keel.Algorithms.Decision_Trees.C45

Class Classification

java.lang.Object

keel.Algorithms.Decision_Trees.C45.Classification

public class Classification
extends java.lang.Object

Class to handle a classification of class values.

Since:

JDK 1.5

Version:

0.1

Author:

Written by Cristobal Romero (Universidad de Córdoba) 10/10/2007

Constructor Summary

Constructors

Constructor and Description
Classification(Classification toMerge) Function to create a new classification with only one value by merging all ç values of given classification.
Classification(Dataset source) Function to create a new classification with only one value.
Classification(Dataset source, Cut model) Function to create a new classification with the given dataset.
Classification(int numValues, int numClasses) Function to create and initialize a new classification.

Method Summary

Modifier and Type	Method and Description
void	add(int valueIndex, Itemset itemset) Function to add the given itemset to given the value.
void	addRange(int valueIndex, Dataset source, int start, int end) Function to add all itemsets in given range to given value.
void	addWeights(Itemset itemset, double[] weights) Funtion to add the given itemset to all values weighting it according to given weights.
void	addWithUnknownValue(Dataset source, int attIndex) Function to add all itemsets with unknown values for given attribute.
boolean	check(double minItemsets) Function to check if at least two values contain a minimum number of itemsets.
double	getTotal() Returns total weight of itemsets.
int	maxClass() Returns class with highest frequency over all values.
int	maxClass(int index) Returns class with highest frequency for given value.
int	maxValue() Returns index of value containing maximum number of itemsets.
int	numClasses() Returns number of classes.
double	numCorrect() Returns the weight of all itemsets of the class with highest frequency.
double	numCorrect(int index) Returns the number of correctly classified itemsets for the given value.
double	numIncorrect() Returns incorrectly classifed
double	numIncorrect(int index) Returns the number of incorrectly classified itemsets for the given value.
int	numValues() Returns number of values.
double	perClass(int classIndex) Returns number of itemsets of given class.
double	perClassPerValue(int valueIndex, int classIndex) Returns number of itemsets of given class in given value.
double	perValue(int valueIndex) Returns number of (possibly fractional) itemsets in given value.
double	probability(int classIndex) Returns relative frequency of class over all values.
double	probability(int classIndex, int attIndex) Returns relative frequency of class for given value.
void	shiftRange(int from, int to, Dataset source, int start, int end) Function to shift all itemsets in given range from one value to another.

Methods inherited from class java.lang.Object

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

Constructor Detail

Classification

public Classification(int numValues,
                      int numClasses)

Function to create and initialize a new classification.

Parameters:

numValues - Number of values used to make the classification.

numClasses - Number of classes used to make the classification.

Classification

public Classification(Dataset source)
               throws java.lang.Exception

Function to create a new classification with only one value.

Parameters:

source - The dataset.

Throws:

java.lang.Exception - If cannot read the dataset.

Classification

public Classification(Dataset source,
                      Cut model)
               throws java.lang.Exception

Function to create a new classification with the given dataset.

Parameters:

source - The dataset.

model - The model selected to make the classification.

Throws:

java.lang.Exception - If cannot build the classification.

Classification

public Classification(Classification toMerge)

Function to create a new classification with only one value by merging all ç values of given classification.

Parameters:

toMerge - The original classification to merge.

Method Detail

add

public final void add(int valueIndex,
                      Itemset itemset)

Function to add the given itemset to given the value.

Parameters:

valueIndex - The index of the value.

itemset - The itemset to add.

addWithUnknownValue

public final void addWithUnknownValue(Dataset source,
                                      int attIndex)

Function to add all itemsets with unknown values for given attribute.

Parameters:

source - The dataset that contains all the itemsets.

attIndex - The index of the attribute with possible unknown values.

addRange

public final void addRange(int valueIndex,
                           Dataset source,
                           int start,
                           int end)

Function to add all itemsets in given range to given value.

Parameters:

valueIndex - The index of the value.

source - The itemset to add.

start - The index of the first itemset to add.

end - The index of the first itemset that will not be added.

addWeights

public final void addWeights(Itemset itemset,
                             double[] weights)

Funtion to add the given itemset to all values weighting it according to given weights.

Parameters:

itemset - The itemset to add.

weights - The weights of the itemset for every value.

check

public final boolean check(double minItemsets)

Function to check if at least two values contain a minimum number of itemsets.

Parameters:

minItemsets - The minimum number of itemsets.

Returns:

True if the condition is satisfied. False otherwise.

maxValue

public final int maxValue()

Returns index of value containing maximum number of itemsets.

Returns:

Index of value containing maximum number.

maxClass

public final int maxClass()

Returns class with highest frequency over all values.

Returns:

The class with highest frequency

maxClass

public final int maxClass(int index)

Returns class with highest frequency for given value.

Parameters:

index - The index of the value.

Returns:

The class with highest frequency for given value.

numValues

public final int numValues()

Returns number of values.

Returns:

Number of values

numClasses

public final int numClasses()

Returns number of classes.

Returns:

Number of classes.

numCorrect

public final double numCorrect()

Returns the weight of all itemsets of the class with highest frequency.

Returns:

the weight of all itemsets of the class with highest frequency.

numIncorrect

public final double numIncorrect()

Returns incorrectly classifed

Returns:

incorrectly classifed.

numIncorrect

public final double numIncorrect(int index)

Returns the number of incorrectly classified itemsets for the given value.

Parameters:

index - The index of the value.

Returns:

The number of incorrectly classified itemsets for the given value.

numCorrect

public final double numCorrect(int index)

Returns the number of correctly classified itemsets for the given value.

Parameters:

index - The index of the value.

Returns:

Number of correctly classified itemsets for the given value.

getTotal

public final double getTotal()

Returns total weight of itemsets.

Returns:

total weight of itemsets.

perClassPerValue

public final double perClassPerValue(int valueIndex,
                                     int classIndex)

Returns number of itemsets of given class in given value.

Parameters:

valueIndex - The index of the value.

classIndex - The index of the class.

Returns:

number of itemsets of given class in given value.

perValue

public final double perValue(int valueIndex)

Returns number of (possibly fractional) itemsets in given value.

Parameters:

valueIndex - The index of the value.

Returns:

number of (possibly fractional) itemsets in given value.

perClass

public final double perClass(int classIndex)

Returns number of itemsets of given class.

Parameters:

classIndex - The index of the class.

Returns:

number of itemsets of given class.

probability

public final double probability(int classIndex)

Returns relative frequency of class over all values.

Parameters:

classIndex - The index of the class.

Returns:

relative frequency of class over all values.

probability

public final double probability(int classIndex,
                                int attIndex)

Returns relative frequency of class for given value.

Parameters:

classIndex - The index of the class.

attIndex - The index of the attribute.

Returns:

relative frequency of class for given value.

shiftRange

public final void shiftRange(int from,
                             int to,
                             Dataset source,
                             int start,
                             int end)

Function to shift all itemsets in given range from one value to another.

Parameters:

from - The minimum value.

to - The maximum value.

source - The dataset.

start - The index of the first itemset to add.

end - The index of the first itemset that will not be added.