Package weka.classifiers.trees.m5

Class Rule

java.lang.Object

weka.classifiers.trees.m5.Rule

All Implemented Interfaces:

Serializable, RevisionHandler

public class Rule
extends Object
implements Serializable, RevisionHandler

Generates a single m5 tree or rule

Version:

$Revision$

Author:

Mark Hall

See Also:

Serialized Form

Constructor Summary

Constructors

Constructor	Description
Rule()	Constructor declaration

Method Summary

Modifier and Type	Method	Description
void	buildClassifier(Instances data)	Generates a single rule or m5 model tree.
double	classifyInstance(Instance instance)	Calculates a prediction for an instance using this rule or M5 model tree
void	freeNotCoveredInstances()	Free up memory consumed by the set of instances not covered by this rule.
boolean	getKuenzelEtAlSplitSelection()	Get whether Kuenzel et al.'s split selection is used
RuleNode	getM5RootNode()
double	getMinNumInstances()	Get the minimum number of instances to allow at a leaf node
int	getNumDecimalPlaces()	Get the number of decimal places.
boolean	getRegressionTree()	Get the value of regressionTree.
String	getRevision()	Returns the revision string.
boolean	getSmoothing()	Get whether or not smoothing has been turned on
boolean	getUnpruned()	Get whether unpruned tree/rules are being generated
boolean	getUseTree()	get whether an m5 tree is being used rather than rules
Instances	notCoveredInstances()	Get the instances not covered by this rule
void	setKuenzelEtAlSplitSelection(boolean unpruned)	Use Kuenzel et al.'s split selection
void	setMinNumInstances(double minNum)	Set the minumum number of instances to allow at a leaf node
void	setNumDecimalPlaces(int num)	Set the number of decimal places.
void	setRegressionTree(boolean newregressionTree)	Set the value of regressionTree.
void	setSmoothing(boolean s)	Smooth predictions
void	setUnpruned(boolean unpruned)	Use unpruned tree/rules
void	setUseTree(boolean u)	Use an m5 tree rather than generate rules
RuleNode	topOfTree()	Returns the top of the tree.
String	toString()	Return a description of the m5 tree or rule

Methods inherited from class java.lang.Object

equals, getClass, hashCode, notify, notifyAll, wait, wait, wait

Constructor Detail

Rule

public Rule()

Constructor declaration

Method Detail

buildClassifier

public void buildClassifier(Instances data)
                     throws Exception

Generates a single rule or m5 model tree.

Parameters:

data - set of instances serving as training data

Throws:

Exception - if the rule has not been generated successfully

classifyInstance

public double classifyInstance(Instance instance)
                        throws Exception

Calculates a prediction for an instance using this rule or M5 model tree

Parameters:

instance - the instance whos class value is to be predicted

Returns:

the prediction

Throws:

Exception - if a prediction can't be made.

topOfTree

public RuleNode topOfTree()

Returns the top of the tree.

toString

public String toString()

Return a description of the m5 tree or rule

Overrides:

toString in class Object

Returns:

a description of the m5 tree or rule as a String

setKuenzelEtAlSplitSelection

public void setKuenzelEtAlSplitSelection(boolean unpruned)

Use Kuenzel et al.'s split selection

Parameters:

unpruned - true if Kuenzel et al.'s split selection is used

getKuenzelEtAlSplitSelection

public boolean getKuenzelEtAlSplitSelection()

Get whether Kuenzel et al.'s split selection is used

Returns:

true if Kuenzel et al.'s split selection is used

setUnpruned

public void setUnpruned(boolean unpruned)

Use unpruned tree/rules

Parameters:

unpruned - true if unpruned tree/rules are to be generated

getUnpruned

public boolean getUnpruned()

Get whether unpruned tree/rules are being generated

Returns:

true if unpruned tree/rules are to be generated

setUseTree

public void setUseTree(boolean u)

Use an m5 tree rather than generate rules

Parameters:

u - true if m5 tree is to be used

getUseTree

public boolean getUseTree()

get whether an m5 tree is being used rather than rules

Returns:

true if an m5 tree is being used.

setSmoothing

public void setSmoothing(boolean s)

Smooth predictions

Parameters:

s - true if smoothing is to be used

getSmoothing

public boolean getSmoothing()

Get whether or not smoothing has been turned on

Returns:

true if smoothing is being used

notCoveredInstances

public Instances notCoveredInstances()

Get the instances not covered by this rule

Returns:

the instances not covered

freeNotCoveredInstances

public void freeNotCoveredInstances()

Free up memory consumed by the set of instances not covered by this rule.

getRegressionTree

public boolean getRegressionTree()

Get the value of regressionTree.

Returns:

Value of regressionTree.

setRegressionTree

public void setRegressionTree(boolean newregressionTree)

Set the value of regressionTree.

Parameters:

newregressionTree - Value to assign to regressionTree.

setMinNumInstances

public void setMinNumInstances(double minNum)

Set the minumum number of instances to allow at a leaf node

Parameters:

minNum - the minimum number of instances

getMinNumInstances

public double getMinNumInstances()

Get the minimum number of instances to allow at a leaf node

Returns:

a double value

getNumDecimalPlaces

public int getNumDecimalPlaces()

Get the number of decimal places.

setNumDecimalPlaces

public void setNumDecimalPlaces(int num)

Set the number of decimal places.

getM5RootNode

public RuleNode getM5RootNode()

getRevision

public String getRevision()

Returns the revision string.

Specified by:

getRevision in interface RevisionHandler

Returns:

the revision