xapiancore
1.4.24

This class implements the PL2 weighting scheme. More...
#include <weight.h>
Public Member Functions  
PL2Weight (double c)  
Construct a PL2Weight.  
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.  
PL2Weight *  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 termindependent 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 withinqueryfrequency of this term.  
Xapian::termcount  get_doclength_upper_bound () const 
An upper bound on the maximum length of any document in the database.  
Xapian::termcount  get_doclength_lower_bound () const 
A lower bound on the minimum length of any document in the database.  
Xapian::termcount  get_wdf_upper_bound () const 
An upper bound on the wdf of this term.  
Xapian::totallength  get_total_length () const 
Total length of all documents in the collection.  
This class implements the PL2 weighting scheme.
PL2 is a representative scheme of the Divergence from Randomness Framework by Gianni Amati.
This weighting scheme is useful for tasks that require early precision.
It uses the Poisson approximation of the Binomial Probabilistic distribution (P) along with Stirling's approximation for the factorial value, the Laplace method to find the aftereffect of sampling (L) 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 PL2 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. 357389.

explicit 
Construct a PL2Weight.
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 titled Probabilistic Models for Information Retrieval based on Divergence from Randomness. 

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.

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.

virtual 
Calculate the termindependent weight component for a document.
The parameter gives information about the document which may be used in the calculations:
doclen  The document's length (unnormalised). 
uniqterms  The number of unique terms in the document. 
Implements Xapian::Weight.

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:
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.

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 namespacequalified 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.

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.

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
serialised  A string containing the serialised parameters. 
Reimplemented from Xapian::Weight.