weka.classifiers.rules
Class JRip.RipperRule

java.lang.Object
  weka.classifiers.rules.Rule
      weka.classifiers.rules.JRip.RipperRule

All Implemented Interfaces:

Copyable, java.io.Serializable, WeightedInstancesHandler

Enclosing class:

JRip

protected class JRip.RipperRule

extends Rule

This class implements a single rule that predicts specified class. A rule consists of antecedents "AND"ed together and the consequent (class value) for the classification. In this class, the Information Gain (p*[log(p/t) - log(P/T)]) is used to select an antecedent and Reduced Error Prunning (REP) with the metric of accuracy rate p/(p+n) or (TP+TN)/(P+N) is used to prune the rule.

See Also:

Serialized Form

Field Summary

protected FastVector	m_Antds The vector of antecedents of this rule
private double	m_Consequent The internal representation of the class label to be predicted

Constructor Summary

JRip.RipperRule()
Constructor

Method Summary

private double	computeDefAccu(Instances data) Private function to compute default number of accurate instances in the specified data for the consequent of the rule
private Instances	computeInfoGain(Instances instances, double defAcRt, JRip.Antd antd) Compute the best information gain for the specified antecedent
java.lang.Object	copy() Get a shallow copy of this rule
boolean	covers(Instance datum) Whether the instance covered by this rule
double	getConsequent() Get the consequent of this rule, i.e. the predicted class
void	grow(Instances data) Build one rule using the growing data
boolean	hasAntds() Whether this rule has antecedents, i.e. whether it is a default rule
void	prune(Instances pruneData, boolean useWhole) Prune all the possible final sequences of the rule using the pruning data.
void	setConsequent(double cl)
double	size() the number of antecedents of the rule
java.lang.String	toString(Attribute classAttr) Prints this rule

Methods inherited from class java.lang.Object

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

Field Detail

m_Consequent

private double m_Consequent

The internal representation of the class label to be predicted

m_Antds

protected FastVector m_Antds

The vector of antecedents of this rule

Constructor Detail

JRip.RipperRule

public JRip.RipperRule()

Constructor

Method Detail

setConsequent

public void setConsequent(double cl)

getConsequent

public double getConsequent()

Description copied from class: Rule

Get the consequent of this rule, i.e. the predicted class

Specified by:

getConsequent in class Rule

Returns:

the consequent

copy

public java.lang.Object copy()

Get a shallow copy of this rule

Specified by:

copy in interface Copyable

Overrides:

copy in class Rule

Returns:

the copy

covers

public boolean covers(Instance datum)

Whether the instance covered by this rule

Specified by:

covers in class Rule

Returns:

the boolean value indicating whether the instance is covered by this rule

hasAntds

public boolean hasAntds()

Whether this rule has antecedents, i.e. whether it is a default rule

Specified by:

hasAntds in class Rule

Returns:

the boolean value indicating whether the rule has antecedents

size

public double size()

the number of antecedents of the rule

Specified by:

size in class Rule

Returns:

the size of this rule

computeDefAccu

private double computeDefAccu(Instances data)

Private function to compute default number of accurate instances in the specified data for the consequent of the rule

Parameters:

data - the data in question

Returns:

the default accuracy number

grow

public void grow(Instances data)
          throws java.lang.Exception

Build one rule using the growing data

Specified by:

grow in class Rule

Parameters:

data - the growing data used to build the rule

Throws:

if - the consequent is not set yet

java.lang.Exception

computeInfoGain

private Instances computeInfoGain(Instances instances,
                                  double defAcRt,
                                  JRip.Antd antd)

Compute the best information gain for the specified antecedent

Parameters:

instances - the data based on which the infoGain is computed

defAcRt - the default accuracy rate of data

antd - the specific antecedent

Returns:

the data covered by the antecedent

prune

public void prune(Instances pruneData,
                  boolean useWhole)

Prune all the possible final sequences of the rule using the pruning data. The measure used to prune the rule is based on flag given.

Parameters:

pruneData - the pruning data used to prune the rule

useWhole - flag to indicate whether use the error rate of the whole pruning data instead of the data covered

toString

public java.lang.String toString(Attribute classAttr)

Prints this rule

Parameters:

classAttr - the class attribute in the data

Returns:

a textual description of this rule