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:
///
///
///
///
|
/// |
/// /// frequency½ /// | ///
/// |
/// /// 1 + log ( /// | ///
///
|
/// /// ) /// | ///
/// queryNorm(q) =
/// |
///
///
|
///
/// |
/// /// ∑ /// | ////// ( /// idf(t) · /// t.getBoost() /// ) 2 /// | ///
/// | t in q | ////// |
/// norm(t,d) =
/// |
/// /// ∏ /// | ///
/// |
///
/// | field f in d named as t | ////// |
numTokens
is large,
/// and larger values when numTokens
is small.
///
/// Info: return values are computed under
/// freq
is large, and smaller values when freq
/// is small.
///
/// The default implementation calls freq
is large, and smaller values when freq
/// is small.
///
/// /// return idf(searcher.docFreq(term), searcher.maxDoc()); ////// /// Info: that
/// idf(searcher.docFreq(term), searcher.maxDoc()); ////// /// Info: