--- /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