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Diffstat (limited to '3rdparty/clucene/src/CLucene/search/Similarity.h')
| -rw-r--r-- | 3rdparty/clucene/src/CLucene/search/Similarity.h | 268 |
1 files changed, 268 insertions, 0 deletions
diff --git a/3rdparty/clucene/src/CLucene/search/Similarity.h b/3rdparty/clucene/src/CLucene/search/Similarity.h new file mode 100644 index 000000000..426af6952 --- /dev/null +++ b/3rdparty/clucene/src/CLucene/search/Similarity.h @@ -0,0 +1,268 @@ +/*------------------------------------------------------------------------------ +* Copyright (C) 2003-2006 Ben van Klinken and the CLucene Team +* +* Distributable under the terms of either the Apache License (Version 2.0) or +* the GNU Lesser General Public License, as specified in the COPYING file. +------------------------------------------------------------------------------*/ +#ifndef _lucene_search_Similarity_ +#define _lucene_search_Similarity_ + +#if defined(_LUCENE_PRAGMA_ONCE) +# pragma once +#endif + +#include "CLucene/index/Term.h" + +CL_NS_DEF(search) + +class Searcher;//save including the searchheader.h +class DefaultSimilarity; + +/** Expert: Scoring API. +* <p>Subclasses implement search scoring. +* +* <p>The score of query <code>q</code> for document <code>d</code> is defined +* in terms of these methods as follows: +* +* <table cellpadding="0" cellspacing="0" border="0"> +* <tr> +* <td valign="middle" align="right" rowspan="2">score(q,d) =<br></td> +* <td valign="middle" align="center"> +* <big><big><big><big><big>Σ</big></big></big></big></big></td> +* <td valign="middle"><small> +* {@link #tf(int32_t) tf}(t in d) * +* {@link #idf(Term,Searcher) idf}(t) * +* {@link Field#getBoost getBoost}(t.field in d) * +* {@link #lengthNorm(TCHAR*,int32_t) lengthNorm}(t.field in d) +* </small></td> +* <td valign="middle" rowspan="2"> * +* {@link #coord(int32_t,int32_t) coord}(q,d) * +* {@link #queryNorm(qreal) queryNorm}(q) +* </td> +* </tr> +* <tr> +* <td valign="top" align="right"> +* <small>t in q</small> +* </td> +* </tr> +* </table> +* +* @see #setDefault(Similarity) +* @see IndexWriter#setSimilarity(Similarity) +* @see Searcher#setSimilarity(Similarity) +*/ +class Similarity:LUCENE_BASE { +public: + virtual ~Similarity(); + + /** Set the default Similarity implementation used by indexing and search + * code. + * + * @see Searcher#setSimilarity(Similarity) + * @see IndexWriter#setSimilarity(Similarity) + */ + static void setDefault(Similarity* similarity); + + /** Return the default Similarity implementation used by indexing and search + * code. + * + * <p>This is initially an instance of {@link DefaultSimilarity}. + * + * @see Searcher#setSimilarity(Similarity) + * @see IndexWriter#setSimilarity(Similarity) + */ + static Similarity* getDefault(); + + /** Encodes a normalization factor for storage in an index. + * + * <p>The encoding uses a five-bit exponent and three-bit 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. + * + * @see Field#setBoost(qreal) + */ + static uint8_t encodeNorm(qreal f); + + /** Decodes a normalization factor stored in an index. + * @see #encodeNorm(qreal) + */ + static qreal decodeNorm(uint8_t b); + + static uint8_t floatToByte(qreal f); + static qreal byteToFloat(uint8_t b); + + /** Computes a score factor for a phrase. + * + * <p>The default implementation sums the {@link #idf(Term,Searcher)} factor + * for each term in the phrase. + * + * @param terms the terms in the phrase + * @param searcher the document collection being searched + * @return a score factor for the phrase + */ + qreal idf(CL_NS(util)::CLVector<CL_NS(index)::Term*>* terms, Searcher* searcher); + //qreal idf(Term** terms, Searcher* searcher); + + + /** Computes a score factor for a simple term. + * + * <p>The default implementation is:<pre> + * return idf(searcher.docFreq(term), searcher.maxDoc()); + * </pre> + * + * Note that {@link Searcher#maxDoc()} is used instead of + * {@link IndexReader#numDocs()} because it is proportional to + * {@link Searcher#docFreq(Term)} , i.e., when one is inaccurate, + * so is the other, and in the same direction. + * + * @param term the term in question + * @param searcher the document collection being searched + * @return a score factor for the term + */ + qreal idf(CL_NS(index)::Term* term, Searcher* searcher); + + + /** Computes a score factor based on a term or phrase's frequency in a + * document. This value is multiplied by the {@link #idf(Term, Searcher)} + * factor for each term in the query and these products are then summed to + * form the initial score for a document. + * + * <p>Terms and phrases repeated in a document indicate the topic of the + * document, so implementations of this method usually return larger values + * when <code>freq</code> is large, and smaller values when <code>freq</code> + * is small. + * + * <p>The default implementation calls {@link #tf(qreal)}. + * + * @param freq the frequency of a term within a document + * @return a score factor based on a term's within-document frequency + */ + inline qreal tf(int32_t freq){ return tf((qreal)freq); } + + /** 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. + * + * <p>Matches in longer fields are less precise, so implemenations of this + * method usually return smaller values when <code>numTokens</code> is large, + * and larger values when <code>numTokens</code> is small. + * + * <p>That these values are computed under {@link + * IndexWriter#addDocument(Document)} and stored then using + * {#encodeNorm(qreal)}. Thus they have limited precision, and documents + * must be re-indexed if this method is altered. + * + * @param fieldName the name of the field + * @param numTokens the total number of tokens contained in fields named + * <i>fieldName</i> of <i>doc</i>. + * @return a normalization factor for hits on this field of this document + * + * @see Field#setBoost(qreal) + */ + virtual qreal lengthNorm(const TCHAR* fieldName, int32_t numTokens) = 0; + + /** 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. + * + * <p>This does not affect ranking, but rather just attempts to make scores + * from different queries comparable. + * + * @param sumOfSquaredWeights the sum of the squares of query term weights + * @return a normalization factor for query weights + */ + virtual qreal queryNorm(qreal sumOfSquaredWeights) = 0; + + /** 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 {@link #tf(qreal)}. + * + * <p>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. + * + * @see PhraseQuery#setSlop(int32_t) + * @param distance the edit distance of this sloppy phrase match + * @return the frequency increment for this match + */ + virtual qreal sloppyFreq(int32_t distance) = 0; + + /** Computes a score factor based on a term or phrase's frequency in a + * document. This value is multiplied by the {@link #idf(Term, Searcher)} + * factor for each term in the query and these products are then summed to + * form the initial score for a document. + * + * <p>Terms and phrases repeated in a document indicate the topic of the + * document, so implemenations of this method usually return larger values + * when <code>freq</code> is large, and smaller values when <code>freq</code> + * is small. + * + * @param freq the frequency of a term within a document + * @return a score factor based on a term's within-document frequency + */ + virtual qreal tf(qreal freq) = 0; + + /** 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 + * {@link #tf(int32_t)} factor for each term in the query and these products are + * then summed to form the initial score for a document. + * + * <p>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. + * + * @param docFreq the number of documents which contain the term + * @param numDocs the total number of documents in the collection + * @return a score factor based on the term's document frequency + */ + virtual qreal idf(int32_t docFreq, int32_t numDocs) = 0; + + /** Computes a score factor based on the fraction of all query terms that a + * document contains. This value is multiplied into scores. + * + * <p>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. + * + * @param overlap the number of query terms matched in the document + * @param maxOverlap the total number of terms in the query + * @return a score factor based on term overlap with the query + */ + virtual qreal coord(int32_t overlap, int32_t maxOverlap) = 0; +}; + + +/** Expert: Default scoring implementation. */ +class DefaultSimilarity: public Similarity { +public: + DefaultSimilarity(); + ~DefaultSimilarity(); + + /** Implemented as <code>1/sqrt(numTerms)</code>. */ + qreal lengthNorm(const TCHAR* fieldName, int32_t numTerms); + + /** Implemented as <code>1/sqrt(sumOfSquaredWeights)</code>. */ + qreal queryNorm(qreal sumOfSquaredWeights); + + /** Implemented as <code>sqrt(freq)</code>. */ + inline qreal tf(qreal freq); + + /** Implemented as <code>1 / (distance + 1)</code>. */ + qreal sloppyFreq(int32_t distance); + + /** Implemented as <code>log(numDocs/(docFreq+1)) + 1</code>. */ + qreal idf(int32_t docFreq, int32_t numDocs); + + /** Implemented as <code>overlap / maxOverlap</code>. */ + qreal coord(int32_t overlap, int32_t maxOverlap); +}; + +CL_NS_END +#endif |