+++ /dev/null
-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</i>,
- * then derive from it <i>Lucene's Conceptual Scoring Formula</i>,
- * from which, finally, evolves <i>Lucene's Practical Scoring Function</i>
- * (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</a> values.
- *
- * <p>VSM does not require weights to be <i>Tf-idf</i> values,
- * but <i>Tf-idf</i> values are believed to produce search results of high quality,
- * and so Lucene is using <i>Tf-idf</i>.
- * <i>Tf</i> and <i>Idf</i> are described in more detail below,
- * but for now, for completion, let's just say that
- * for given term <i>t</i> and document (or query) <i>x</i>,
- * <i>Tf(t,x)</i> varies with the number of occurrences of term <i>t</i> in <i>x</i>
- * (when one increases so does the other) and
- * <i>idf(t)</i> similarly varies with the inverse of the
- * number of index documents containing term <i>t</i>.
- *
- * <p><i>VSM score</i> of document <i>d</i> for query <i>q</i> is the
- * <a href="http://en.wikipedia.org/wiki/Cosine_similarity">
- * Cosine Similarity</a>
- * of the weighted query vectors <i>V(q)</i> and <i>V(d)</i>:
- *
- * <br> <br>
- * <table cellpadding="2" cellspacing="2" border="0" align="center">
- * <tr><td>
- * <table cellpadding="1" cellspacing="0" border="1" align="center">
- * <tr><td>
- * <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><td align="center"><small>V(q) · V(d)</small></td></tr>
- * <tr><td align="center">–––––––––</td></tr>
- * <tr><td align="center"><small>|V(q)| |V(d)|</small></td></tr>
- * </table>
- * </td>
- * </tr>
- * </table>
- * </td></tr>
- * </table>
- * </td></tr>
- * <tr><td>
- * <center><font=-1><u>VSM Score</u></font></center>
- * </td></tr>
- * </table>
- * <br> <br>
- *
- *
- * Where <i>V(q)</i> · <i>V(d)</i> is the
- * <a href="http://en.wikipedia.org/wiki/Dot_product">dot product</a>
- * of the weighted vectors,
- * and <i>|V(q)|</i> and <i>|V(d)|</i> are their
- * <a href="http://en.wikipedia.org/wiki/Euclidean_norm#Euclidean_norm">Euclidean norms</a>.
- *
- * <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)</i> by its euclidean norm is normalizing it to a unit vector.
- *
- * <p>Lucene refines <i>VSM score</i> for both search quality and usability:
- * <ul>
- * <li>Normalizing <i>V(d)</i> 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</i> 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)</i>.
- * </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)</i>.
- * </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)</i>.
- * </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)</i>.
- * </li>
- * </ul>
- *
- * <p>Under the simplifying assumption of a single field in the index,
- * we get <i>Lucene's Conceptual scoring formula</i>:
- *
- * <br> <br>
- * <table cellpadding="2" cellspacing="2" border="0" align="center">
- * <tr><td>
- * <table cellpadding="1" cellspacing="0" border="1" align="center">
- * <tr><td>
- * <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> ·
- * <font color="#CCCC00">query-boost(q)</font> ·
- * </td>
- * <td valign="middle" align="center">
- * <table>
- * <tr><td align="center"><small><font color="#993399">V(q) · V(d)</font></small></td></tr>
- * <tr><td align="center">–––––––––</td></tr>
- * <tr><td align="center"><small><font color="#FF33CC">|V(q)|</font></small></td></tr>
- * </table>
- * </td>
- * <td valign="middle" align="right" rowspan="1">
- * · <font color="#3399FF">doc-len-norm(d)</font>
- * · <font color="#3399FF">doc-boost(d)</font>
- * </td>
- * </tr>
- * </table>
- * </td></tr>
- * </table>
- * </td></tr>
- * <tr><td>
- * <center><font=-1><u>Lucene Conceptual Scoring Formula</u></font></center>
- * </td></tr>
- * </table>
- * <br> <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</i>.
- *
- * <p>For efficient score computation some scoring components
- * are computed and aggregated in advance:
- *
- * <ul>
- * <li><i>Query-boost</i> for the query (actually for each query term)
- * is known when search starts.
- * </li>
- *
- * <li>Query Euclidean norm <i>|V(q)|</i> 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)|</i>,
- * 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</i>
- * for query <i>d1</i> is comparable to the score of document <i>d3</i>
- * for query <i>d2</i>. 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)</i>
- * 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 <i>doc-len-norm(d)</i> and document
- * boost <i>doc-boost(d)</i> 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)</i>.
- * (In the equations below, <i>norm(t in d)</i> means <i>norm(field(t) in doc d)</i>
- * where <i>field(t)</i> is the field associated with term <i>t</i>.)
- * </li>
- * </ul>
- *
- * <p><i>Lucene's Practical Scoring Function</i> is derived from the above.
- * The color codes demonstrate how it relates
- * to those of the <i>conceptual</i> formula:
- *
- * <P>
- * <table cellpadding="2" cellspacing="2" border="0" align="center">
- * <tr><td>
- * <table cellpadding="" cellspacing="2" border="2" align="center">
- * <tr><td>
- * <table cellpadding="2" cellspacing="2" border="0" align="center">
- * <tr>
- * <td valign="middle" align="right" rowspan="1">
- * score(q,d) =
- * <A HREF="#formula_coord"><font color="#FF9933">coord(q,d)</font></A> ·
- * <A HREF="#formula_queryNorm"><font color="#FF33CC">queryNorm(q)</font></A> ·
- * </td>
- * <td valign="bottom" align="center" rowspan="1">
- * <big><big><big>∑</big></big></big>
- * </td>
- * <td valign="middle" align="right" rowspan="1">
- * <big><big>(</big></big>
- * <A HREF="#formula_tf"><font color="#993399">tf(t in d)</font></A> ·
- * <A HREF="#formula_idf"><font color="#993399">idf(t)</font></A><sup>2</sup> ·
- * <A HREF="#formula_termBoost"><font color="#CCCC00">t.getBoost()</font></A> ·
- * <A HREF="#formula_norm"><font color="#3399FF">norm(t,d)</font></A>
- * <big><big>)</big></big>
- * </td>
- * </tr>
- * <tr valigh="top">
- * <td></td>
- * <td align="center"><small>t in q</small></td>
- * <td></td>
- * </tr>
- * </table>
- * </td></tr>
- * </table>
- * </td></tr>
- * <tr><td>
- * <center><font=-1><u>Lucene Practical Scoring Function</u></font></center>
- * </td></tr>
- * </table>
- *
- * <p> where
- * <ol>
- * <li>
- * <A NAME="formula_tf"></A>
- * <b><i>tf(t in d)</i></b>
- * correlates to the term's <i>frequency</i>,
- * defined as the number of times term <i>t</i> appears in the currently scored document <i>d</i>.
- * Documents that have more occurrences of a given term receive a higher score.
- * Note that <i>tf(t in q)</i> is assumed to be <i>1</i> 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)</i> in
- * {@link org.apache.lucene.search.DefaultSimilarity#tf(float) DefaultSimilarity} is:
- *
- * <br> <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><big>½</big></sup>
- * </td>
- * </tr>
- * </table>
- * <br> <br>
- * </li>
- *
- * <li>
- * <A NAME="formula_idf"></A>
- * <b><i>idf(t)</i></b> stands for Inverse Document Frequency. This value
- * correlates to the inverse of <i>docFreq</i>
- * (the number of documents in which the term <i>t</i> appears).
- * This means rarer terms give higher contribution to the total score.
- * <i>idf(t)</i> appears for <i>t</i> in both the query and the document,
- * hence it is squared in the equation.
- * The default computation for <i>idf(t)</i> in
- * {@link org.apache.lucene.search.DefaultSimilarity#idf(int, int) DefaultSimilarity} is:
- *
- * <br> <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>(</big>
- * </td>
- * <td valign="middle" align="center">
- * <table>
- * <tr><td align="center"><small>numDocs</small></td></tr>
- * <tr><td align="center">–––––––––</td></tr>
- * <tr><td align="center"><small>docFreq+1</small></td></tr>
- * </table>
- * </td>
- * <td valign="middle" align="center">
- * <big>)</big>
- * </td>
- * </tr>
- * </table>
- * <br> <br>
- * </li>
- *
- * <li>
- * <A NAME="formula_coord"></A>
- * <b><i>coord(q,d)</i></b>
- * 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> <br>
- * </li>
- *
- * <li><b>
- * <A NAME="formula_queryNorm"></A>
- * <i>queryNorm(q)</i>
- * </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</a>:
- * <br> <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><td align="center"><big>1</big></td></tr>
- * <tr><td align="center"><big>
- * ––––––––––––––
- * </big></td></tr>
- * <tr><td align="center">sumOfSquaredWeights<sup><big>½</big></sup></td></tr>
- * </table>
- * </td>
- * </tr>
- * </table>
- * <br> <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> <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><big>2</big></sup>
- * ·
- * </td>
- * <td valign="bottom" align="center" rowspan="1">
- * <big><big><big>∑</big></big></big>
- * </td>
- * <td valign="middle" align="right" rowspan="1">
- * <big><big>(</big></big>
- * <A HREF="#formula_idf">idf(t)</A> ·
- * <A HREF="#formula_termBoost">t.getBoost()</A>
- * <big><big>) <sup>2</sup> </big></big>
- * </td>
- * </tr>
- * <tr valigh="top">
- * <td></td>
- * <td align="center"><small>t in q</small></td>
- * <td></td>
- * </tr>
- * </table>
- * <br> <br>
- *
- * </li>
- *
- * <li>
- * <A NAME="formula_termBoost"></A>
- * <b><i>t.getBoost()</i></b>
- * is a search time boost of term <i>t</i> in the query <i>q</i> as
- * specified in the query text
- * (see <A HREF="../../../../../../queryparsersyntax.html#Boosting a Term">query syntax</A>),
- * 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> <br>
- * </li>
- *
- * <li>
- * <A NAME="formula_norm"></A>
- * <b><i>norm(t,d)</i></b> encapsulates a few (indexing time) boost and length factors:
- *
- * <ul>
- * <li><b>Document boost</b> - set by calling
- * {@link org.apache.lucene.document.Document#setBoost(float) doc.setBoost()}
- * before adding the document to the index.
- * </li>
- * <li><b>Field boost</b> - set by calling
- * {@link org.apache.lucene.document.Fieldable#setBoost(float) field.setBoost()}
- * before adding the field to a document.
- * </li>
- * <li><b>lengthNorm</b> - 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> <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><big><big>∏</big></big></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>
- * <td align="center"><small>field <i><b>f</b></i> in <i>d</i> named as <i><b>t</b></i></small></td>
- * <td></td>
- * </tr>
- * </table>
- * <br> <br>
- * However the resulted <i>norm</i> 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</i> 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</i>.
- * For instance, <i>decode(encode(0.89)) = 0.75</i>.
- * <br> <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> <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> <br>
- * Last, note that search time is too late to modify this <i>norm</i> part of scoring, e.g. by
- * using a different {@link Similarity} for search.
- * <br> <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()</code> is large,
- * and larger values when <code>state.getLength()</code> 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</code> is large,
- * and larger values when <code>numTokens</code> 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</i> of <i>doc</i>.
- * @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</code> is large, and smaller values when <code>freq</code>
- * 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</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
- */
- 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)</code> 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;
- }
-
-}
fnp / pylucene.git / blobdiff