keel.Algorithms.Decision_Trees.ID3

Class ID3

java.lang.Object

keel.Algorithms.Decision_Trees.ID3.Algorithm

keel.Algorithms.Decision_Trees.ID3.ID3

public class ID3
extends Algorithm

A Java implementation of the ID3 algorithm. This is a binary version where nodes are split by atributtes and values, instead of the generic ID3, which only considers attributes for splitting.

Version:

1.0 (30-03-06)

Author:

Cristóbal Romero Morales (UCO)

Field Summary

Fields inherited from class keel.Algorithms.Decision_Trees.ID3.Algorithm

correct, log, modelDataset, modelFileName, resultFileName, startTime, testCorrect, testDataset, testFileName, testOutputFileName, trainDataset, trainFileName, trainOutputFileName

Constructor Summary

Constructors

Constructor and Description
ID3(java.lang.String paramFile) Constructor.

Method Summary

Modifier and Type	Method and Description
boolean	alreadyUsedToDecompose(keel.Algorithms.Decision_Trees.ID3.Node node, int attribute, int value) Function to check if the specified attribute and value are already used to decompose the data.
double	computeEntropy(java.util.Vector data) Function to compute the entropy of the set of data points.
void	decomposeNode(keel.Algorithms.Decision_Trees.ID3.Node node) Function to decompose the specified node.
int	evaluateItemset(Itemset itemset, keel.Algorithms.Decision_Trees.ID3.Node node) Function to evaluate the class which the itemset must have according to the classification of the tree.
void	generateTree() Run the algorithm.
int[]	getAllValues(java.util.Vector data, int attribute) Function to return all the values of the specified attribute in the data set.
java.lang.String	getCommonClass(java.util.Vector data, int attribute) Function to return the most common class of the itemsets in data.
java.util.Vector	getComplement(java.util.Vector data, java.util.Vector oldset) Function to returns a subset of data, which is the complement of the second argument.
java.util.Vector	getSubset(java.util.Vector data, int attribute, int value) Function to returns a subset of data.
static void	main(java.lang.String[] args) Main function.
void	printResult() Writes the tree and the results of the training and the test in the file.
void	printTest() Evaluates the test dataset and writes the results in the file.
void	printTrain() Evaluates the training dataset and writes the results in the file.
protected void	setOptions(java.io.StreamTokenizer options) Function to read the options from the execution file and assign the values to the parameters.
java.lang.String	writeTree(keel.Algorithms.Decision_Trees.ID3.Node node, java.lang.String tab) Function to write the decision tree in the form of rules.

Methods inherited from class keel.Algorithms.Decision_Trees.ID3.Algorithm

getHeader, initTokenizer

Methods inherited from class java.lang.Object

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

Constructor Detail

ID3

public ID3(java.lang.String paramFile)

Constructor.

Parameters:

paramFile - The parameters file.

Method Detail

setOptions

protected void setOptions(java.io.StreamTokenizer options)
                   throws java.lang.Exception

Function to read the options from the execution file and assign the values to the parameters.

Specified by:

setOptions in class Algorithm

Parameters:

options - The StreamTokenizer that reads the parameters file.

Throws:

java.lang.Exception - If the format of the file is not correct.

generateTree

public void generateTree()

Run the algorithm.

writeTree

public java.lang.String writeTree(keel.Algorithms.Decision_Trees.ID3.Node node,
                                  java.lang.String tab)

Function to write the decision tree in the form of rules.

Parameters:

node - The current node.

tab - The indentation of the current rule.

Returns:

The tree in form of rules.

evaluateItemset

public int evaluateItemset(Itemset itemset,
                           keel.Algorithms.Decision_Trees.ID3.Node node)

Function to evaluate the class which the itemset must have according to the classification of the tree.

Parameters:

itemset - The itemset to evaluate.

node - The node that is evaluated at this time.

Returns:

The index of the class index predicted.

getAllValues

public int[] getAllValues(java.util.Vector data,
                          int attribute)

Function to return all the values of the specified attribute in the data set.

Parameters:

data - All the itemsets.

attribute - Number of attributes.

Returns:

All the values that can have the attributes.

getCommonClass

public java.lang.String getCommonClass(java.util.Vector data,
                                       int attribute)

Function to return the most common class of the itemsets in data.

Parameters:

data - All the itemsets.

attribute - Index of attribute.

Returns:

The most common class.

getSubset

public java.util.Vector getSubset(java.util.Vector data,
                                  int attribute,
                                  int value)

Function to returns a subset of data.

Parameters:

data - The itemsets that where to extract the subset.

attribute - The attribute to make the division.

value - The value of the attribute.

Returns:

All the itemsets in data that has the value given for the attribute given.

getComplement

public java.util.Vector getComplement(java.util.Vector data,
                                      java.util.Vector oldset)

Function to returns a subset of data, which is the complement of the second argument.

Parameters:

data - The itemsets that where to extract the subset.

oldset - The complement set.

Returns:

All the itemsets that are contained in data but are not in oldset.

computeEntropy

public double computeEntropy(java.util.Vector data)

Function to compute the entropy of the set of data points.

Parameters:

data - The set of itemsets over is wanted to compute the entropy.

Returns:

The entropy of data.

alreadyUsedToDecompose

public boolean alreadyUsedToDecompose(keel.Algorithms.Decision_Trees.ID3.Node node,
                                      int attribute,
                                      int value)

Function to check if the specified attribute and value are already used to decompose the data.

Parameters:

node - The node to check at this time.

attribute - The attribute to check.

value - The value to check.

Returns:

True if the attribute and the values are already used to decompose, or false otherwise.

decomposeNode

public void decomposeNode(keel.Algorithms.Decision_Trees.ID3.Node node)

Function to decompose the specified node.

Parameters:

node - The node to decompose.

printResult

public void printResult()
                 throws java.io.IOException

Writes the tree and the results of the training and the test in the file.

Specified by:

printResult in class Algorithm

Throws:

java.io.IOException - If the file cannot be written.

printTrain

public void printTrain()

Evaluates the training dataset and writes the results in the file.

Specified by:

printTrain in class Algorithm

printTest

public void printTest()

Evaluates the test dataset and writes the results in the file.

Specified by:

printTest in class Algorithm

main

public static void main(java.lang.String[] args)

Main function.

Parameters:

args - The parameters file.