This class implements the IneB2 weighting scheme. More...

#include <weight.h>

Inheritance diagram for Xapian::IneB2Weight:

Public Member Functions
	IneB2Weight (double c)
	Construct an IneB2Weight.

std::string	name () const
	Return the name of this weighting scheme.

std::string	serialise () const
	Return this object's parameters serialised as a single string.

IneB2Weight *	unserialise (const std::string &serialised) const
	Unserialise parameters.

double	get_sumpart (Xapian::termcount wdf, Xapian::termcount doclen, Xapian::termcount uniqterms) const
	Calculate the weight contribution for this object's term to a document.

double	get_maxpart () const
	Return an upper bound on what get_sumpart() can return for any document.

double	get_sumextra (Xapian::termcount doclen, Xapian::termcount uniqterms) const
	Calculate the term-independent weight component for a document.

double	get_maxextra () const
	Return an upper bound on what get_sumextra() can return for any document.

Public Member Functions inherited from Xapian::Weight
	Weight ()
	Default constructor, needed by subclass constructors.

virtual	~Weight ()
	Virtual destructor, because we have virtual methods.

Additional Inherited Members
Public Types inherited from Xapian::Weight
enum	type_smoothing { TWO_STAGE_SMOOTHING = 1 , DIRICHLET_SMOOTHING = 2 , ABSOLUTE_DISCOUNT_SMOOTHING = 3 , JELINEK_MERCER_SMOOTHING = 4 , DIRICHLET_PLUS_SMOOTHING = 5 }
	Type of smoothing to use with the Language Model Weighting scheme. More...

Protected Types inherited from Xapian::Weight
enum	stat_flags { COLLECTION_SIZE = 1 , RSET_SIZE = 2 , AVERAGE_LENGTH = 4 , TERMFREQ = 8 , RELTERMFREQ = 16 , QUERY_LENGTH = 32 , WQF = 64 , WDF = 128 , DOC_LENGTH = 256 , DOC_LENGTH_MIN = 512 , DOC_LENGTH_MAX = 1024 , WDF_MAX = 2048 , COLLECTION_FREQ = 4096 , UNIQUE_TERMS = 8192 , TOTAL_LENGTH = COLLECTION_SIZE \| AVERAGE_LENGTH }
	Stats which the weighting scheme can use (see need_stat()). More...

Protected Member Functions inherited from Xapian::Weight
void	need_stat (stat_flags flag)
	Tell Xapian that your subclass will want a particular statistic.

	Weight (const Weight &)
	Don't allow copying.

Xapian::doccount	get_collection_size () const
	The number of documents in the collection.

Xapian::doccount	get_rset_size () const
	The number of documents marked as relevant.

Xapian::doclength	get_average_length () const
	The average length of a document in the collection.

Xapian::doccount	get_termfreq () const
	The number of documents which this term indexes.

Xapian::doccount	get_reltermfreq () const
	The number of relevant documents which this term indexes.

Xapian::termcount	get_collection_freq () const
	The collection frequency of the term.

Xapian::termcount	get_query_length () const
	The length of the query.

Xapian::termcount	get_wqf () const
	The within-query-frequency of this term.

Xapian::termcount	get_doclength_upper_bound () const
	An upper bound on the maximum length of any document in the shard.

Xapian::termcount	get_doclength_lower_bound () const
	A lower bound on the minimum length of any document in the shard.

Xapian::termcount	get_wdf_upper_bound () const
	An upper bound on the wdf of this term in the shard.

Xapian::totallength	get_total_length () const
	Total length of all documents in the collection.

Detailed Description

This class implements the IneB2 weighting scheme.

IneB2 is a representative scheme of the Divergence from Randomness Framework by Gianni Amati.

It uses the Inverse expected document frequency model (Ine), the Bernoulli method to find the aftereffect of sampling (B) and the second wdf normalization proposed by Amati to normalize the wdf in the document to the length of the document (H2).

For more information about the DFR Framework and the IneB2 scheme, please refer to: Gianni Amati and Cornelis Joost Van Rijsbergen Probabilistic models of information retrieval based on measuring the divergence from randomness ACM Transactions on Information Systems (TOIS) 20, (4), 2002, pp. 357-389.

Constructor & Destructor Documentation

◆ IneB2Weight()

Xapian::IneB2Weight::IneB2Weight ( double c )

explicit

Construct an IneB2Weight.

Parameters

c	A strictly positive parameter controlling the extent of the normalization of the wdf to the document length. The default value of 1 is suitable for longer queries but it may need to be changed for shorter queries. For more information, please refer to Gianni Amati's PHD thesis.

Member Function Documentation

◆ get_maxextra()

double Xapian::IneB2Weight::get_maxextra ( ) const

virtual

Return an upper bound on what get_sumextra() can return for any document.

This information is used by the matcher to perform various optimisations, so strive to make the bound as tight as possible.

Implements Xapian::Weight.

◆ get_maxpart()

double Xapian::IneB2Weight::get_maxpart ( ) const

virtual

Return an upper bound on what get_sumpart() can return for any document.

This information is used by the matcher to perform various optimisations, so strive to make the bound as tight as possible.

Implements Xapian::Weight.

◆ get_sumextra()

double Xapian::IneB2Weight::get_sumextra	(	Xapian::termcount	doclen,
		Xapian::termcount	uniqterms
	)		const

virtual

Calculate the term-independent weight component for a document.

The parameter gives information about the document which may be used in the calculations:

Parameters

doclen	The document's length (unnormalised).
uniqterms	The number of unique terms in the document.

Implements Xapian::Weight.

◆ get_sumpart()

double Xapian::IneB2Weight::get_sumpart	(	Xapian::termcount	wdf,
		Xapian::termcount	doclen,
		Xapian::termcount	uniqterms
	)		const

virtual

Calculate the weight contribution for this object's term to a document.

The parameters give information about the document which may be used in the calculations:

Parameters

wdf	The within document frequency of the term in the document.
doclen	The document's length (unnormalised).
uniqterms	Number of unique terms in the document (used for absolute smoothing).

Implements Xapian::Weight.

◆ name()

std::string Xapian::IneB2Weight::name ( ) const

virtual

Return the name of this weighting scheme.

This name is used by the remote backend. It is passed along with the serialised parameters to the remote server so that it knows which class to create.

Return the full namespace-qualified name of your class here - if your class is called FooWeight, return "FooWeight" from this method (Xapian::BM25Weight returns "Xapian::BM25Weight" here).

If you don't want to support the remote backend, you can use the default implementation which simply returns an empty string.

Reimplemented from Xapian::Weight.

◆ serialise()

std::string Xapian::IneB2Weight::serialise ( ) const

virtual

Return this object's parameters serialised as a single string.

If you don't want to support the remote backend, you can use the default implementation which simply throws Xapian::UnimplementedError.

Reimplemented from Xapian::Weight.

◆ unserialise()

IneB2Weight * Xapian::IneB2Weight::unserialise ( const std::string & serialised ) const

virtual

Unserialise parameters.

This method unserialises parameters serialised by the serialise() method and allocates and returns a new object initialised with them.

If you don't want to support the remote backend, you can use the default implementation which simply throws Xapian::UnimplementedError.

Note that the returned object will be deallocated by Xapian after use with "delete". If you want to handle the deletion in a special way (for example when wrapping the Xapian API for use from another language) then you can define a static operator delete method in your subclass as shown here: https://trac.xapian.org/ticket/554#comment:1

Parameters

serialised A string containing the serialised parameters.

Reimplemented from Xapian::Weight.

The documentation for this class was generated from the following file:

xapian/weight.h

Public Member Functions

Additional Inherited Members

Detailed Description

Constructor & Destructor Documentation

◆ IneB2Weight()

Member Function Documentation

◆ get_maxextra()

◆ get_maxpart()

◆ get_sumextra()

◆ get_sumpart()

◆ name()

◆ serialise()

◆ unserialise()