Class Similarity

Expert: Scoring API.

Subclasses implement search scoring.

The score of query q for document d correlates to the cosine-distance or dot-product between document and query vectors in a Vector Space Model (VSM) of Information Retrieval. A document whose vector is closer to the query vector in that model is scored higher.

The score is computed as follows:

score(q,d) =
coord(q,d) · queryNorm(q) · 		∑ ( tf(t in d) · idf(t)2 · t.Boost · norm(t,d) )
t in q

where

Inheritance
System.Object
Similarity
DefaultSimilarity
SimilarityDelegator
Namespace:
Assembly: Lucene.Net.NetCore.dll
Syntax
public abstract class Similarity : object

Constructors

Name Description
Similarity()

Fields

Name Description
NO_DOC_ID_PROVIDED

Properties

Name Description
Default

Gets or sets the default Similarity implementation used by indexing and search code.

This is initially an instance of DefaultSimilarity.

Methods

Name Description
ComputeNorm(String, FieldInvertState)

Compute the normalization value for a field, given the accumulated state of term processing for this field (see FieldInvertState).

Implementations should calculate a float value based on the field state and then return that value.

For backward compatibility this method by default calls passing Length as the second argument, and then multiplies this value by Boost.

WARNING: This API is new and experimental and may suddenly change.
Coord(Int32, Int32)

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.
DecodeNorm(Byte)

Decodes a normalization factor stored in an index.
EncodeNorm(Single)

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.
GetNormDecoder()

Returns a table for decoding normalization bytes.
Idf(Int32, Int32)

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(Int32) 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.
IdfExplain(ICollection<Term>, Searcher)

Computes a score factor for a phrase.

The default implementation sums the idf factor for each term in the phrase.
IdfExplain(Term, Searcher)

Computes a score factor for a simple term and returns an explanation for that score factor.

The default implementation uses:

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(String, Int32)

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.

Note that the return values are computed under AddDocument(Document) and then stored using EncodeNorm(Single).
Thus they have limited precision, and documents must be re-indexed if this method is altered.
QueryNorm(Single)

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.
ScorePayload(Int32, String, Int32, Int32, Byte[], Int32, Int32)

Calculate a scoring factor based on the data in the payload. Overriding implementations are responsible for interpreting what is in the payload. Lucene makes no assumptions about what is in the byte array.

The default implementation returns 1.
SloppyFreq(Int32)

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(Single).

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(Int32)

Computes a score factor based on a term or phrase's frequency in a document. This value is multiplied by the Idf(Int32, Int32) 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(Single).
Tf(Single)

Computes a score factor based on a term or phrase's frequency in a document. This value is multiplied by the Idf(Int32, Int32) 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.

See Also