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

All Implemented Interfaces:

java.io.Serializable, java.lang.Cloneable, CapabilitiesHandler, OptionHandler, RevisionHandler
```
public class RuleNode
extends Classifier
```
Constructs a node for use in an m5 tree or rule

Version:

$Revision: 1.13 $

Author:

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

See Also:

Serialized Form

Field Summary

Fields
Modifier and Type Field Description

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

Constructor Summary

Constructors
Constructor Description

RuleNode(double globalDev, double globalAbsDev, RuleNode parent)
Creates a new RuleNode instance.

Method Summary

All Methods Instance Methods Concrete Methods
Modifier and Type	Method	Description
`void`	`buildClassifier(Instances data)`	Build this node (find an attribute and split point)
`double`	`classifyInstance(Instance inst)`	Classify an instance using this node.
`void`	`findBestLeaf(double[] maxCoverage, RuleNode[] bestLeaf)`	Find the leaf with greatest coverage
`double`	`getMinNumInstances()`	Get the minimum number of instances to allow at a leaf node
`PreConstructedLinearModel`	`getModel()`	Get the linear model at this node
`int`	`getNumInstances()`	Return the number of instances that reach this node.
`boolean`	`getRegressionTree()`	Get the value of regressionTree.
`java.lang.String`	`getRevision()`	Returns the revision string.
`void`	`graph(java.lang.StringBuffer text)`	Assign a unique identifier to each node in the tree and then calls graphTree
`void`	`installLinearModels()`	Traverses the tree and installs linear models at each node.
`void`	`installSmoothedModels()`
`boolean`	`isLeaf()`	Return true if this node is a leaf
`RuleNode`	`leftNode()`	Get the left child of this node
`java.lang.String`	`nodeToString()`	Returns a description of this node (debugging purposes)
`int`	`numberOfLinearModels()`	Get the number of linear models in the tree
`int`	`numLeaves(int leafCounter)`	Sets the leaves' numbers
`RuleNode`	`parentNode()`	Get the parent of this node
`void`	`printAllModels()`	Print all the linear models at the learf (debugging purposes)
`java.lang.String`	`printLeafModels()`	print all leaf models
`java.lang.String`	`printNodeLinearModel()`	print the linear model at this node
`void`	`prune()`	Recursively prune the tree
`void`	`returnLeaves(FastVector[] v)`	Return a list containing all the leaves in the tree
`RuleNode`	`rightNode()`	Get the right child of this node
`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.
`void`	`split()`	Finds an attribute and split point for this node
`int`	`splitAtt()`	Get the index of the splitting attribute for this node
`double`	`splitVal()`	Get the split point for this node
`java.lang.String`	`toString()`	print the linear model at this node
`java.lang.String`	`treeToString(int level)`	Recursively builds a textual description of the tree

Methods inherited from class weka.classifiers.Classifier
debugTipText, distributionForInstance, forName, getCapabilities, getDebug, getOptions, listOptions, makeCopies, makeCopy, setDebug, setOptions

Methods inherited from class java.lang.Object
equals, getClass, hashCode, notify, notifyAll, wait, wait, wait

- Field Detail
  - m_numParameters
```
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
- Constructor Detail
  - RuleNode
```
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
- Method Detail
  - buildClassifier
```
public void buildClassifier(Instances data)
                     throws java.lang.Exception
```
    Build this node (find an attribute and split point)
    
    Specified by:
    
    buildClassifier in class Classifier
    
    Parameters:
    
    data - the instances on which to build this node
    
    Throws:
    
    java.lang.Exception - if an error occurs
  - classifyInstance
```
public double classifyInstance(Instance inst)
                        throws java.lang.Exception
```
    Classify an instance using this node. Recursively calls classifyInstance on child nodes.
    
    Overrides:
    
    classifyInstance in class Classifier
    
    Parameters:
    
    inst - the instance to classify
    
    Returns:
    
    the prediction for this instance
    
    Throws:
    
    java.lang.Exception - if an error occurs
  - split
```
public void split()
           throws java.lang.Exception
```
    Finds an attribute and split point for this node
    
    Throws:
    
    java.lang.Exception - if an error occurs
  - numLeaves
```
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
  - toString
```
public java.lang.String toString()
```
    print the linear model at this node
    
    Overrides:
    
    toString in class java.lang.Object
    
    Returns:
    
    the linear model
  - printNodeLinearModel
```
public java.lang.String printNodeLinearModel()
```
    print the linear model at this node
    
    Returns:
    
    the linear model at this node
  - printLeafModels
```
public java.lang.String printLeafModels()
```
    print all leaf models
    
    Returns:
    
    the leaf models
  - nodeToString
```
public java.lang.String nodeToString()
```
    Returns a description of this node (debugging purposes)
    
    Returns:
    
    a string describing this node
  - treeToString
```
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
  - installLinearModels
```
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
  - installSmoothedModels
```
public void installSmoothedModels()
                           throws java.lang.Exception
```
    Throws:
    
    java.lang.Exception
  - prune
```
public void prune()
           throws java.lang.Exception
```
    Recursively prune the tree
    
    Throws:
    
    java.lang.Exception - if an error occurs
  - findBestLeaf
```
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
  - returnLeaves
```
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
  - parentNode
```
public RuleNode parentNode()
```
    Get the parent of this node
    
    Returns:
    
    the parent of this node
  - leftNode
```
public RuleNode leftNode()
```
    Get the left child of this node
    
    Returns:
    
    the left child of this node
  - rightNode
```
public RuleNode rightNode()
```
    Get the right child of this node
    
    Returns:
    
    the right child of this node
  - splitAtt
```
public int splitAtt()
```
    Get the index of the splitting attribute for this node
    
    Returns:
    
    the index of the splitting attribute
  - splitVal
```
public double splitVal()
```
    Get the split point for this node
    
    Returns:
    
    the split point for this node
  - numberOfLinearModels
```
public int numberOfLinearModels()
```
    Get the number of linear models in the tree
    
    Returns:
    
    the number of linear models
  - isLeaf
```
public boolean isLeaf()
```
    Return true if this node is a leaf
    
    Returns:
    
    true if this node is a leaf
  - getModel
```
public PreConstructedLinearModel getModel()
```
    Get the linear model at this node
    
    Returns:
    
    the linear model at this node
  - getNumInstances
```
public int getNumInstances()
```
    Return the number of instances that reach this node.
    
    Returns:
    
    the number of instances at this node.
  - getRegressionTree
```
public boolean getRegressionTree()
```
    Get the value of regressionTree.
    
    Returns:
    
    Value of 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
  - setRegressionTree
```
public void setRegressionTree(boolean newregressionTree)
```
    Set the value of regressionTree.
    
    Parameters:
    
    newregressionTree - Value to assign to regressionTree.
  - printAllModels
```
public void printAllModels()
```
    Print all the linear models at the learf (debugging purposes)
  - graph
```
public void graph(java.lang.StringBuffer text)
```
    Assign a unique identifier to each node in the tree and then calls graphTree
    
    Parameters:
    
    text - a StringBuffer value
  - getRevision
```
public java.lang.String getRevision()
```
    Returns the revision string.
    
    Specified by:
    
    getRevision in interface RevisionHandler
    
    Overrides:
    
    getRevision in class Classifier
    
    Returns:
    
    the revision

Class RuleNode

Field Summary

Constructor Summary

Method Summary

Methods inherited from class weka.classifiers.Classifier

Methods inherited from class java.lang.Object

Field Detail

m_numParameters

Constructor Detail

RuleNode

Method Detail

buildClassifier

classifyInstance

split

numLeaves

toString

printNodeLinearModel

printLeafModels

nodeToString

treeToString

installLinearModels

installSmoothedModels

prune

findBestLeaf

returnLeaves

parentNode

leftNode

rightNode

splitAtt

splitVal

numberOfLinearModels

isLeaf

getModel

getNumInstances

getRegressionTree

setMinNumInstances

getMinNumInstances

setRegressionTree

printAllModels

graph

getRevision