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Class weka.classifiers.trees.m5.RuleNode

java.lang.Object
    |
    +----weka.classifiers.Classifier
            |
            +----weka.classifiers.trees.m5.RuleNode

public class RuleNode

extends Classifier

Version:

$Revision: 1.5 $

Author:

Mark Hall (mhall@cs.waikato.ac.nz)

m_numParameters

the number of paramters in the chosen model for this node---either the subtree model or the linear model.

RuleNode(double, double, RuleNode)

Creates a new RuleNode instance.

buildClassifier(Instances)

Build this node (find an attribute and split point)

classifyInstance(Instance)

Classify an instance using this node.

findBestLeaf(double[], RuleNode[])

Find the leaf with greatest coverage

getMinNumInstances()

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

getRegressionTree()

Get the value of regressionTree.

getSmoothing()

Method declaration

installLinearModels()

Traverses the tree and installs linear models at each node.

leftNode()

Get the left child of this node

nodeToString()

Returns a description of this node (debugging purposes)

numberOfLinearModels()

Get the number of linear models in the tree

numLeaves(int)

Sets the leaves' numbers

parentNode()

Get the parent of this node

printAllModels()

Print all the linear models at the learf (debugging purposes)

printLeafModels()

print all leaf models

printNodeLinearModel()

print the linear model at this node

prune()

Recursively prune the tree

returnLeaves(FastVector[])

Return a list containing all the leaves in the tree

rightNode()

Get the right child of this node

setMinNumInstances(double)

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

setRegressionTree(boolean)

Set the value of regressionTree.

setSmoothing(boolean)

Get if smoothing is being used

split()

Finds an attribute and split point for this node

splitAtt()

Get the index of the splitting attribute for this node

splitVal()

Get the split point for this node

toString()

print the linear model at this node

treeToString(int)

Recursively builds a textual description of the tree

public int m_numParameters

          the number of paramters in the chosen model for this node---either the subtree model or the linear model. The constant term is counted as a paramter---this is for pruning purposes

public RuleNode(double globalDev,
                double globalAbsDev,
                RuleNode parent)

          Creates a new RuleNode instance.

Parameters:

globalDev - the global standard deviation of the class

globalAbsDev - the global absolute deviation of the class

parent - the parent of this node

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

          Build this node (find an attribute and split point)

Parameters:

data - the instances on which to build this node

Throws:

java.lang.Exception - if an error occurs

Overrides:

buildClassifier in class Classifier

public double classifyInstance(Instance inst) throws java.lang.Exception

          Classify an instance using this node. Recursively calls classifyInstance on child nodes.

Parameters:

inst - the instance to classify

Returns:

the prediction for this instance

Throws:

java.lang.Exception - if an error occurs

Overrides:

classifyInstance in class Classifier

public void split() throws java.lang.Exception

          Finds an attribute and split point for this node

Throws:

java.lang.Exception - if an error occurs

public int numLeaves(int leafCounter)

          Sets the leaves' numbers

Parameters:

leafCounter - the number of leaves counted

Returns:

the number of the total leaves under the node

public java.lang.String toString()

          print the linear model at this node

Overrides:

toString in class java.lang.Object

public java.lang.String printNodeLinearModel()

          print the linear model at this node

public java.lang.String printLeafModels()

          print all leaf models

public java.lang.String nodeToString()

          Returns a description of this node (debugging purposes)

Returns:

a string describing this node

public java.lang.String treeToString(int level)

          Recursively builds a textual description of the tree

Parameters:

level - the level of this node

Returns:

string describing the tree

public void installLinearModels() throws java.lang.Exception

          Traverses the tree and installs linear models at each node. This method must be called if pruning is not to be performed.

Throws:

java.lang.Exception - if an error occurs

public void prune() throws java.lang.Exception

          Recursively prune the tree

Throws:

java.lang.Exception - if an error occurs

public void findBestLeaf(double maxCoverage[],
                         RuleNode bestLeaf[])

          Find the leaf with greatest coverage

Parameters:

maxCoverage - the greatest coverage found so far

bestLeaf - the leaf with the greatest coverage

public void returnLeaves(FastVector v[])

          Return a list containing all the leaves in the tree

Parameters:

v - a single element array containing a vector of leaves

public RuleNode parentNode()

          Get the parent of this node

Returns:

the parent of this node

public RuleNode leftNode()

          Get the left child of this node

Returns:

the left child of this node

public RuleNode rightNode()

          Get the right child of this node

Returns:

the right child of this node

public int splitAtt()

          Get the index of the splitting attribute for this node

Returns:

the index of the splitting attribute

public double splitVal()

          Get the split point for this node

Returns:

the split point for this node

public int numberOfLinearModels()

          Get the number of linear models in the tree

Returns:

the number of linear models

public void setSmoothing(boolean s)

          Get if smoothing is being used

Parameters:

s - true if smoothing is being used

public boolean getSmoothing()

          Method declaration

Returns:

true if smoothing has been selected.

public boolean getRegressionTree()

          Get the value of regressionTree.

Returns:

Value of regressionTree.

public void setMinNumInstances(double minNum)

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

Parameters:

minNum - the minimum number of instances

public double getMinNumInstances()

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

Returns:

a double value

public void setRegressionTree(boolean newregressionTree)

          Set the value of regressionTree.

Parameters:

newregressionTree - Value to assign to regressionTree.

public void printAllModels()

          Print all the linear models at the learf (debugging purposes)

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