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* 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.
*/
using System;
using Term = Lucene.Net.Index.Term;
using SmallFloat = Lucene.Net.Util.SmallFloat;
namespace Lucene.Net.Search
{
/// <summary>Expert: Scoring API.
/// <p>Subclasses implement search scoring.
///
/// <p>The score of query <code>q</code> for document <code>d</code> correlates to the
/// cosine-distance or dot-product between document and query vectors in a
/// <a href="http://en.wikipedia.org/wiki/Vector_Space_Model">
/// Vector Space Model (VSM) of Information Retrieval</a>.
/// A document whose vector is closer to the query vector in that model is scored higher.
///
/// The score is computed as follows:
///
/// <P>
/// <table cellpadding="1" cellspacing="0" border="1" align="center">
/// <tr><td>
/// <table cellpadding="1" cellspacing="0" border="0" align="center">
/// <tr>
/// <td valign="middle" align="right" rowspan="1">
/// score(q,d) =
/// <A HREF="#formula_coord">coord(q,d)</A> ·
/// <A HREF="#formula_queryNorm">queryNorm(q)</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">tf(t in d)</A> ·
/// <A HREF="#formula_idf">idf(t)</A><sup>2</sup> ·
/// <A HREF="#formula_termBoost">t.getBoost()</A> ·
/// <A HREF="#formula_norm">norm(t,d)</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>
///
/// <p> where
/// <ol>
/// <li>
/// <A NAME="formula_tf"></A>
/// <b>tf(t in d)</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.
/// The default computation for <i>tf(t in d)</i> in
/// {@link Lucene.Net.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 Lucene.Net.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>idf(t)</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.
/// The default computation for <i>idf(t)</i> in
/// {@link Lucene.Net.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 Lucene.Net.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>coord(q,d)</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>
/// queryNorm(q)
/// </b>
/// is a normalizing factor used to make scores between queries comparable.
/// This factor does not affect document ranking (since all ranked documents are multiplied by the same factor),
/// but rather just attempts to make scores from different queries (or even different indexes) comparable.
/// This is a search time factor computed by the Similarity in effect at search time.
///
/// The default computation in
/// {@link Lucene.Net.Search.DefaultSimilarity#QueryNorm(float) DefaultSimilarity}
/// is:
/// <br> <br>
/// <table cellpadding="1" cellspacing="0" border="0" align="center">
/// <tr>
/// <td valign="middle" align="right" rowspan="1">
/// queryNorm(q) =
/// {@link Lucene.Net.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 Lucene.Net.Search.Weight} object.
/// For example, a {@link Lucene.Net.Search.BooleanQuery boolean query}
/// 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 Lucene.Net.Search.Weight#SumOfSquaredWeights() sumOfSquaredWeights} =
/// {@link Lucene.Net.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>t.getBoost()</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 Lucene.Net.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 Lucene.Net.Search.TermQuery TermQuery} objects,
/// and so the boost of a term in the query is accessible by calling the sub-query
/// {@link Lucene.Net.Search.Query#GetBoost() getBoost()}.
/// <br> <br>
/// </li>
///
/// <li>
/// <A NAME="formula_norm"></A>
/// <b>norm(t,d)</b> encapsulates a few (indexing time) boost and length factors:
///
/// <ul>
/// <li><b>Document boost</b> - set by calling
/// {@link Lucene.Net.Documents.Document#SetBoost(float) doc.setBoost()}
/// before adding the document to the index.
/// </li>
/// <li><b>Field boost</b> - set by calling
/// {@link Lucene.Net.Documents.Fieldable#SetBoost(float) field.setBoost()}
/// before adding the field to a document.
/// </li>
/// <li>{@link #LengthNorm(String, int) <b>lengthNorm</b>(field)} - 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>
///
/// <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 Lucene.Net.Documents.Document#GetBoost() doc.getBoost()}
/// ·
/// {@link #LengthNorm(String, int) lengthNorm(field)}
/// ·
/// </td>
/// <td valign="bottom" align="center" rowspan="1">
/// <big><big><big>∏</big></big></big>
/// </td>
/// <td valign="middle" align="right" rowspan="1">
/// {@link Lucene.Net.Documents.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 #EncodeNorm(float) encoded} as a single byte
/// before being stored.
/// At search time, the norm byte value is read from the index
/// {@link Lucene.Net.Store.Directory directory} and
/// {@link #DecodeNorm(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 decode(encode(x)) = x.
/// For instance, decode(encode(0.89)) = 0.75.
/// Also notice 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>
///
/// </summary>
/// <seealso cref="#SetDefault(Similarity)">
/// </seealso>
/// <seealso cref="IndexWriter#SetSimilarity(Similarity)">
/// </seealso>
/// <seealso cref="Searcher#SetSimilarity(Similarity)">
/// </seealso>
[Serializable]
public abstract class Similarity
{
/// <summary>The Similarity implementation used by default. </summary>
private static Similarity defaultImpl = new DefaultSimilarity();
/// <summary>Set the default Similarity implementation used by indexing and search
/// code.
///
/// </summary>
/// <seealso cref="Searcher#SetSimilarity(Similarity)">
/// </seealso>
/// <seealso cref="IndexWriter#SetSimilarity(Similarity)">
/// </seealso>
public static void SetDefault(Similarity similarity)
{
Similarity.defaultImpl = similarity;
}
/// <summary>Return the default Similarity implementation used by indexing and search
/// code.
///
/// <p>This is initially an instance of {@link DefaultSimilarity}.
///
/// </summary>
/// <seealso cref="Searcher#SetSimilarity(Similarity)">
/// </seealso>
/// <seealso cref="IndexWriter#SetSimilarity(Similarity)">
/// </seealso>
public static Similarity GetDefault()
{
return Similarity.defaultImpl;
}
/// <summary>Cache of decoded bytes. </summary>
private static readonly float[] NORM_TABLE = new float[256];
/// <summary>Decodes a normalization factor stored in an index.</summary>
/// <seealso cref="#EncodeNorm(float)">
/// </seealso>
public static float DecodeNorm(byte b)
{
return NORM_TABLE[b & 0xFF]; // & 0xFF maps negative bytes to positive above 127
}
/// <summary>Returns a table for decoding normalization bytes.</summary>
/// <seealso cref="#EncodeNorm(float)">
/// </seealso>
public static float[] GetNormDecoder()
{
return NORM_TABLE;
}
/// <summary>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>That these values are computed under
/// {@link Lucene.Net.Index.IndexWriter#AddDocument(Lucene.Net.Documents.Document)}
/// and stored then using
/// {@link #EncodeNorm(float)}.
/// Thus they have limited precision, and documents
/// must be re-indexed if this method is altered.
///
/// </summary>
/// <param name="fieldName">the name of the field
/// </param>
/// <param name="numTokens">the total number of tokens contained in fields named
/// <i>fieldName</i> of <i>doc</i>.
/// </param>
/// <returns> a normalization factor for hits on this field of this document
///
/// </returns>
/// <seealso cref="Lucene.Net.Documents.Field.SetBoost(float)">
/// </seealso>
public abstract float LengthNorm(System.String fieldName, int numTokens);
/// <summary>Computes the normalization value for a query given the sum of the squared
/// weights of each of the query terms. This value is then multipled into the
/// weight of each query term.
///
/// <p>This does not affect ranking, but rather just attempts to make scores
/// from different queries comparable.
///
/// </summary>
/// <param name="sumOfSquaredWeights">the sum of the squares of query term weights
/// </param>
/// <returns> a normalization factor for query weights
/// </returns>
public abstract float QueryNorm(float sumOfSquaredWeights);
/// <summary>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.
///
/// </summary>
/// <seealso cref="Lucene.Net.Documents.Field#SetBoost(float)">
/// </seealso>
/// <seealso cref="SmallFloat">
/// </seealso>
public static byte EncodeNorm(float f)
{
return (byte) SmallFloat.FloatToByte315(f);
}
/// <summary>Computes a score factor based on a term or phrase's frequency in a
/// document. This value is multiplied by the {@link #Idf(Term, Searcher)}
/// factor for each term in the query and these products are then summed to
/// form the initial score for a document.
///
/// <p>Terms and phrases repeated in a document indicate the topic of the
/// document, so implementations of this method usually return larger values
/// when <code>freq</code> is large, and smaller values when <code>freq</code>
/// is small.
///
/// <p>The default implementation calls {@link #Tf(float)}.
///
/// </summary>
/// <param name="freq">the frequency of a term within a document
/// </param>
/// <returns> a score factor based on a term's within-document frequency
/// </returns>
public virtual float Tf(int freq)
{
return Tf((float) freq);
}
/// <summary>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.
///
/// </summary>
/// <seealso cref="PhraseQuery.SetSlop(int)">
/// </seealso>
/// <param name="distance">the edit distance of this sloppy phrase match
/// </param>
/// <returns> the frequency increment for this match
/// </returns>
public abstract float SloppyFreq(int distance);
/// <summary>Computes a score factor based on a term or phrase's frequency in a
/// document. This value is multiplied by the {@link #Idf(Term, Searcher)}
/// factor for each term in the query and these products are then summed to
/// form the initial score for a document.
///
/// <p>Terms and phrases repeated in a document indicate the topic of the
/// document, so implementations of this method usually return larger values
/// when <code>freq</code> is large, and smaller values when <code>freq</code>
/// is small.
///
/// </summary>
/// <param name="freq">the frequency of a term within a document
/// </param>
/// <returns> a score factor based on a term's within-document frequency
/// </returns>
public abstract float Tf(float freq);
/// <summary>Computes a score factor for a simple term.
///
/// <p>The default implementation is:<pre>
/// return idf(searcher.docFreq(term), searcher.maxDoc());
/// </pre>
///
/// Note that {@link Searcher#MaxDoc()} is used instead of
/// {@link IndexReader#NumDocs()} because it is proportional to
/// {@link Searcher#DocFreq(Term)} , i.e., when one is inaccurate,
/// so is the other, and in the same direction.
///
/// </summary>
/// <param name="term">the term in question
/// </param>
/// <param name="searcher">the document collection being searched
/// </param>
/// <returns> a score factor for the term
/// </returns>
public virtual float Idf(Term term, Searcher searcher)
{
return Idf(searcher.DocFreq(term), searcher.MaxDoc());
}
/// <summary>Computes a score factor for a phrase.
///
/// <p>The default implementation sums the {@link #Idf(Term,Searcher)} factor
/// for each term in the phrase.
///
/// </summary>
/// <param name="terms">the terms in the phrase
/// </param>
/// <param name="searcher">the document collection being searched
/// </param>
/// <returns> a score factor for the phrase
/// </returns>
public virtual float Idf(System.Collections.ICollection terms, Searcher searcher)
{
float idf = 0.0f;
System.Collections.IEnumerator i = terms.GetEnumerator();
while (i.MoveNext())
{
idf += Idf((Term) i.Current, searcher);
}
return idf;
}
/// <summary>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.
///
/// </summary>
/// <param name="docFreq">the number of documents which contain the term
/// </param>
/// <param name="numDocs">the total number of documents in the collection
/// </param>
/// <returns> a score factor based on the term's document frequency
/// </returns>
public abstract float Idf(int docFreq, int numDocs);
/// <summary>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.
///
/// </summary>
/// <param name="overlap">the number of query terms matched in the document
/// </param>
/// <param name="maxOverlap">the total number of terms in the query
/// </param>
/// <returns> a score factor based on term overlap with the query
/// </returns>
public abstract float Coord(int overlap, int maxOverlap);
/// <summary> 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.
///
/// </summary>
/// <param name="fieldName">The fieldName of the term this payload belongs to
/// </param>
/// <param name="payload">The payload byte array to be scored
/// </param>
/// <param name="offset">The offset into the payload array
/// </param>
/// <param name="length">The length in the array
/// </param>
/// <returns> An implementation dependent float to be used as a scoring factor
/// </returns>
public virtual float ScorePayload(System.String fieldName, byte[] payload, int offset, int length)
{
//Do nothing
return 1;
}
static Similarity()
{
{
for (int i = 0; i < 256; i++)
NORM_TABLE[i] = SmallFloat.Byte315ToFloat((byte) i);
}
}
}
}
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