weka.classifiers.m5
Class Node

java.lang.Object
  |
  +--weka.classifiers.m5.Node

All Implemented Interfaces:

java.io.Serializable

public final class Node

extends java.lang.Object

implements java.io.Serializable

Class for handing a node in the tree or the subtree under this node

Author:

Yong Wang (yongwang@cs.waikato.ac.nz)

See Also:

Serialized Form

Constructor Summary

Node(Instances inst, Node up)
Constructs a new node

Node(Instances inst, Node up, Options options)
Constructs the root of a tree

Method Summary

Node	copy(Node up) Makes a copy of the tree under this node
Errors	errors(Instances inst, boolean smooth) Evaluates a tree
double	factor(int n, int v, double pruningFactor) Calculates a multiplication factor used at this node
java.lang.String	formulaeToString(boolean smooth) Converts all the linear models at the leaves under the node to a string
void	function() Finds the appropriate order of the unsmoothed linear model at this node
void	leafNode() Sets the node to a leaf
int	leafNum(Instance instance) Detects which leaf a instance falls into
Measures	measures(Instances inst, boolean smooth) Computes performance measures of a tree
java.lang.String	measuresToString(Measures[] measures, Instances inst, int lmNo, int verbosity, java.lang.String str) Converts the performance measures into a string
int	numberOfLinearModels() Counts the number of linear models in the tree.
int	numLeaves(int leafCounter) Sets the leaves' numbers
double	predict(Instance instance, boolean smooth) Predicts the class value of an instance by the tree
java.lang.String	predictionsToString(Instances inst, int lmNo, boolean smooth) Converts the predictions by the tree under this node to a string
void	prune() Prunes the model tree
void	regression(Function function) Computes the coefficients of a linear model using the instances at this node
java.lang.String	singleNodeToString() Converts the information stored at this node to a string
void	smoothen() Smoothens all unsmoothed formulae at the tree leaves under this node.
void	smoothenFormula(Node current) Recursively smoothens the unsmoothed linear model at this node with the unsmoothed linear models at the nodes above this
void	split(Instances inst) Splits the node recursively, unless there are few instances or instances have similar values of the class attribute
java.lang.String	treeToString(int treeLevel, double deviation) Converts the tree under this node to a string
Measures[]	validation(Instances inst) Computes performance measures for both unsmoothed and smoothed models
void	valueNode() Takes a constant value as the function at the node

Methods inherited from class java.lang.Object

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

Constructor Detail

Node

public Node(Instances inst,
            Node up)

Constructs a new node

Parameters:

inst - instances

up - the parent node

Node

public Node(Instances inst,
            Node up,
            Options options)

Constructs the root of a tree

Parameters:

inst - instances

up - the parent node

options - the options

Method Detail

singleNodeToString

public final java.lang.String singleNodeToString()
                                          throws java.lang.Exception

Converts the information stored at this node to a string

Returns:

the converted string

Throws:

java.lang.Exception - if something goes wrong

treeToString

public final java.lang.String treeToString(int treeLevel,
                                           double deviation)

Converts the tree under this node to a string

Parameters:

treeLevel - the depth of this node; the root of a tree should have treeLevel = 0

deviation - the global deviation of the class column, used for evaluating relative errors

Returns:

the converted string

numberOfLinearModels

public final int numberOfLinearModels()

Counts the number of linear models in the tree.

formulaeToString

public final java.lang.String formulaeToString(boolean smooth)
                                        throws java.lang.Exception

Converts all the linear models at the leaves under the node to a string

Parameters:

smooth - either the smoothed models if true, otherwise the unsmoothed are converted

Returns:

the converted string

Throws:

java.lang.Exception - if something goes wrong

numLeaves

public final 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

split

public final void split(Instances inst)
                 throws java.lang.Exception

Splits the node recursively, unless there are few instances or instances have similar values of the class attribute

Parameters:

inst - instances

Throws:

java.lang.Exception - if something goes wrong

leafNode

public final void leafNode()
                    throws java.lang.Exception

Sets the node to a leaf

Throws:

java.lang.Exception - if something goes wrong

valueNode

public final void valueNode()
                     throws java.lang.Exception

Takes a constant value as the function at the node

Throws:

java.lang.Exception - if something goes wrong

prune

public final void prune()
                 throws java.lang.Exception

Prunes the model tree

Parameters:

modelType - determines what kind a model is constructed, a model tree, a regression tree or a simple linear regression

pruningFactor - the pruning factor influences the size of the pruned tree

Throws:

java.lang.Exception - if something goes wrong

regression

public final void regression(Function function)

Computes the coefficients of a linear model using the instances at this node

Parameters:

function - the linear model containing the index of the attributes; coefficients are to be computed

function

public final void function()
                    throws java.lang.Exception

Finds the appropriate order of the unsmoothed linear model at this node

Throws:

java.lang.Exception - if something goes wrong

factor

public final double factor(int n,
                           int v,
                           double pruningFactor)

Calculates a multiplication factor used at this node

Parameters:

n - the number of instances

v - the number of the coefficients

Returns:

multiplication factor

smoothen

public final void smoothen()

Smoothens all unsmoothed formulae at the tree leaves under this node.

smoothenFormula

public final void smoothenFormula(Node current)

Recursively smoothens the unsmoothed linear model at this node with the unsmoothed linear models at the nodes above this

Parameters:

current - the unsmoothed linear model at the up node of the 'current' will be used for smoothening

predictionsToString

public final java.lang.String predictionsToString(Instances inst,
                                                  int lmNo,
                                                  boolean smooth)
                                           throws java.lang.Exception

Converts the predictions by the tree under this node to a string

Parameters:

insta - instances

smooth - =ture using the smoothed models; otherwise, the unsmoothed

Returns:

the converted string

Throws:

java.lang.Exception - if something goes wrong

leafNum

public final int leafNum(Instance instance)

Detects which leaf a instance falls into

Parameters:

i - instance i

inst - instances

Returns:

the leaf no.

predict

public final double predict(Instance instance,
                            boolean smooth)

Predicts the class value of an instance by the tree

Parameters:

i - instance i

Returns:

the predicted value

errors

public final Errors errors(Instances inst,
                           boolean smooth)
                    throws java.lang.Exception

Evaluates a tree

Parameters:

inst - instances

Returns:

the evaluation results

Throws:

java.lang.Exception - if something goes wrong

measures

public final Measures measures(Instances inst,
                               boolean smooth)
                        throws java.lang.Exception

Computes performance measures of a tree

Parameters:

inst - instances

smooth - =true uses the smoothed models; otherwise uses the unsmoothed models

Returns:

the performance measures

Throws:

java.lang.Exception - if something goes wrong

validation

public final Measures[] validation(Instances inst)
                            throws java.lang.Exception

Computes performance measures for both unsmoothed and smoothed models

Parameters:

inst - instances

Throws:

java.lang.Exception - if something goes wrong

copy

public final Node copy(Node up)
                throws java.lang.Exception

Makes a copy of the tree under this node

Parameters:

up - the parant node of the new node

Returns:

a copy of the tree under this node

Throws:

java.lang.Exception - if something goes wrong

measuresToString

public final java.lang.String measuresToString(Measures[] measures,
                                               Instances inst,
                                               int lmNo,
                                               int verbosity,
                                               java.lang.String str)
                                        throws java.lang.Exception

Converts the performance measures into a string

Parameters:

measures[] - contains both the unsmoothed and smoothed measures

inst - the instances

lmNo - also converts the predictions by all linear models if lmNo=0, or one linear model spedified by lmNo.

verbosity - the verbosity level

str - the type of evaluation, one of "t" for training, "T" for testing, "f" for fold training, "F" for fold testing, "x" for cross-validation

Returns:

the converted string

Throws:

java.lang.Exception - if something goes wrong