MyDataset

java.lang.Object
- keel.Algorithms.Genetic_Rule_Learning.PART.MyDataset

```
public class MyDataset
extends java.lang.Object
```
Class to implement the dataset

Since:

JDK1.2

Version:

1.1

Author:

Written by Cristóbal Romero Morales (University of Oviedo) 01/07/2008, Modified by Xavi Solé (La Salle, Ramón Llull University - Barcelona) 03/12/2008

Field Summary

Fields
Modifier and Type	Field and Description
`protected java.util.Vector`	`attributes` The attributes.
`protected int`	`classIndex` The index of the class attribute.
`protected InstanceSet`	`IS` Keel dataset InstanceSet
`protected java.util.Vector`	`itemsets` The itemsets.
`protected java.lang.String`	`name` The name of the dataset.

Constructor Summary

Constructors
Constructor and Description
`MyDataset(MyDataset dataset)` Constructor that copies another dataset.
`MyDataset(MyDataset dataset, int capacity)` Constructor to copy all the attributes of another dataset but the itemsets.
`MyDataset(java.lang.String name, boolean train)` Function to read the .dat file that contains the information of the dataset.

Method Summary

All Methods Instance Methods Concrete Methods
Modifier and Type	Method and Description
`void`	`addItemset(Itemset itemset)` Function to add one itemset.
`java.lang.String[]`	`classify(Mask actives, Ruleset[] rulesets, int length)` Classifies the entries' classes according to several sets of rules.
`java.lang.String[]`	`classify(Mask actives, java.util.Vector rules)` Classifies the entries' classes according to several rules.
`java.lang.String[]`	`classify(Ruleset[] rulesets, int length)` Classifies the entries' classes according to several sets of rules.
`java.lang.String[]`	`classify(java.util.Vector rules)` Classifies the entries' classes according to several rules.
`java.lang.String`	`copyHeader()` It copies the header of the dataset
`void`	`delete(int index)` Function to remove an itemset at the given position.
`void`	`deleteWithMissing(int attIndex)` Function to remove all the attributes with missing value in the given attribute.
`java.util.Enumeration`	`enumerateAttributes()` Enumerates all the attributes.
`java.util.Enumeration`	`enumerateItemsets()` Enumerates all the itemsets.
`void`	`filter(Mask mask, int A, double V, int operator)` It filters the instances covered by a simple rule from this dataset; i.e., it deactivates the instances not covered by that rule.
`void`	`filter(Mask mask, Rule rule)` It filters the instances covered by a rule from this dataset; i.e., it deactivates the instances not covered by that rule.
`void`	`filter(Mask mask, Ruleset rules)` It filters the instances covered by a set of rule from this dataset; i.e., it deactivates the instances not covered by that ruleset.
`void`	`filter(Mask mask, Ruleset rules, int ignore)` It filters the instances covered by a set of rule from this dataset; i.e., it deactivates the instances not covered by that ruleset.
`void`	`filter(Mask mask, SimpleRule sr)` It filters the instances covered by a simple rule from this dataset; i.e., it deactivates the instances not covered by that rule.
`void`	`filterByClass(Mask mask, java.lang.String class_name)` It filters the instances of a given class from this dataset; i.e., it deactivates the instances from the other class.
`MyAttribute`	`getAttribute(int index)` Returns the attribute that has the index.
`MyAttribute`	`getAttribute(java.lang.String name)` Returns the attribute that has the name.
`MyAttribute`	`getClassAttribute()` Returns class attribute.
`int[]`	`getClassFequency()` Returns the frequency (number of instances) of each class.
`int[]`	`getClassFequency(Mask filter)` Returns the frequency (number of instances) of each class.
`int`	`getClassIndex()` Returns the index of the class attribute.
`double`	`getEntropy()` Returns the class entropy of this set.
`double`	`getEntropy(Mask filter)` Returns the class entropy of this set.
`double[]`	`getExample(int pos)` Output a specific example
`double[]`	`getExample(Mask mask)` Output a specific example
`java.lang.String`	`getMostFrequentClass()` Returns the most frequent class.
`java.lang.String`	`getName()` Returns the name of the dataset.
`boolean`	`isMissing(int exemple, int attribute)` It returns wether the value for an attribute in a given exemple is missing
`boolean`	`isMissing(Mask mask, int attribute)` It returns wether the value for an attribute in a given exemple is missing
`Itemset`	`itemset(int index)` Returns the itemset at the given position.
`Itemset`	`lastItemset()` Returns the last itemset.
`int`	`numAttributes()` Returns the number of attributes.
`int`	`numClasses()` Returns the number of possible values of the class attribute.
`int`	`numItemsets()` Returns the number of itemsets.
`int`	`size()` It returns the number of exemple of the dataset
`void`	`sort(int attIndex)` Function to sort the dataset based on an attribute.
`MyDataset[]`	`split(Rule r)` It split phisically the itemsets into two subdatasets, according to the coverage of a rule.
`void`	`substract(Mask mask, int A, double V, int operator)` It substracts the instances covered by a simple rule from this dataset; i.e., it deactivates the instances covered by that rule.
`void`	`substract(Mask mask, Rule rule)` It substracts the instances covered by a rule from this dataset; i.e., it deactivates the instances covered by that rule.
`void`	`substract(Mask mask, Ruleset rules)` It substracts the instances covered by a set of rule from this dataset; i.e., it deactivates the instances covered by that ruleset.
`void`	`substract(Mask mask, Ruleset rules, int ignore)` It substracts the instances covered by a set of rules from this dataset; i.e., it deactivates the instances covered by that ruleset.
`void`	`substract(Mask mask, SimpleRule sr)` It substracts the instances covered by a simple rule from this dataset; i.e., it deactivates the instances covered by that rule.
`double`	`sumOfWeights()` Function to compute the sum of all the weights of the itemsets.
`java.lang.String`	`toString()` Returns a string representation of the entries of this MyDataset.
`java.lang.String`	`toString(keel.Algorithms.Genetic_Rule_Learning.PART.IncrementalMask mask)` Returns a string representation of the active entries of this MyDataset.
`java.lang.String`	`toString(Mask mask)` Returns a string representation of the active entries of this MyDataset.

Methods inherited from class java.lang.Object
clone, equals, finalize, getClass, hashCode, notify, notifyAll, wait, wait, wait

- Field Detail
  - name
```
protected java.lang.String name
```
    The name of the dataset.
  - attributes
```
protected java.util.Vector attributes
```
    The attributes.
  - itemsets
```
protected java.util.Vector itemsets
```
    The itemsets.
  - classIndex
```
protected int classIndex
```
    The index of the class attribute.
  - IS
```
protected InstanceSet IS
```
    Keel dataset InstanceSet
- Constructor Detail
  - MyDataset
```
public MyDataset(java.lang.String name,
                 boolean train)
```
    Function to read the .dat file that contains the information of the dataset.
    
    Parameters:
    
    name - The reader object where the itemsets are readed.
    
    train - The flag if the file is for training
  - MyDataset
```
public MyDataset(MyDataset dataset)
```
    Constructor that copies another dataset.
    
    Parameters:
    
    dataset - The dataset to be copied.
  - MyDataset
```
public MyDataset(MyDataset dataset,
                 int capacity)
```
    Constructor to copy all the attributes of another dataset but the itemsets.
    
    Parameters:
    
    dataset - The dataset to be copied.
    
    capacity - The number of itemsets.
- Method Detail
  - addItemset
```
public final void addItemset(Itemset itemset)
```
    Function to add one itemset.
    
    Parameters:
    
    itemset - The itemset to add to the dataset.
  - getName
```
public java.lang.String getName()
```
    Returns the name of the dataset.
    
    Returns:
    
    the name of the dataset.
  - getAttribute
```
public final MyAttribute getAttribute(int index)
```
    Returns the attribute that has the index.
    
    Parameters:
    
    index - int The index of the attribute.
    
    Returns:
    
    the attribute that has the index.
  - getAttribute
```
public final MyAttribute getAttribute(java.lang.String name)
```
    Returns the attribute that has the name.
    
    Parameters:
    
    name - String The name of the attribute.
    
    Returns:
    
    the attribute that has the name.
  - getClassAttribute
```
public final MyAttribute getClassAttribute()
```
    Returns class attribute.
    
    Returns:
    
    class attribute
  - getClassIndex
```
public final int getClassIndex()
```
    Returns the index of the class attribute.
    
    Returns:
    
    the index of the class attribute.
  - numAttributes
```
public final int numAttributes()
```
    Returns the number of attributes.
    
    Returns:
    
    the number of attributes.
  - numClasses
```
public final int numClasses()
```
    Returns the number of possible values of the class attribute.
    
    Returns:
    
    the number of possible values of the class attribute.
  - numItemsets
```
public final int numItemsets()
```
    Returns the number of itemsets.
    
    Returns:
    
    the number of itemsets.
  - delete
```
public final void delete(int index)
```
    Function to remove an itemset at the given position.
    
    Parameters:
    
    index - The index of the itemset to be deleted.
  - deleteWithMissing
```
public final void deleteWithMissing(int attIndex)
```
    Function to remove all the attributes with missing value in the given attribute.
    
    Parameters:
    
    attIndex - The index of the attribute.
  - enumerateAttributes
```
public java.util.Enumeration enumerateAttributes()
```
    Enumerates all the attributes.
    
    Returns:
    
    An enumeration that contains all the attributes.
  - enumerateItemsets
```
public final java.util.Enumeration enumerateItemsets()
```
    Enumerates all the itemsets.
    
    Returns:
    
    An enumeration that contains all the itemsets.
  - itemset
```
public final Itemset itemset(int index)
```
    Returns the itemset at the given position.
    
    Parameters:
    
    index - The index of the itemset.
    
    Returns:
    
    the itemset at the given position.
  - lastItemset
```
public final Itemset lastItemset()
```
    Returns the last itemset.
    
    Returns:
    
    the last itemset
  - sumOfWeights
```
public final double sumOfWeights()
```
    Function to compute the sum of all the weights of the itemsets.
    
    Returns:
    
    The weight of all the itemsets.
  - sort
```
public final void sort(int attIndex)
```
    Function to sort the dataset based on an attribute.
    
    Parameters:
    
    attIndex - The index of the attribute.
  - filter
```
public void filter(Mask mask,
                   int A,
                   double V,
                   int operator)
```
    It filters the instances covered by a simple rule from this dataset; i.e., it deactivates the instances not covered by that rule.
    
    Parameters:
    
    mask - Mask the mask with the active entries of the dataset
    
    A - int attribute's id
    
    V - double attribute's value
    
    operator - int rule operator. It could be: Rule.EQUAL(for discret attributes), Rule.GREATER (>) or Rule.LOWER(<=)
  - filter
```
public void filter(Mask mask,
                   SimpleRule sr)
```
    It filters the instances covered by a simple rule from this dataset; i.e., it deactivates the instances not covered by that rule.
    
    Parameters:
    
    mask - Mask the mask with the actives entries of the dataset
    
    sr - SimpleRule the rule
  - filter
```
public void filter(Mask mask,
                   Rule rule)
```
    It filters the instances covered by a rule from this dataset; i.e., it deactivates the instances not covered by that rule.
    
    Parameters:
    
    mask - Mask the mask with the active entries of the dataset
    
    rule - Rule the rule
  - filter
```
public void filter(Mask mask,
                   Ruleset rules)
```
    It filters the instances covered by a set of rule from this dataset; i.e., it deactivates the instances not covered by that ruleset.
    
    Parameters:
    
    mask - Mask the mask with the active entries of the dataset
    
    rules - Ruleset the ruleset
  - filter
```
public void filter(Mask mask,
                   Ruleset rules,
                   int ignore)
```
    It filters the instances covered by a set of rule from this dataset; i.e., it deactivates the instances not covered by that ruleset.
    
    Parameters:
    
    mask - Mask the mask with the active entries of the dataset
    
    rules - Ruleset the ruleset
    
    ignore - int the algorithm ignores the i-th rule of the ruleset
  - filterByClass
```
public void filterByClass(Mask mask,
                          java.lang.String class_name)
```
    It filters the instances of a given class from this dataset; i.e., it deactivates the instances from the other class.
    
    Parameters:
    
    mask - Mask the mask whit the active entries of the dataset
    
    class_name - String the name of the class
  - substract
```
public void substract(Mask mask,
                      int A,
                      double V,
                      int operator)
```
    It substracts the instances covered by a simple rule from this dataset; i.e., it deactivates the instances covered by that rule.
    
    Parameters:
    
    mask - Mask the mask with the active entries of the dataset
    
    A - int attribute's id
    
    V - double attribute's value
    
    operator - int rule operator. It could be: Rule.EQUAL(for discret attributes), Rule.GREATER (>) or Rule.LOWER(<=)
  - substract
```
public void substract(Mask mask,
                      SimpleRule sr)
```
    It substracts the instances covered by a simple rule from this dataset; i.e., it deactivates the instances covered by that rule.
    
    Parameters:
    
    mask - Mask the mask with the active entries of the dataset
    
    sr - SimpleRule the rule
  - substract
```
public void substract(Mask mask,
                      Rule rule)
```
    It substracts the instances covered by a rule from this dataset; i.e., it deactivates the instances covered by that rule.
    
    Parameters:
    
    mask - Mask the mask with the active entries of the dataset
    
    rule - Rule the rule
  - substract
```
public void substract(Mask mask,
                      Ruleset rules)
```
    It substracts the instances covered by a set of rule from this dataset; i.e., it deactivates the instances covered by that ruleset.
    
    Parameters:
    
    mask - Mask the mask with the active entries of the dataset
    
    rules - Ruleset the set of rules
  - substract
```
public void substract(Mask mask,
                      Ruleset rules,
                      int ignore)
```
    It substracts the instances covered by a set of rules from this dataset; i.e., it deactivates the instances covered by that ruleset. This method allows to ignore a rule of the set.
    
    Parameters:
    
    mask - Mask the mask with the active entries of the dataset
    
    rules - Ruleset the set of rules
    
    ignore - int number of the rule to ignore
  - split
```
public MyDataset[] split(Rule r)
```
    It split phisically the itemsets into two subdatasets, according to the coverage of a rule.
    
    Parameters:
    
    r - the rule
    
    Returns:
    
    two dataset, the first one (0) with the exemples covered by the rule, the second one (1) with the rest of them (the uncoverd exemples).
  - classify
```
public java.lang.String[] classify(Mask actives,
                                   Ruleset[] rulesets,
                                   int length)
```
    Classifies the entries' classes according to several sets of rules. The last of wich is a default ruleset.
    
    Parameters:
    
    actives - Mask active entries of the dataset
    
    rulesets - Ruleset[] the rulesets
    
    length - int the number of rulesets
    
    Returns:
    
    a vector of the length of this dataset with the class name for each entry.
  - classify
```
public java.lang.String[] classify(Mask actives,
                                   java.util.Vector rules)
```
    Classifies the entries' classes according to several rules. The last of wich is a default rule.
    
    Parameters:
    
    actives - Mask active entries of the dataset
    
    rules - Vector the rules vector
    
    Returns:
    
    a vector of the length of this dataset with the class name for each entry.
  - isMissing
```
public boolean isMissing(int exemple,
                         int attribute)
```
    It returns wether the value for an attribute in a given exemple is missing
    
    Parameters:
    
    exemple - int index of the exemple in the dataset
    
    attribute - int index of the attribute
    
    Returns:
    
    true if the value for an attribute in a given exemple is missing
  - isMissing
```
public boolean isMissing(Mask mask,
                         int attribute)
```
    It returns wether the value for an attribute in a given exemple is missing
    
    Parameters:
    
    mask - Mask the index of the mask signs the given exemple
    
    attribute - int index of the attribute
    
    Returns:
    
    true if the value for an attribute in a given exemple is missing
  - size
```
public int size()
```
    It returns the number of exemple of the dataset
    
    Returns:
    
    the number of exemple of the dataset
  - classify
```
public java.lang.String[] classify(Ruleset[] rulesets,
                                   int length)
```
    Classifies the entries' classes according to several sets of rules.
    
    Parameters:
    
    rulesets - Ruleset[] the rulesets
    
    length - int the number of rulesets
    
    Returns:
    
    a vector of the length of this dataset with the class name for each entry.
  - classify
```
public java.lang.String[] classify(java.util.Vector rules)
```
    Classifies the entries' classes according to several rules. The last of wich is a default rule.
    
    Parameters:
    
    rules - Vector the rules vector
    
    Returns:
    
    a vector of the length of this dataset with the class name for each entry.
  - getExample
```
public double[] getExample(int pos)
```
    Output a specific example
    
    Parameters:
    
    pos - int position (id) of the example in the data-set
    
    Returns:
    
    double[] the attributes of the given example
  - getExample
```
public double[] getExample(Mask mask)
```
    Output a specific example
    
    Parameters:
    
    mask - Mask with the position (id) of the example in the data-set
    
    Returns:
    
    double[] the attributes of the given example
  - getClassFequency
```
public int[] getClassFequency()
```
    Returns the frequency (number of instances) of each class.
    
    Returns:
    
    the frequency (number of instances) of each class.
  - getMostFrequentClass
```
public java.lang.String getMostFrequentClass()
```
    Returns the most frequent class.
    
    Returns:
    
    the most frequent class.
  - getClassFequency
```
public int[] getClassFequency(Mask filter)
```
    Returns the frequency (number of instances) of each class.
    
    Parameters:
    
    filter - Mask filter
    
    Returns:
    
    the frequency (number of instances) of each class.
  - getEntropy
```
public double getEntropy()
```
    Returns the class entropy of this set.
    
    Returns:
    
    the class entropy of this set.
  - getEntropy
```
public double getEntropy(Mask filter)
```
    Returns the class entropy of this set.
    
    Parameters:
    
    filter - Mask only active entries.
    
    Returns:
    
    the class entropy of this set.
  - copyHeader
```
public java.lang.String copyHeader()
```
    It copies the header of the dataset
    
    Returns:
    
    String A string containing all the data-set information
  - toString
```
public java.lang.String toString()
```
    Returns a string representation of the entries of this MyDataset.
    
    Overrides:
    
    toString in class java.lang.Object
    
    Returns:
    
    a string representation of the entries of this MyDataset.
  - toString
```
public java.lang.String toString(Mask mask)
```
    Returns a string representation of the active entries of this MyDataset.
    
    Parameters:
    
    mask - Mask active entries
    
    Returns:
    
    a string representation of the active entries of this MyDataset.
  - toString
```
public java.lang.String toString(keel.Algorithms.Genetic_Rule_Learning.PART.IncrementalMask mask)
```
    Returns a string representation of the active entries of this MyDataset.
    
    Parameters:
    
    mask - IncrementalMask active entries
    
    Returns:
    
    a string representation of the active entries of this MyDataset.

Class MyDataset

Field Summary

Constructor Summary

Method Summary

Methods inherited from class java.lang.Object

Field Detail

name

attributes

itemsets

classIndex

IS

Constructor Detail

MyDataset

MyDataset

MyDataset

Method Detail

addItemset

getName

getAttribute

getAttribute

getClassAttribute

getClassIndex

numAttributes

numClasses

numItemsets

delete

deleteWithMissing

enumerateAttributes

enumerateItemsets

itemset

lastItemset

sumOfWeights

sort

filter

filter

filter

filter

filter

filterByClass

substract

substract

substract

substract

substract

split

classify

classify

isMissing

isMissing

size

classify

classify

getExample

getExample

getClassFequency

getMostFrequentClass

getClassFequency

getEntropy

getEntropy

copyHeader

toString

toString

toString