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* <tr> * <tr> * </table> * </td> * </tr> * </table> * </td> * </table> * </td> * <tr> * </table> * <br> 
* * * Where <i>V(q) · V(d) is the * <a href="http://en.wikipedia.org/wiki/Dot_product">dot product * of the weighted vectors, * and <i>|V(q)| and |V(d)| are their * <a href="http://en.wikipedia.org/wiki/Euclidean_norm#Euclidean_norm">Euclidean norms. * * <p>Note: the above equation can be viewed as the dot product of * the normalized weighted vectors, in the sense that dividing * <i>V(q) by its euclidean norm is normalizing it to a unit vector. * * <p>Lucene refines VSM score for both search quality and usability: * <ul> * <li>Normalizing V(d) to the unit vector is known to be problematic in that * it removes all document length information. * For some documents removing this info is probably ok, * e.g. a document made by duplicating a certain paragraph <i>10 times, * especially if that paragraph is made of distinct terms. * But for a document which contains no duplicated paragraphs, * this might be wrong. * To avoid this problem, a different document length normalization * factor is used, which normalizes to a vector equal to or larger * than the unit vector: <i>doc-len-norm(d). * </li> * * <li>At indexing, users can specify that certain documents are more * important than others, by assigning a document boost. * For this, the score of each document is also multiplied by its boost value * <i>doc-boost(d). * </li> * * <li>Lucene is field based, hence each query term applies to a single * field, document length normalization is by the length of the certain field, * and in addition to document boost there are also document fields boosts. * </li> * * <li>The same field can be added to a document during indexing several times, * and so the boost of that field is the multiplication of the boosts of * the separate additions (or parts) of that field within the document. * </li> * * <li>At search time users can specify boosts to each query, sub-query, and * each query term, hence the contribution of a query term to the score of * a document is multiplied by the boost of that query term <i>query-boost(q). * </li> * * <li>A document may match a multi term query without containing all * the terms of that query (this is correct for some of the queries), * and users can further reward documents matching more query terms * through a coordination factor, which is usually larger when * more terms are matched: <i>coord-factor(q,d). * </li> * </ul> * * <p>Under the simplifying assumption of a single field in the index, * we get <i>Lucene's Conceptual scoring formula: * * <br> 
* <table cellpadding="2" cellspacing="2" border="0" align="center"> * <tr> * <tr> * <tr> * </table> * </td> * <td valign="middle" align="right" rowspan="1"> *   ·   <font color="#3399FF">doc-len-norm(d) *   ·   <font color="#3399FF">doc-boost(d) * </td> * </tr> * </table> * </td> * </table> * </td> * <tr> * </table> * <br> 
* * <p>The conceptual formula is a simplification in the sense that (1) terms and documents * are fielded and (2) boosts are usually per query term rather than per query. * * <p>We now describe how Lucene implements this conceptual scoring formula, and * derive from it <i>Lucene's Practical Scoring Function. * * <p>For efficient score computation some scoring components * are computed and aggregated in advance: * * <ul> * <li>Query-boost for the query (actually for each query term) * is known when search starts. * </li> * * <li>Query Euclidean norm |V(q)| can be computed when search starts, * as it is independent of the document being scored. * From search optimization perspective, it is a valid question * why bother to normalize the query at all, because all * scored documents will be multiplied by the same <i>|V(q)|, * and hence documents ranks (their order by score) will not * be affected by this normalization. * There are two good reasons to keep this normalization: * <ul> * <li>Recall that * <a href="http://en.wikipedia.org/wiki/Cosine_similarity"> * Cosine Similarity</a> can be used find how similar * two documents are. One can use Lucene for e.g. * clustering, and use a document as a query to compute * its similarity to other documents. * In this use case it is important that the score of document <i>d3 * for query <i>d1 is comparable to the score of document d3 * for query <i>d2. In other words, scores of a document for two * distinct queries should be comparable. * There are other applications that may require this. * And this is exactly what normalizing the query vector <i>V(q) * provides: comparability (to a certain extent) of two or more queries. * </li> * * <li>Applying query normalization on the scores helps to keep the * scores around the unit vector, hence preventing loss of score data * because of floating point precision limitations. * </li> * </ul> * </li> * * <li>Document length norm doc-len-norm(d) and document * boost <i>doc-boost(d) are known at indexing time. * They are computed in advance and their multiplication * is saved as a single value in the index: <i>norm(d). * (In the equations below, <i>norm(t in d) means norm(field(t) in doc d) * where <i>field(t) is the field associated with term t.) * </li> * </ul> * * <p>Lucene's Practical Scoring Function is derived from the above. * The color codes demonstrate how it relates * to those of the <i>conceptual formula: * * <P> * <table cellpadding="2" cellspacing="2" border="0" align="center"> * <tr> * <td align="center">t in q * <td> * </tr> * </table> * </td> * </table> * </td> * <tr> * </table> * * <p> where * <ol> * <li> * <A NAME="formula_tf"> * <b>tf(t in d) * correlates to the term's <i>frequency, * defined as the number of times term <i>t appears in the currently scored document d. * Documents that have more occurrences of a given term receive a higher score. * Note that <i>tf(t in q) is assumed to be 1 and therefore it does not appear in this equation, * However if a query contains twice the same term, there will be * two term-queries with that same term and hence the computation would still be correct (although * not very efficient). * The default computation for <i>tf(t in d) in * {@link org.apache.lucene.search.DefaultSimilarity#tf(float) DefaultSimilarity} is: * * <br> 
* <table cellpadding="2" cellspacing="2" border="0" align="center"> * <tr> * <td valign="middle" align="right" rowspan="1"> * {@link org.apache.lucene.search.DefaultSimilarity#tf(float) tf(t in d)}   =   * </td> * <td valign="top" align="center" rowspan="1"> * frequency<sup>½ * </td> * </tr> * </table> * <br> 
* </li> * * <li> * <A NAME="formula_idf"> * <b>idf(t) stands for Inverse Document Frequency. This value * correlates to the inverse of <i>docFreq * (the number of documents in which the term <i>t appears). * This means rarer terms give higher contribution to the total score. * <i>idf(t) appears for t in both the query and the document, * hence it is squared in the equation. * The default computation for <i>idf(t) in * {@link org.apache.lucene.search.DefaultSimilarity#idf(int, int) DefaultSimilarity} is: * * <br> 
* <table cellpadding="2" cellspacing="2" border="0" align="center"> * <tr> * <td valign="middle" align="right"> * {@link org.apache.lucene.search.DefaultSimilarity#idf(int, int) idf(t)}  =   * </td> * <td valign="middle" align="center"> * 1 + log <big>( * </td> * <td valign="middle" align="center"> * <table> * <tr> * <tr> * <tr> * </table> * </td> * <td valign="middle" align="center"> * <big>) * </td> * </tr> * </table> * <br> 
* </li> * * <li> * <A NAME="formula_coord"> * <b>coord(q,d) * is a score factor based on how many of the query terms are found in the specified document. * Typically, a document that contains more of the query's terms will receive a higher score * than another document with fewer query terms. * This is a search time factor computed in * {@link #coord(int, int) coord(q,d)} * by the Similarity in effect at search time. * <br> 
* </li> * * <li> * <A NAME="formula_queryNorm"> * <i>queryNorm(q) * </b> * is a normalizing factor used to make scores between queries comparable. * This factor does not affect document ranking (since all ranked documents are multiplied by the same factor), * but rather just attempts to make scores from different queries (or even different indexes) comparable. * This is a search time factor computed by the Similarity in effect at search time. * * The default computation in * {@link org.apache.lucene.search.DefaultSimilarity#queryNorm(float) DefaultSimilarity} * produces a <a href="http://en.wikipedia.org/wiki/Euclidean_norm#Euclidean_norm">Euclidean norm: * <br> 
* <table cellpadding="1" cellspacing="0" border="0" align="center"> * <tr> * <td valign="middle" align="right" rowspan="1"> * queryNorm(q)   =   * {@link org.apache.lucene.search.DefaultSimilarity#queryNorm(float) queryNorm(sumOfSquaredWeights)} *   =   * </td> * <td valign="middle" align="center" rowspan="1"> * <table> * <tr>
* <tr> * <tr> * </table> * </td> * </tr> * </table> * <br> 
* * The sum of squared weights (of the query terms) is * computed by the query {@link org.apache.lucene.search.Weight} object. * For example, a {@link org.apache.lucene.search.BooleanQuery} * computes this value as: * * <br> 
* <table cellpadding="1" cellspacing="0" border="0"n align="center"> * <tr> * <td valign="middle" align="right" rowspan="1"> * {@link org.apache.lucene.search.Weight#sumOfSquaredWeights() sumOfSquaredWeights}   =   * {@link org.apache.lucene.search.Query#getBoost() q.getBoost()} <sup>2 *  ·  * </td> * <td valign="bottom" align="center" rowspan="1"> * <big> * </td> * <td valign="middle" align="right" rowspan="1"> * <big>( * <A HREF="#formula_idf">idf(t)  ·  * <A HREF="#formula_termBoost">t.getBoost() * <big>) 2 * </td> * </tr> * <tr valigh="top"> * <td> * <td align="center">t in q * <td> * </tr> * </table> * <br> 
* * </li> * * <li> * <A NAME="formula_termBoost"> * <b>t.getBoost()
* is a search time boost of term <i>t in the query q as * specified in the query text * (see <A HREF="../../../../../../queryparsersyntax.html#Boosting a Term">query syntax), * or as set by application calls to * {@link org.apache.lucene.search.Query#setBoost(float) setBoost()}. * Notice that there is really no direct API for accessing a boost of one term in a multi term query, * but rather multi terms are represented in a query as multi * {@link org.apache.lucene.search.TermQuery TermQuery} objects, * and so the boost of a term in the query is accessible by calling the sub-query * {@link org.apache.lucene.search.Query#getBoost() getBoost()}. * <br> 
* </li> * * <li> * <A NAME="formula_norm"> * <b>norm(t,d) encapsulates a few (indexing time) boost and length factors: * * <ul> * <li>Document boost - set by calling * {@link org.apache.lucene.document.Document#setBoost(float) doc.setBoost()} * before adding the document to the index. * </li> * <li>Field boost - set by calling * {@link org.apache.lucene.document.Fieldable#setBoost(float) field.setBoost()} * before adding the field to a document. * </li> * <li>lengthNorm - computed * when the document is added to the index in accordance with the number of tokens * of this field in the document, so that shorter fields contribute more to the score. * LengthNorm is computed by the Similarity class in effect at indexing. * </li> * </ul> * The {@link #computeNorm} method is responsible for * combining all of these factors into a single float. * * <p> * When a document is added to the index, all the above factors are multiplied. * If the document has multiple fields with the same name, all their boosts are multiplied together: * * <br> 
* <table cellpadding="1" cellspacing="0" border="0"n align="center"> * <tr> * <td valign="middle" align="right" rowspan="1"> * norm(t,d)   =   * {@link org.apache.lucene.document.Document#getBoost() doc.getBoost()} *  ·  * lengthNorm *  ·  * </td> * <td valign="bottom" align="center" rowspan="1"> * <big> * </td> * <td valign="middle" align="right" rowspan="1"> * {@link org.apache.lucene.document.Fieldable#getBoost() f.getBoost}() * </td> * </tr> * <tr valigh="top"> * <td> * <td align="center">field f in d named as t * <td> * </tr> * </table> * <br> 
* However the resulted <i>norm value is {@link #encodeNormValue(float) encoded} as a single byte * before being stored. * At search time, the norm byte value is read from the index * {@link org.apache.lucene.store.Directory directory} and * {@link #decodeNormValue(byte) decoded} back to a float <i>norm value. * This encoding/decoding, while reducing index size, comes with the price of * precision loss - it is not guaranteed that <i>decode(encode(x)) = x. * For instance, <i>decode(encode(0.89)) = 0.75. * <br> 
* Compression of norm values to a single byte saves memory at search time, * because once a field is referenced at search time, its norms - for * all documents - are maintained in memory. * <br> 
* The rationale supporting such lossy compression of norm values is that * given the difficulty (and inaccuracy) of users to express their true information * need by a query, only big differences matter. * <br> 
* Last, note that search time is too late to modify this <i>norm part of scoring, e.g. by * using a different {@link Similarity} for search. * <br> 
* </li> * </ol> * * @see #setDefault(Similarity) * @see org.apache.lucene.index.IndexWriter#setSimilarity(Similarity) * @see Searcher#setSimilarity(Similarity) */ public abstract class Similarity implements Serializable { // NOTE: this static code must precede setting the static defaultImpl: private static final VirtualMethod<Similarity> withoutDocFreqMethod = new VirtualMethod<Similarity>(Similarity.class, "idfExplain", Term.class, Searcher.class); private static final VirtualMethod<Similarity> withDocFreqMethod = new VirtualMethod<Similarity>(Similarity.class, "idfExplain", Term.class, Searcher.class, int.class); private final boolean hasIDFExplainWithDocFreqAPI = VirtualMethod.compareImplementationDistance(getClass(), withDocFreqMethod, withoutDocFreqMethod) >= 0; // its ok for both to be overridden /** * The Similarity implementation used by default. **/ private static Similarity defaultImpl = new DefaultSimilarity(); public static final int NO_DOC_ID_PROVIDED = -1; /** Set the default Similarity implementation used by indexing and search * code. * * @see Searcher#setSimilarity(Similarity) * @see org.apache.lucene.index.IndexWriter#setSimilarity(Similarity) */ public static void setDefault(Similarity similarity) { Similarity.defaultImpl = 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 org.apache.lucene.index.IndexWriter#setSimilarity(Similarity) */ public static Similarity getDefault() { return Similarity.defaultImpl; } /** Cache of decoded bytes. */ private static final float[] NORM_TABLE = new float[256]; static { for (int i = 0; i < 256; i++) NORM_TABLE[i] = SmallFloat.byte315ToFloat((byte)i); } /** * Decodes a normalization factor stored in an index. * @see #decodeNormValue(byte) * @deprecated Use {@link #decodeNormValue} instead. */ @Deprecated public static float decodeNorm(byte b) { return NORM_TABLE[b & 0xFF]; // & 0xFF maps negative bytes to positive above 127 } /** Decodes a normalization factor stored in an index. * <p> * <b>WARNING: If you override this method, you should change the default * Similarity to your implementation with {@link Similarity#setDefault(Similarity)}. * Otherwise, your method may not always be called, especially if you omit norms * for some fields.</b> * @see #encodeNormValue(float) */ public float decodeNormValue(byte b) { return NORM_TABLE[b & 0xFF]; // & 0xFF maps negative bytes to positive above 127 } /** Returns a table for decoding normalization bytes. * @see #encodeNormValue(float) * @see #decodeNormValue(byte) * * @deprecated Use instance methods for encoding/decoding norm values to enable customization. */ @Deprecated public static float[] getNormDecoder() { return NORM_TABLE; } /** * Computes the normalization value for a field, given the accumulated * state of term processing for this field (see {@link FieldInvertState}). * * <p>Implementations should calculate a float value based on the field * state and then return that value. * * <p>Matches in longer fields are less precise, so implementations of this * method usually return smaller values when <code>state.getLength() is large, * and larger values when <code>state.getLength() is small. * * <p>Note that the return values are computed under * {@link org.apache.lucene.index.IndexWriter#addDocument(org.apache.lucene.document.Document)} * and then stored using * {@link #encodeNormValue(float)}. * Thus they have limited precision, and documents * must be re-indexed if this method is altered. * * <p>For backward compatibility this method by default calls * {@link #lengthNorm(String, int)} passing * {@link FieldInvertState#getLength()} as the second argument, and * then multiplies this value by {@link FieldInvertState#getBoost()}.</p> * * @lucene.experimental * * @param field field name * @param state current processing state for this field * @return the calculated float norm */ public abstract float computeNorm(String field, FieldInvertState state); /** 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 implementations of this * method usually return smaller values when <code>numTokens is large, * and larger values when <code>numTokens is small. * * <p>Note that the return values are computed under * {@link org.apache.lucene.index.IndexWriter#addDocument(org.apache.lucene.document.Document)} * and then stored using * {@link #encodeNormValue(float)}. * 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 of doc. * @return a normalization factor for hits on this field of this document * * @see org.apache.lucene.document.Field#setBoost(float) * * @deprecated Please override computeNorm instead */ @Deprecated public final float lengthNorm(String fieldName, int numTokens) { throw new UnsupportedOperationException("please use computeNorm instead"); } /** Computes the normalization value for a query given the sum of the squared * weights of each of the query terms. This value is multiplied into the * weight of each query term. While the classic query normalization factor is * computed as 1/sqrt(sumOfSquaredWeights), other implementations might * completely ignore sumOfSquaredWeights (ie return 1). * * <p>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. * * @param sumOfSquaredWeights the sum of the squares of query term weights * @return a normalization factor for query weights */ public abstract float queryNorm(float sumOfSquaredWeights); /** Encodes a normalization factor for storage in an index. * * <p>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. * <p> * <b>WARNING: If you override this method, you should change the default * Similarity to your implementation with {@link Similarity#setDefault(Similarity)}. * Otherwise, your method may not always be called, especially if you omit norms * for some fields.</b> * @see org.apache.lucene.document.Field#setBoost(float) * @see org.apache.lucene.util.SmallFloat */ public byte encodeNormValue(float f) { return SmallFloat.floatToByte315(f); } /** * Static accessor kept for backwards compability reason, use encodeNormValue instead. * @param f norm-value to encode * @return byte representing the given float * @deprecated Use {@link #encodeNormValue} instead. * * @see #encodeNormValue(float) */ @Deprecated public static byte encodeNorm(float f) { return SmallFloat.floatToByte315(f); } /** Computes a score factor based on a term or phrase's frequency in a * document. This value is multiplied by the {@link #idf(int, int)} * 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 is large, and smaller values when freq * is small. * * <p>The default implementation calls {@link #tf(float)}. * * @param freq the frequency of a term within a document * @return a score factor based on a term's within-document frequency */ public float tf(int freq) { return tf((float)freq); } /** 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(float)}. * * <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(int) * @param distance the edit distance of this sloppy phrase match * @return the frequency increment for this match */ public abstract float sloppyFreq(int distance); /** Computes a score factor based on a term or phrase's frequency in a * document. This value is multiplied by the {@link #idf(int, int)} * 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 is large, and smaller values when freq * is small. * * @param freq the frequency of a term within a document * @return a score factor based on a term's within-document frequency */ public abstract float tf(float freq); /** * Computes a score factor for a simple term and returns an explanation * for that score factor. * * <p> * The default implementation uses: * * <pre> * idf(searcher.docFreq(term), searcher.maxDoc()); * </pre> * * Note that {@link Searcher#maxDoc()} is used instead of * {@link org.apache.lucene.index.IndexReader#numDocs() IndexReader#numDocs()} because also * {@link Searcher#docFreq(Term)} is used, and when the latter * is inaccurate, so is {@link Searcher#maxDoc()}, and in the same direction. * In addition, {@link Searcher#maxDoc()} is more efficient to compute * * @param term the term in question * @param searcher the document collection being searched * @return an IDFExplain object that includes both an idf score factor and an explanation for the term. * @throws IOException */ public IDFExplanation idfExplain(final Term term, final Searcher searcher, int docFreq) throws IOException { if (!hasIDFExplainWithDocFreqAPI) { // Fallback to slow impl return idfExplain(term, searcher); } final int df = docFreq; final int max = searcher.maxDoc(); final float idf = idf(df, max); return new IDFExplanation() { @Override public String explain() { return "idf(docFreq=" + df + ", maxDocs=" + max + ")"; } @Override public float getIdf() { return idf; }}; } /** * This method forwards to {@link * #idfExplain(Term,Searcher,int)} by passing * <code>searcher.docFreq(term) as the docFreq. * * WARNING: if you subclass Similariary and override this * method then you may hit a peformance hit for certain * queries. Better to override {@link * #idfExplain(Term,Searcher,int)} instead. */ public IDFExplanation idfExplain(final Term term, final Searcher searcher) throws IOException { return idfExplain(term, searcher, searcher.docFreq(term)); } /** * Computes a score factor for a phrase. * * <p> * The default implementation sums the idf factor for * each term in the phrase. * * @param terms the terms in the phrase * @param searcher the document collection being searched * @return an IDFExplain object that includes both an idf * score factor for the phrase and an explanation * for each term. * @throws IOException */ public IDFExplanation idfExplain(Collection<Term> terms, Searcher searcher) throws IOException { final int max = searcher.maxDoc(); float idf = 0.0f; final StringBuilder exp = new StringBuilder(); for (final Term term : terms ) { final int df = searcher.docFreq(term); idf += idf(df, max); exp.append(" "); exp.append(term.text()); exp.append("="); exp.append(df); } final float fIdf = idf; return new IDFExplanation() { @Override public float getIdf() { return fIdf; } @Override public String explain() { return exp.toString(); } }; } /** 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(int)} 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 * implementations 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 */ public abstract float idf(int docFreq, int numDocs); /** 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 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. * * @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 */ public abstract float coord(int overlap, int maxOverlap); /** * 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. * <p> * The default implementation returns 1. * * @param docId The docId currently being scored. If this value is {@link #NO_DOC_ID_PROVIDED}, then it should be assumed that the PayloadQuery implementation does not provide document information * @param fieldName The fieldName of the term this payload belongs to * @param start The start position of the payload * @param end The end position of the payload * @param payload The payload byte array to be scored * @param offset The offset into the payload array * @param length The length in the array * @return An implementation dependent float to be used as a scoring factor * */ public float scorePayload(int docId, String fieldName, int start, int end, byte [] payload, int offset, int length) { return 1; } }

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Lucene example source code file (Similarity.java)

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Java - Lucene tags/keywords

deprecated, deprecated, idfexplanation, idfexplanation, io, ioexception, norm_table, norm_table, override, override, searcher, similarity, string, term, util, virtualmethod

The Lucene Similarity.java source code

package org.apache.lucene.search;

/**
 * Licensed to the Apache Software Foundation (ASF) under one or more
 * contributor license agreements.  See the NOTICE file distributed with
 * this work for additional information regarding copyright ownership.
 * The ASF licenses this file to You under the Apache License, Version 2.0
 * (the "License"); you may not use this file except in compliance with
 * the License.  You may obtain a copy of the License at
 *
 *     http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 */


import org.apache.lucene.index.FieldInvertState;
import org.apache.lucene.index.Term;
import org.apache.lucene.search.Explanation.IDFExplanation;
import org.apache.lucene.util.SmallFloat;
import org.apache.lucene.util.VirtualMethod;

import java.io.IOException;
import java.io.Serializable;
import java.util.Collection;


/** 
 * Expert: Scoring API.
 *
 * <p>Similarity defines the components of Lucene scoring.
 * Overriding computation of these components is a convenient
 * way to alter Lucene scoring.
 *
 * <p>Suggested reading:
 * <a href="http://nlp.stanford.edu/IR-book/html/htmledition/queries-as-vectors-1.html">
 * Introduction To Information Retrieval, Chapter 6</a>.
 *
 * <p>The following describes how Lucene scoring evolves from
 * underlying information retrieval models to (efficient) implementation.
 * We first brief on <i>VSM Score, 
 * then derive from it <i>Lucene's Conceptual Scoring Formula,
 * from which, finally, evolves <i>Lucene's Practical Scoring Function 
 * (the latter is connected directly with Lucene classes and methods).    
 *
 * <p>Lucene combines
 * <a href="http://en.wikipedia.org/wiki/Standard_Boolean_model">
 * Boolean model (BM) of Information Retrieval</a>
 * with
 * <a href="http://en.wikipedia.org/wiki/Vector_Space_Model">
 * Vector Space Model (VSM) of Information Retrieval</a> -
 * documents "approved" by BM are scored by VSM.
 *
 * <p>In VSM, documents and queries are represented as
 * weighted vectors in a multi-dimensional space,
 * where each distinct index term is a dimension,
 * and weights are
 * <a href="http://en.wikipedia.org/wiki/Tfidf">Tf-idf values.
 *
 * <p>VSM does not require weights to be Tf-idf values,
 * but <i>Tf-idf values are believed to produce search results of high quality,
 * and so Lucene is using <i>Tf-idf.
 * <i>Tf and Idf are described in more detail below,
 * but for now, for completion, let's just say that
 * for given term <i>t and document (or query) x,
 * <i>Tf(t,x) varies with the number of occurrences of term t in x
 * (when one increases so does the other) and
 * <i>idf(t) similarly varies with the inverse of the
 * number of index documents containing term <i>t.
 *
 * <p>VSM score of document d for query q is the
 * <a href="http://en.wikipedia.org/wiki/Cosine_similarity">
 * Cosine Similarity</a>
 * of the weighted query vectors <i>V(q) and V(d):
 *
 *  <br> 
* <table cellpadding="2" cellspacing="2" border="0" align="center"> * <tr>
* <table cellpadding="1" cellspacing="0" border="1" align="center"> * <tr> * <table cellpadding="2" cellspacing="2" border="0" align="center"> * <tr> * <td valign="middle" align="right" rowspan="1"> * cosine-similarity(q,d)   =   * </td> * <td valign="middle" align="center"> * <table> * <tr>V(q) · V(d)
–––––––––
|V(q)| |V(d)|
* <center>VSM Score * </td>
* <table cellpadding="1" cellspacing="0" border="1" align="center"> * <tr> * <table cellpadding="2" cellspacing="2" border="0" align="center"> * <tr> * <td valign="middle" align="right" rowspan="1"> * score(q,d)   =   * <font color="#FF9933">coord-factor(q,d) ·   * <font color="#CCCC00">query-boost(q) ·   * </td> * <td valign="middle" align="center"> * <table> * <tr>V(q) · V(d)
–––––––––
|V(q)|
* <center>Lucene Conceptual Scoring Formula * </td>
* <table cellpadding="" cellspacing="2" border="2" align="center"> * <tr> * <table cellpadding="2" cellspacing="2" border="0" align="center"> * <tr> * <td valign="middle" align="right" rowspan="1"> * score(q,d)   =   * <A HREF="#formula_coord">coord(q,d)  ·  * <A HREF="#formula_queryNorm">queryNorm(q)  ·  * </td> * <td valign="bottom" align="center" rowspan="1"> * <big> * </td> * <td valign="middle" align="right" rowspan="1"> * <big>( * <A HREF="#formula_tf">tf(t in d)  ·  * <A HREF="#formula_idf">idf(t)2  ·  * <A HREF="#formula_termBoost">t.getBoost() ·  * <A HREF="#formula_norm">norm(t,d) * <big>) * </td> * </tr> * <tr valigh="top"> * <td>
* <center>Lucene Practical Scoring Function * </td>
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docFreq+1
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* –––––––––––––– * </big>
sumOfSquaredWeights½


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