weka.classifiers.trees

Class RandomTree.Tree

java.lang.Object

weka.classifiers.trees.RandomTree.Tree

All Implemented Interfaces:

java.io.Serializable

Enclosing class:

RandomTree

public class RandomTree.Tree
extends java.lang.Object
implements java.io.Serializable

The inner class for dealing with the tree.

See Also:

Serialized Form

Field Summary

Fields

Modifier and Type	Field and Description
protected int	m_Attribute The attribute to split on.
protected double[]	m_ClassDistribution Class probabilities from the training data in the nominal case.
protected double[]	m_Distribution Holds the sum of squared errors and the weight in the numeric case.
protected double[]	m_Prop The proportions of training instances going down each branch.
protected double	m_SplitPoint The split point.
protected RandomTree.Tree[]	m_Successors The subtrees appended to this tree.

Constructor Summary

Constructors

Constructor and Description
Tree()

Method Summary

Modifier and Type	Method and Description
void	backfitData(Instances data) Backfits the given data into the tree.
protected void	backfitData(Instances data, double[] classProbs, double totalWeight) Recursively backfits data into the tree.
protected void	buildTree(Instances data, double[] classProbs, int[] attIndicesWindow, double totalWeight, java.util.Random random, int depth, double minVariance) Recursively generates a tree.
protected double	distribution(double[][] props, double[][][] dists, int att, Instances data) Computes class distribution for an attribute.
double[]	distributionForInstance(Instance instance) Computes class distribution of an instance using the decision tree.
protected double	gain(double[][] dist, double priorVal) Computes value of splitting criterion after split.
int	getM_Attribute() Get the attribute this tree splits by.
double[]	getM_Classdistribution() Gets the class distribution for the last Instances object
double	getM_SplitPoint() Get the split point for the attribute (relevant only if it is numeric).
RandomTree.Tree[]	getM_Successors() Get the successor subtrees of this tree.
java.lang.String	getRevision() Returns the revision string.
protected java.lang.String	leafString() Outputs a leaf.
protected double	numericDistribution(double[][] props, double[][][] dists, int att, double[][] subsetWeights, Instances data, double[] vals) Computes numeric class distribution for an attribute
int	numNodes() Computes size of the tree.
protected double	priorVal(double[][] dist) Computes value of splitting criterion before split.
protected Instances[]	splitData(Instances data) Splits instances into subsets based on the given split.
int	toGraph(java.lang.StringBuffer text, int num) Outputs one node for graph.
protected int	toGraph(java.lang.StringBuffer text, int num, RandomTree.Tree parent) Outputs one node for graph.
protected java.lang.String	toString(int level) Recursively outputs the tree.

Methods inherited from class java.lang.Object

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

Field Detail

m_Successors

protected RandomTree.Tree[] m_Successors

The subtrees appended to this tree.

m_Attribute

protected int m_Attribute

The attribute to split on.

m_SplitPoint

protected double m_SplitPoint

The split point.

m_Prop

protected double[] m_Prop

The proportions of training instances going down each branch.

m_ClassDistribution

protected double[] m_ClassDistribution

Class probabilities from the training data in the nominal case. Holds the mean in the numeric case.

m_Distribution

protected double[] m_Distribution

Holds the sum of squared errors and the weight in the numeric case.

Constructor Detail

Tree

public Tree()

Method Detail

backfitData

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

Backfits the given data into the tree.

Throws:

java.lang.Exception

distributionForInstance

public double[] distributionForInstance(Instance instance)
                                 throws java.lang.Exception

Computes class distribution of an instance using the decision tree.

Parameters:

instance - the instance to compute the distribution for

Returns:

the computed class distribution

Throws:

java.lang.Exception - if computation fails

toGraph

public int toGraph(java.lang.StringBuffer text,
                   int num)
            throws java.lang.Exception

Outputs one node for graph.

Parameters:

text - the buffer to append the output to

num - unique node id

Returns:

the next node id

Throws:

java.lang.Exception - if generation fails

leafString

protected java.lang.String leafString()
                               throws java.lang.Exception

Outputs a leaf.

Returns:

the leaf as string

Throws:

java.lang.Exception - if generation fails

toString

protected java.lang.String toString(int level)

Recursively outputs the tree.

Parameters:

level - the current level of the tree

Returns:

the generated subtree

backfitData

protected void backfitData(Instances data,
                           double[] classProbs,
                           double totalWeight)
                    throws java.lang.Exception

Recursively backfits data into the tree.

Parameters:

data - the data to work with

classProbs - the class distribution

Throws:

java.lang.Exception - if generation fails

buildTree

protected void buildTree(Instances data,
                         double[] classProbs,
                         int[] attIndicesWindow,
                         double totalWeight,
                         java.util.Random random,
                         int depth,
                         double minVariance)
                  throws java.lang.Exception

Recursively generates a tree.

Parameters:

data - the data to work with

classProbs - the class distribution

attIndicesWindow - the attribute window to choose attributes from

random - random number generator for choosing random attributes

depth - the current depth

Throws:

java.lang.Exception - if generation fails

numNodes

public int numNodes()

Computes size of the tree.

Returns:

the number of nodes

splitData

protected Instances[] splitData(Instances data)
                         throws java.lang.Exception

Splits instances into subsets based on the given split.

Parameters:

data - the data to work with

Returns:

the subsets of instances

Throws:

java.lang.Exception - if something goes wrong

numericDistribution

protected double numericDistribution(double[][] props,
                                     double[][][] dists,
                                     int att,
                                     double[][] subsetWeights,
                                     Instances data,
                                     double[] vals)
                              throws java.lang.Exception

Computes numeric class distribution for an attribute

Parameters:

props -

dists -

att -

subsetWeights -

data -

vals -

Returns:

Throws:

java.lang.Exception - if a problem occurs

distribution

protected double distribution(double[][] props,
                              double[][][] dists,
                              int att,
                              Instances data)
                       throws java.lang.Exception

Computes class distribution for an attribute.

Parameters:

props -

dists -

att - the attribute index

data - the data to work with

Throws:

java.lang.Exception - if something goes wrong

priorVal

protected double priorVal(double[][] dist)
                   throws java.lang.InterruptedException

Computes value of splitting criterion before split.

Parameters:

dist - the distributions

Returns:

the splitting criterion

Throws:

java.lang.InterruptedException

gain

protected double gain(double[][] dist,
                      double priorVal)

Computes value of splitting criterion after split.

Parameters:

dist - the distributions

priorVal - the splitting criterion

Returns:

the gain after the split

getRevision

public java.lang.String getRevision()

Returns the revision string.

Returns:

the revision

toGraph

protected int toGraph(java.lang.StringBuffer text,
                      int num,
                      RandomTree.Tree parent)
               throws java.lang.Exception

Outputs one node for graph.

Parameters:

text - the buffer to append the output to

num - the current node id

parent - the parent of the nodes

Returns:

the next node id

Throws:

java.lang.Exception - if something goes wrong

getM_Successors

public RandomTree.Tree[] getM_Successors()

Get the successor subtrees of this tree.

Returns:

the subtrees

getM_Attribute

public int getM_Attribute()

Get the attribute this tree splits by.

Returns:

the attribute index

getM_SplitPoint

public double getM_SplitPoint()

Get the split point for the attribute (relevant only if it is numeric). If the attribute value is strictly smaller than the split point, the relevant successor is the first successor, otherwise it is the second successor.

Returns:

the split point

getM_Classdistribution

public double[] getM_Classdistribution()

Gets the class distribution for the last Instances object

Returns:

the array that captures the likelihood of each class