weka.classifiers.trees.m5

## Class RuleNode

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

- All Implemented Interfaces:
    java.io.Serializable, java.lang.Cloneable, Classifier, BatchPredictor, CapabilitiesHandler, CapabilitiesIgnorer, OptionHandler, RevisionHandler

      public class RuleNode
      extends AbstractClassifier

  Constructs a node for use in an m5 tree or rule
  Version:
    $Revision: 10283 $
  Author:
    Mark Hall (mhall@cs.waikato.ac.nz)
  See Also:
    Serialized Form

- - ### Field Summary

    Fields
    Modifier and Type | Field and Description
    --- | ---
    `int` | `m_numParameters` the number of paramters in the chosen model for this node---either the subtree model or the linear model.

    - ### Fields inherited from class weka.classifiers.AbstractClassifier

      `NUM_DECIMAL_PLACES_DEFAULT`

  - ### Constructor Summary

    Constructors
    Constructor and Description
    `RuleNode(double globalDev, double globalAbsDev, RuleNode parent)` Creates a new `RuleNode` instance.

  - ### Method Summary

    Methods
    Modifier and Type | Method and 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(java.util.ArrayList<RuleNode>[] 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.AbstractClassifier

      `batchSizeTipText, debugTipText, distributionForInstance, distributionsForInstances, doNotCheckCapabilitiesTipText, forName, getBatchSize, getCapabilities, getDebug, getDoNotCheckCapabilities, getNumDecimalPlaces, getOptions, implementsMoreEfficientBatchPrediction, listOptions, makeCopies, makeCopy, numDecimalPlacesTipText, runClassifier, setBatchSize, setDebug, setDoNotCheckCapabilities, setNumDecimalPlaces, 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)
      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.
      Specified by:
        `classifyInstance` in interface `Classifier`
      Overrides:
        `classifyInstance` in class `AbstractClassifier`
      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(java.util.ArrayList<RuleNode>[] 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 `AbstractClassifier`
      Returns:
        the revision