| /* |
| * 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. |
| */ |
| package org.apache.lucene.search.similarities; |
| |
| |
| import java.util.ArrayList; |
| import java.util.List; |
| |
| import org.apache.lucene.index.FieldInvertState; |
| import org.apache.lucene.index.IndexOptions; |
| import org.apache.lucene.search.CollectionStatistics; |
| import org.apache.lucene.search.Explanation; |
| import org.apache.lucene.search.IndexSearcher; |
| import org.apache.lucene.search.TermStatistics; |
| import org.apache.lucene.util.SmallFloat; |
| |
| |
| /** |
| * Implementation of {@link Similarity} with the Vector Space Model. |
| * <p> |
| * Expert: Scoring API. |
| * <p>TFIDFSimilarity 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" style="width:auto; margin-left:auto; margin-right:auto" summary="formatting only"> |
| * <tr><td> |
| * <table cellpadding="1" cellspacing="0" border="1" style="margin-left:auto; margin-right:auto" summary="formatting only"> |
| * <tr><td> |
| * <table cellpadding="2" cellspacing="2" border="0" style="margin-left:auto; margin-right:auto" summary="cosine similarity formula"> |
| * <tr> |
| * <td style="vertical-align: middle; text-align: right" rowspan="1"> |
| * cosine-similarity(q,d) = |
| * </td> |
| * <td style="vertical-align: middle; text-align: center"> |
| * <table> |
| * <caption>cosine similarity formula</caption> |
| * <tr><td style="text-align: center"><small>V(q) · V(d)</small></td></tr> |
| * <tr><td style="text-align: center">–––––––––</td></tr> |
| * <tr><td style="text-align: center"><small>|V(q)| |V(d)|</small></td></tr> |
| * </table> |
| * </td> |
| * </tr> |
| * </table> |
| * </td></tr> |
| * </table> |
| * </td></tr> |
| * <tr><td> |
| * <center><u>VSM Score</u></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). |
| * </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" style="width:auto; margin-left:auto; margin-right:auto" summary="formatting only"> |
| * <tr><td> |
| * <table cellpadding="1" cellspacing="0" border="1" style="margin-left:auto; margin-right:auto" summary="formatting only"> |
| * <tr><td> |
| * <table cellpadding="2" cellspacing="2" border="0" style="margin-left:auto; margin-right:auto" summary="formatting only"> |
| * <tr> |
| * <td style="vertical-align: middle; text-align: right" rowspan="1"> |
| * score(q,d) = |
| * <span style="color: #CCCC00">query-boost(q)</span> · |
| * </td> |
| * <td style="vertical-align: middle; text-align: center"> |
| * <table> |
| * <caption>Lucene conceptual scoring formula</caption> |
| * <tr><td style="text-align: center"><small><span style="color: #993399">V(q) · V(d)</span></small></td></tr> |
| * <tr><td style="text-align: center">–––––––––</td></tr> |
| * <tr><td style="text-align: center"><small><span style="color: #FF33CC">|V(q)|</span></small></td></tr> |
| * </table> |
| * </td> |
| * <td style="vertical-align: middle; text-align: right" rowspan="1"> |
| * · <span style="color: #3399FF">doc-len-norm(d)</span> |
| * · <span style="color: #3399FF">doc-boost(d)</span> |
| * </td> |
| * </tr> |
| * </table> |
| * </td></tr> |
| * </table> |
| * </td></tr> |
| * <tr><td> |
| * <center><u>Lucene Conceptual Scoring Formula</u></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> |
| * </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: |
| * |
| * <table cellpadding="2" cellspacing="2" border="0" style="width:auto; margin-left:auto; margin-right:auto" summary="formatting only"> |
| * <tr><td> |
| * <table cellpadding="" cellspacing="2" border="2" style="margin-left:auto; margin-right:auto" summary="formatting only"> |
| * <tr><td> |
| * <table cellpadding="2" cellspacing="2" border="0" style="margin-left:auto; margin-right:auto" summary="Lucene conceptual scoring formula"> |
| * <tr> |
| * <td style="vertical-align: middle; text-align: right" rowspan="1"> |
| * score(q,d) = |
| * <big><big><big>∑</big></big></big> |
| * </td> |
| * <td style="vertical-align: middle; text-align: right" rowspan="1"> |
| * <span style="font-size: larger">(</span> |
| * <A HREF="#formula_tf"><span style="color: #993399">tf(t in d)</span></A> · |
| * <A HREF="#formula_idf"><span style="color: #993399">idf(t)</span></A><sup>2</sup> · |
| * <A HREF="#formula_termBoost"><span style="color: #CCCC00">t.getBoost()</span></A> · |
| * <A HREF="#formula_norm"><span style="color: #3399FF">norm(t,d)</span></A> |
| * <big><big>)</big></big> |
| * </td> |
| * </tr> |
| * <tr style="vertical-align: top"> |
| * <td></td> |
| * <td align="center" style="text-align: center"><small>t in q</small></td> |
| * <td></td> |
| * </tr> |
| * </table> |
| * </td></tr> |
| * </table> |
| * </td></tr> |
| * <tr><td> |
| * <center><u>Lucene Practical Scoring Function</u></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.similarities.ClassicSimilarity#tf(float) ClassicSimilarity} is: |
| * |
| * <br> <br> |
| * <table cellpadding="2" cellspacing="2" border="0" style="width:auto; margin-left:auto; margin-right:auto" summary="term frequency computation"> |
| * <tr> |
| * <td style="vertical-align: middle; text-align: right" rowspan="1"> |
| * {@link org.apache.lucene.search.similarities.ClassicSimilarity#tf(float) tf(t in d)} = |
| * </td> |
| * <td style="vertical-align: top; text-align: center" rowspan="1"> |
| * frequency<sup><span style="font-size: larger">½</span></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.similarities.ClassicSimilarity#idf(long, long) ClassicSimilarity} is: |
| * |
| * <br> <br> |
| * <table cellpadding="2" cellspacing="2" border="0" style="width:auto; margin-left:auto; margin-right:auto" summary="inverse document frequency computation"> |
| * <tr> |
| * <td style="vertical-align: middle; text-align: right"> |
| * {@link org.apache.lucene.search.similarities.ClassicSimilarity#idf(long, long) idf(t)} = |
| * </td> |
| * <td style="vertical-align: middle; text-align: center"> |
| * 1 + log <span style="font-size: larger">(</span> |
| * </td> |
| * <td style="vertical-align: middle; text-align: center"> |
| * <table> |
| * <caption>inverse document frequency computation</caption> |
| * <tr><td style="text-align: center"><small>docCount+1</small></td></tr> |
| * <tr><td style="text-align: center">–––––––––</td></tr> |
| * <tr><td style="text-align: center"><small>docFreq+1</small></td></tr> |
| * </table> |
| * </td> |
| * <td style="vertical-align: middle; text-align: center"> |
| * <span style="font-size: larger">)</span> |
| * </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="{@docRoot}/../queryparser/org/apache/lucene/queryparser/classic/package-summary.html#Boosting_a_Term">query syntax</A>), |
| * or as set by wrapping with |
| * {@link org.apache.lucene.search.BoostQuery#BoostQuery(org.apache.lucene.search.Query, float) BoostQuery}. |
| * 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.BoostQuery#getBoost() getBoost()}. |
| * <br> <br> |
| * </li> |
| * |
| * <li> |
| * <A NAME="formula_norm"></A> |
| * <b><i>norm(t,d)</i></b> is an index-time boost factor that solely |
| * depends on the number of tokens of this field in the document, so |
| * that shorter fields contribute more to the score. |
| * </li> |
| * </ol> |
| * |
| * @see org.apache.lucene.index.IndexWriterConfig#setSimilarity(Similarity) |
| * @see IndexSearcher#setSimilarity(Similarity) |
| */ |
| public abstract class TFIDFSimilarity extends Similarity { |
| |
| /** |
| * Sole constructor. (For invocation by subclass |
| * constructors, typically implicit.) |
| */ |
| public TFIDFSimilarity() {} |
| |
| /** |
| * True if overlap tokens (tokens with a position of increment of zero) are |
| * discounted from the document's length. |
| */ |
| protected boolean discountOverlaps = true; |
| |
| /** Determines whether overlap tokens (Tokens with |
| * 0 position increment) are ignored when computing |
| * norm. By default this is true, meaning overlap |
| * tokens do not count when computing norms. |
| * |
| * @lucene.experimental |
| * |
| * @see #computeNorm |
| */ |
| public void setDiscountOverlaps(boolean v) { |
| discountOverlaps = v; |
| } |
| |
| /** |
| * Returns true if overlap tokens are discounted from the document's length. |
| * @see #setDiscountOverlaps |
| */ |
| public boolean getDiscountOverlaps() { |
| return discountOverlaps; |
| } |
| |
| /** Computes a score factor based on a term or phrase's frequency in a |
| * document. This value is multiplied by the {@link #idf(long, long)} |
| * 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 class="prettyprint"> |
| * idf(docFreq, docCount); |
| * </pre> |
| * |
| * Note that {@link CollectionStatistics#docCount()} is used instead of |
| * {@link org.apache.lucene.index.IndexReader#numDocs() IndexReader#numDocs()} because also |
| * {@link TermStatistics#docFreq()} is used, and when the latter |
| * is inaccurate, so is {@link CollectionStatistics#docCount()}, and in the same direction. |
| * In addition, {@link CollectionStatistics#docCount()} does not skew when fields are sparse. |
| * |
| * @param collectionStats collection-level statistics |
| * @param termStats term-level statistics for the term |
| * @return an Explain object that includes both an idf score factor |
| and an explanation for the term. |
| */ |
| public Explanation idfExplain(CollectionStatistics collectionStats, TermStatistics termStats) { |
| final long df = termStats.docFreq(); |
| final long docCount = collectionStats.docCount(); |
| final float idf = idf(df, docCount); |
| return Explanation.match(idf, "idf(docFreq, docCount)", |
| Explanation.match(df, "docFreq, number of documents containing term"), |
| Explanation.match(docCount, "docCount, total number of documents with field")); |
| } |
| |
| /** |
| * Computes a score factor for a phrase. |
| * |
| * <p> |
| * The default implementation sums the idf factor for |
| * each term in the phrase. |
| * |
| * @param collectionStats collection-level statistics |
| * @param termStats term-level statistics for the terms in the phrase |
| * @return an Explain object that includes both an idf |
| * score factor for the phrase and an explanation |
| * for each term. |
| */ |
| public Explanation idfExplain(CollectionStatistics collectionStats, TermStatistics termStats[]) { |
| double idf = 0d; // sum into a double before casting into a float |
| List<Explanation> subs = new ArrayList<>(); |
| for (final TermStatistics stat : termStats ) { |
| Explanation idfExplain = idfExplain(collectionStats, stat); |
| subs.add(idfExplain); |
| idf += idfExplain.getValue().floatValue(); |
| } |
| return Explanation.match((float) idf, "idf(), sum of:", subs); |
| } |
| |
| /** 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(float)} 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 docCount the total number of documents in the collection |
| * @return a score factor based on the term's document frequency |
| */ |
| public abstract float idf(long docFreq, long docCount); |
| |
| /** |
| * Compute an index-time normalization value for this field instance. |
| * |
| * @param length the number of terms in the field, optionally {@link #setDiscountOverlaps(boolean) discounting overlaps} |
| * @return a length normalization value |
| */ |
| public abstract float lengthNorm(int length); |
| |
| @Override |
| public final long computeNorm(FieldInvertState state) { |
| final int numTerms; |
| if (state.getIndexOptions() == IndexOptions.DOCS && state.getIndexCreatedVersionMajor() >= 8) { |
| numTerms = state.getUniqueTermCount(); |
| } else if (discountOverlaps) { |
| numTerms = state.getLength() - state.getNumOverlap(); |
| } else { |
| numTerms = state.getLength(); |
| } |
| return SmallFloat.intToByte4(numTerms); |
| } |
| |
| @Override |
| public final SimScorer scorer(float boost, CollectionStatistics collectionStats, TermStatistics... termStats) { |
| final Explanation idf = termStats.length == 1 |
| ? idfExplain(collectionStats, termStats[0]) |
| : idfExplain(collectionStats, termStats); |
| float[] normTable = new float[256]; |
| for (int i = 1; i < 256; ++i) { |
| int length = SmallFloat.byte4ToInt((byte) i); |
| float norm = lengthNorm(length); |
| normTable[i] = norm; |
| } |
| normTable[0] = 1f / normTable[255]; |
| return new TFIDFScorer(boost, idf, normTable); |
| } |
| |
| |
| /** Collection statistics for the TF-IDF model. The only statistic of interest |
| * to this model is idf. */ |
| class TFIDFScorer extends SimScorer { |
| /** The idf and its explanation */ |
| private final Explanation idf; |
| private final float boost; |
| private final float queryWeight; |
| final float[] normTable; |
| |
| public TFIDFScorer(float boost, Explanation idf, float[] normTable) { |
| // TODO: Validate? |
| this.idf = idf; |
| this.boost = boost; |
| this.queryWeight = boost * idf.getValue().floatValue(); |
| this.normTable = normTable; |
| } |
| |
| @Override |
| public float score(float freq, long norm) { |
| final float raw = tf(freq) * queryWeight; // compute tf(f)*weight |
| float normValue = normTable[(int) (norm & 0xFF)]; |
| return raw * normValue; // normalize for field |
| } |
| |
| @Override |
| public Explanation explain(Explanation freq, long norm) { |
| return explainScore(freq, norm, normTable); |
| } |
| |
| private Explanation explainScore(Explanation freq, long encodedNorm, float[] normTable) { |
| List<Explanation> subs = new ArrayList<Explanation>(); |
| if (boost != 1F) { |
| subs.add(Explanation.match(boost, "boost")); |
| } |
| subs.add(idf); |
| Explanation tf = Explanation.match(tf(freq.getValue().floatValue()), "tf(freq="+freq.getValue()+"), with freq of:", freq); |
| subs.add(tf); |
| |
| float norm = normTable[(int) (encodedNorm & 0xFF)]; |
| |
| Explanation fieldNorm = Explanation.match(norm, "fieldNorm"); |
| subs.add(fieldNorm); |
| |
| return Explanation.match( |
| queryWeight * tf.getValue().floatValue() * norm, |
| "score(freq="+freq.getValue()+"), product of:", |
| subs); |
| } |
| } |
| |
| } |
