weka.classifiers.trees.m5
Class Rule2

java.lang.Object
  weka.classifiers.trees.m5.Rule2

All Implemented Interfaces:

Serializable, weka.core.RevisionHandler

public class Rule2
extends Object
implements Serializable, weka.core.RevisionHandler

Generates a single m5 tree or rule

Version:

$Revision: 4584 $

Author:

Mark Hall

See Also:

Serialized Form

Field Summary

protected static int	LEFT
protected RuleNode2	m_topOfTree the top of the m5 tree for this rule
protected static int	RIGHT

Constructor Summary

Rule2()
Constructor declaration

Method Summary

protected static double	absDev(int attr, weka.core.Instances inst) Returns the absolute deviation value of the supplied attribute index.
void	buildClassifier(weka.core.Instances data) Generates a single rule or m5 model tree.
double	classifyInstance(weka.core.Instance instance) Calculates a prediction for an instance using this rule or M5 model tree
RuleNode2	getM5RootNode()
double	getMinNumInstances() Get the minimum number of instances to allow at a leaf node
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
weka.core.Instances	notCoveredInstances() Get the instances not covered by this rule
void	setMinNumInstances(double minNum) Set the minumum number of instances to allow at a leaf node
void	setRegressionTree(boolean newregressionTree) Set the value of regressionTree.
protected void	setSaveInstances(boolean save) Sets whether instances at each node in an M5 tree should be saved for visualization purposes.
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
protected static double	stdDev(int attr, weka.core.Instances inst) Returns the standard deviation value of the supplied attribute index.
RuleNode2	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

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

Field Detail

LEFT

protected static int LEFT

RIGHT

protected static int RIGHT

m_topOfTree

protected RuleNode2 m_topOfTree

the top of the m5 tree for this rule

Constructor Detail

Rule2

public Rule2()

Constructor declaration

Method Detail

buildClassifier

public void buildClassifier(weka.core.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(weka.core.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 RuleNode2 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

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 weka.core.Instances notCoveredInstances()

Get the instances not covered by this rule

Returns:

the instances not covered

stdDev

protected static final double stdDev(int attr,
                                     weka.core.Instances inst)

Returns the standard deviation value of the supplied attribute index.

Parameters:

attr - an attribute index

inst - the instances

Returns:

the standard deviation value

absDev

protected static final double absDev(int attr,
                                     weka.core.Instances inst)

Returns the absolute deviation value of the supplied attribute index.

Parameters:

attr - an attribute index

inst - the instances

Returns:

the absolute deviation value

setSaveInstances

protected void setSaveInstances(boolean save)

Sets whether instances at each node in an M5 tree should be saved for visualization purposes. Default is to save memory.

Parameters:

save - a boolean value

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

getM5RootNode

public RuleNode2 getM5RootNode()

getRevision

public String getRevision()

Returns the revision string.

Specified by:

getRevision in interface weka.core.RevisionHandler

Returns:

the revision