org.terrier.matching.models.basicmodel

Class B

java.lang.Object

org.terrier.matching.models.basicmodel.BasicModel

org.terrier.matching.models.basicmodel.B

All Implemented Interfaces:

Serializable, Cloneable

public class B
extends BasicModel

This class implements the B basic model for randomness. B stands for Bose-Einstein statistics.

Author:

Ben He

See Also:

Serialized Form

Field Summary

Fields

Modifier and Type	Field and Description
protected String	modelName The name of the model.

Fields inherited from class org.terrier.matching.models.basicmodel.BasicModel

i, numberOfDocuments, numberOfTokens

Constructor Summary

Constructors

Constructor and Description
B()

Method Summary

Modifier and Type	Method and Description
String	getInfo() Returns the name of the model.
double	score(double tf, double documentFrequency, double termFrequency, double keyFrequency, double documentLength) This method computes the score for the implemented weighting model.

Methods inherited from class org.terrier.matching.models.basicmodel.BasicModel

clone, setNumberOfDocuments, setNumberOfTokens, stirlingPower

Methods inherited from class java.lang.Object

equals, finalize, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait

Field Detail

modelName

protected String modelName

The name of the model.

Constructor Detail

B

public B()

Method Detail

getInfo

public String getInfo()

Returns the name of the model.

Specified by:

getInfo in class BasicModel

Returns:

the name of the model

score

public double score(double tf,
                    double documentFrequency,
                    double termFrequency,
                    double keyFrequency,
                    double documentLength)

This method computes the score for the implemented weighting model.

Specified by:

score in class BasicModel

Parameters:

tf - The term frequency in the document

documentFrequency - The document frequency of the term

termFrequency - the term frequency in the collection

keyFrequency - The normalised query term frequency.

documentLength - The length of the document.

Returns:

the score returned by the implemented weighting model.