInvertState state)
Implementations should calculate a float value based on the field state and then return that value.
Matches in longer fields are less precise, so implementations of this
method usually return smaller values when
state.getLength() is large,
and larger values when
state.getLength() is small.
Note that the return values are computed under
and then stored using
Thus they have limited precision, and documents
must be re-indexed if this method is altered.
For backward compatibility this method by default calls
Similarity.lengthNorm(String, int) passing
FieldInvertState.getLength() as the second argument, and
then multiplies this value by
fieldName- field name
state- current processing state for this field
public float queryNorm(float sumOfSquaredWeights)
This does not affect ranking, but the default implementation does make scores from different queries more comparable than they would be by eliminating the magnitude of the Query vector as a factor in the score.
sumOfSquaredWeights- the sum of the squares of query term weights
public float tf(float freq)
Similarity.idf(int, int)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
freq is large, and smaller values when
freq- the frequency of a term within a document
public float sloppyFreq(int distance)
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
public float idf(int docFreq, int numDocs)
Similarity.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 term
numDocs- the total number of documents in the collection
public float coord(int overlap, int maxOverlap)
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 document
maxOverlap- the total number of terms in the query
public float scorePayload(int docId, String fieldName, int start, int end, byte payload, int offset, int length)
The default implementation returns 1.
docId- The docId currently being scored. If this value is
Similarity.NO_DOC_ID_PROVIDED, then it should be assumed that the PayloadQuery implementation does not provide document information
fieldName- The fieldName of the term this payload belongs to
start- The start position of the payload
end- The end position of the payload
payload- The payload byte array to be scored
offset- The offset into the payload array
length- The length in the array