MyDataset

java.lang.Object
- keel.Algorithms.Genetic_Rule_Learning.M5Rules.MyDataset

public class MyDataset
extends java.lang.Object

Class to implement the dataset

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 int[]`	`m_IndicesBuffer` Buffer of indices for sparse itemsets
`protected double[]`	`m_ValueBuffer` Buffer of values for sparse 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(MyDataset source, int first, int toCopy)` Creates a new set of itemsets by copying a subset of another set.
`MyDataset(java.lang.String name, boolean train)` Function to read the .dat file that contains the information of the dataset.
`MyDataset(java.lang.String name, java.util.Vector attInfo, int capacity)` Creates an empty set of itemsets.

Method Summary

All Methods Instance Methods Concrete Methods
Modifier and Type	Method and Description
`void`	`addItemset(Itemset itemset)` Function to add one itemset.
`double[]`	`attributeToDoubleArray(int index)` Gets the value of all itemsets in this dataset for a particular attribute.
`double`	`averageClassValue()` Returns the average class value of the set.
`double`	`averageClassValue(Rule r)` Returns the average class value of the instances covered by a rule.
`double`	`averagePredictedClassValue(Rule r)` Returns the average predicted class value of the instances covered by a rule.
`double`	`averageValue(int att)` Returns the average value for a given attribute of the set.
`boolean`	`checkInstance(Itemset itemset)` Checks if the given itemset is compatible with this dataset.
`java.lang.String[]`	`classify(Mask actives, Ruleset[] rulesets, int length)` Classifies the entries' classes according to several sets of rules.
`double[]`	`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.
`double[]`	`classify(java.util.Vector rules)` Classifies the entries' classes according to several rules.
`double`	`classPredictedSTD(Rule r)` Computes the standard deviation (over the predicted values) for the instances covered by a rule.
`double`	`classPredictedVariance(Rule r)` Computes the variance (over the predicted values) for the instances covered by a rule.
`double`	`classSTD()` Computes the standard deviation for the class attribute.
`double`	`classSTD(Rule r)` Computes the standard deviation for the instances covered by a rule.
`double`	`classVariance()` Computes the variance for the class attribute.
`double`	`classVariance(Rule r)` Computes the variance for the instances covered by a rule.
`void`	`compactify()` Compactifies the set of itemsets.
`java.lang.String`	`copyHeader()` It copies the header of the dataset
`void`	`delete()` Removes all itemsets from the set.
`void`	`delete(int index)` Function to remove an itemset at the given position.
`void`	`deleteAttributeAt(int position)` Deletes an attribute at the given position (0 to numAttributes() - 1).
`void`	`deleteWithMissing(int attIndex)` Function to remove all the attributes with missing value in the given attribute.
`void`	`deleteWithMissing(MyAttribute att)` Removes all itemsets with missing values for a particular attribute from the dataset.
`void`	`deleteWithMissingClass()` Removes all itemsets with a missing class value from the dataset.
`MyDataset`	`discretToBinary()` Transforms the discret attribute into numValues()-1 synthetic binary attributes.
`java.util.Enumeration`	`enumerateAttributes()` Enumerates all the attributes.
`java.util.Enumeration`	`enumerateItemsets()` Enumerates all the itemsets.
`boolean`	`equalHeaders(MyDataset dataset)` Checks if two headers are equivalent.
`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.
`Itemset`	`firstInstance()` Returns the first itemset in the set.
`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[]`	`getExample(int pos)` Output a specific example
`double[]`	`getExample(Mask mask)` Output a specific example
`java.lang.String`	`getName()` Returns the name of the dataset.
`void`	`insertAttributeAt(MyAttribute att, int position)` Inserts an attribute at the given position (0 to numAttributes()) and sets all values to be missing.
`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.
`double`	`meanOrMode(int attIndex)` Returns the mean (mode) for a numeric (nominal) attribute as a floating-point value.
`double`	`meanOrMode(MyAttribute att)` Returns the mean (mode) for a numeric (nominal) attribute as a floating-point value.
`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.
`double`	`ruleCorrelation(Rule r)` Computes the third heuristic exposed in [Holmes99]
`double`	`ruleDeviation(Rule r)` Computes the deviation of a rule from the predicted class values.
`double`	`ruleMeanAbsoluteError(Rule r)` Computes the mean absolute error of a rule for the predicted class values.
`void`	`setClass(MyAttribute att)` Sets the class attribute.
`void`	`setClassIndex(int classIndex)` Sets the class index of the set.
`void`	`setName(java.lang.String name)` Returns the name of the dataset.
`int`	`size()` It returns the number of exemple of the dataset
`void`	`sort(int attIndex)` Function to sort the dataset based on an attribute.
`void`	`sort(MyAttribute att)` Function to sort the dataset based on an attribute.
`int[][]`	`sortByAverageClassValues()` Computes the average class values for each attribute and value, and sort them by it.
`MyDataset[]`	`split(Rule r)` It split phisically the itemsets into two subdatasets, according to the coverage of a rule.
`void`	`stratify(int numFolds)` Stratifies a set of itemsets according to its class values if the class attribute is nominal (so that afterwards a stratified cross-validation can be performed).
`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.
`MyDataset`	`testCV(int numFolds, int numFold)` Creates the test set for one fold of a cross-validation on the dataset.
`java.lang.String`	`toString()` Returns a string representation of the entries of this MyDataset.
`java.lang.String`	`toString(Mask mask)` Returns a string representation of the active entries of this MyDataset.
`MyDataset`	`trainCV(int numFolds, int numFold)` Creates the training set for one fold of a cross-validation on the dataset.
`double`	`variance(int attIndex)` Computes the variance for a numeric attribute.
`double`	`variance(MyAttribute att)` Computes the variance for a numeric attribute.

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
  - m_ValueBuffer
```
protected double[] m_ValueBuffer
```
    Buffer of values for sparse itemsets
  - m_IndicesBuffer
```
protected int[] m_IndicesBuffer
```
    Buffer of indices for sparse itemsets
- 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(java.lang.String name,
                 java.util.Vector attInfo,
                 int capacity)
```
    Creates an empty set of itemsets. Uses the given attribute information. Sets the capacity of the set of itemsets to 0 if its negative. Given attribute information must not be changed after this constructor has been used.
    
    Parameters:
    
    name - the name of the relation
    
    attInfo - the attribute information
    
    capacity - the capacity of the set
  - MyDataset
```
public MyDataset(MyDataset dataset)
```
    Constructor that copies another dataset.
    
    Parameters:
    
    dataset - The dataset to be copied.
  - MyDataset
```
public MyDataset(MyDataset source,
                 int first,
                 int toCopy)
```
    Creates a new set of itemsets by copying a subset of another set.
    
    Parameters:
    
    source - the set of itemsets from which a subset is to be created
    
    first - the index of the first itemset to be copied
    
    toCopy - the number of itemsets to be copied
    
    Throws:
    
    java.lang.IllegalArgumentException - if first and toCopy are out of range
  - 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.
  - setName
```
public void setName(java.lang.String name)
```
    Returns the name of the dataset.
    
    Parameters:
    
    name - 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.
  - attributeToDoubleArray
```
public double[] attributeToDoubleArray(int index)
```
    Gets the value of all itemsets in this dataset for a particular attribute. Useful in conjunction with Utils.sort to allow iterating through the dataset in sorted order for some attribute.
    
    Parameters:
    
    index - the index of the attribute.
    
    Returns:
    
    an array containing the value of the desired attribute for each itemset in the dataset.
  - getClassAttribute
```
public final MyAttribute getClassAttribute()
```
    Returns class attribute.
    
    Returns:
    
    class attribute
  - setClass
```
public final void setClass(MyAttribute att)
```
    Sets the class attribute.
    
    Parameters:
    
    att - attribute to be the class
  - getClassIndex
```
public final int getClassIndex()
```
    Returns the index of the class attribute.
    
    Returns:
    
    the index of the class attribute.
  - setClassIndex
```
public final void setClassIndex(int classIndex)
```
    Sets the class index of the set. If the class index is negative there is assumed to be no class. (ie. it is undefined)
    
    Parameters:
    
    classIndex - the new class index
    
    Throws:
    
    java.lang.IllegalArgumentException - if the class index is too big or < 0
  - variance
```
public final double variance(int attIndex)
```
    Computes the variance for a numeric attribute.
    
    Parameters:
    
    attIndex - the numeric attribute
    
    Returns:
    
    the variance if the attribute is numeric
    
    Throws:
    
    java.lang.IllegalArgumentException - if the attribute is not numeric
  - variance
```
public final double variance(MyAttribute att)
```
    Computes the variance for a numeric attribute.
    
    Parameters:
    
    att - the numeric attribute
    
    Returns:
    
    the variance if the attribute is numeric
    
    Throws:
    
    java.lang.IllegalArgumentException - if the attribute is not numeric
  - classVariance
```
public final double classVariance()
```
    Computes the variance for the class attribute.
    
    Returns:
    
    the variance if the class is numeric
    
    Throws:
    
    java.lang.IllegalArgumentException - if the class is not numeric
  - classSTD
```
public final double classSTD()
```
    Computes the standard deviation for the class attribute.
    
    Returns:
    
    the standard deviation if the class is numeric
    
    Throws:
    
    java.lang.IllegalArgumentException - if the class is not numeric
  - classVariance
```
public final double classVariance(Rule r)
```
    Computes the variance for the instances covered by a rule.
    
    Parameters:
    
    r - the rule
    
    Returns:
    
    the variance if the class is numeric
    
    Throws:
    
    java.lang.IllegalArgumentException - if the class is not numeric
  - classSTD
```
public final double classSTD(Rule r)
```
    Computes the standard deviation for the instances covered by a rule.
    
    Parameters:
    
    r - the rule
    
    Returns:
    
    the standard deviation if the class is numeric
    
    Throws:
    
    java.lang.IllegalArgumentException - if the class is not numeric
  - classPredictedVariance
```
public final double classPredictedVariance(Rule r)
```
    Computes the variance (over the predicted values) for the instances covered by a rule.
    
    Parameters:
    
    r - the rule
    
    Returns:
    
    the variance if the class is numeric
    
    Throws:
    
    java.lang.IllegalArgumentException - if the class is not numeric
  - classPredictedSTD
```
public final double classPredictedSTD(Rule r)
```
    Computes the standard deviation (over the predicted values) for the instances covered by a rule.
    
    Parameters:
    
    r - the rule
    
    Returns:
    
    the standard deviation if the class is numeric
    
    Throws:
    
    java.lang.IllegalArgumentException - if the class is not numeric
  - meanOrMode
```
public final double meanOrMode(int attIndex)
```
    Returns the mean (mode) for a numeric (nominal) attribute as a floating-point value. Returns 0 if the attribute is neither nominal nor numeric. If all values are missing it returns zero.
    
    Parameters:
    
    attIndex - the attribute's index
    
    Returns:
    
    the mean or the mode
  - meanOrMode
```
public final double meanOrMode(MyAttribute att)
```
    Returns the mean (mode) for a numeric (nominal) attribute as a floating-point value. Returns 0 if the attribute is neither nominal nor numeric. If all values are missing it returns zero.
    
    Parameters:
    
    att - the attribute
    
    Returns:
    
    the mean or the mode
  - 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 class labels as an integer if the class attribute is nominal, 1 otherwise.
  - numItemsets
```
public final int numItemsets()
```
    Returns the number of itemsets.
    
    Returns:
    
    the number of itemsets.
  - compactify
```
public final void compactify()
```
    Compactifies the set of itemsets. Decreases the capacity of the set so that it matches the number of itemsets in the set.
  - delete
```
public final void delete()
```
    Removes all itemsets from the set.
  - 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.
  - checkInstance
```
public final boolean checkInstance(Itemset itemset)
```
    Checks if the given itemset is compatible with this dataset. Only looks at the size of the itemset and the ranges of the values for nominal and string attributes.
    
    Parameters:
    
    itemset - the itemset
    
    Returns:
    
    true if the itemset is compatible with the dataset
  - insertAttributeAt
```
public void insertAttributeAt(MyAttribute att,
                              int position)
```
    Inserts an attribute at the given position (0 to numAttributes()) and sets all values to be missing. Shallow copies the attribute before it is inserted, and performs a deep copy of the existing attribute information.
    
    Parameters:
    
    att - the attribute to be inserted
    
    position - the attribute's position
    
    Throws:
    
    java.lang.IllegalArgumentException - if the given index is out of range
  - deleteAttributeAt
```
public void deleteAttributeAt(int position)
```
    Deletes an attribute at the given position (0 to numAttributes() - 1). A deep copy of the attribute information is performed before the attribute is deleted.
    
    Parameters:
    
    position - the attribute's position
    
    Throws:
    
    java.lang.IllegalArgumentException - if the given index is out of range or the class attribute is being 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.
  - deleteWithMissing
```
public final void deleteWithMissing(MyAttribute att)
```
    Removes all itemsets with missing values for a particular attribute from the dataset.
    
    Parameters:
    
    att - the attribute
  - deleteWithMissingClass
```
public final void deleteWithMissingClass()
                                  throws java.lang.Exception
```
    Removes all itemsets with a missing class value from the dataset.
    
    Throws:
    
    java.lang.Exception - UnassignedClassException if class is not set
  - 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.
  - stratify
```
public final void stratify(int numFolds)
                    throws java.lang.Exception
```
    Stratifies a set of itemsets according to its class values if the class attribute is nominal (so that afterwards a stratified cross-validation can be performed).
    
    Parameters:
    
    numFolds - the number of folds in the cross-validation
    
    Throws:
    
    java.lang.Exception - UnassignedClassException if the class is not set
  - firstInstance
```
public final Itemset firstInstance()
```
    Returns the first itemset in the set.
    
    Returns:
    
    the first itemset in the set
  - 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.
  - equalHeaders
```
public final boolean equalHeaders(MyDataset dataset)
```
    Checks if two headers are equivalent.
    
    Parameters:
    
    dataset - another dataset
    
    Returns:
    
    true if the header of the given dataset is equivalent to this header
  - sort
```
public final void sort(int attIndex)
```
    Function to sort the dataset based on an attribute.
    
    Parameters:
    
    attIndex - The index of the attribute.
  - sort
```
public final void sort(MyAttribute att)
```
    Function to sort the dataset based on an attribute.
    
    Parameters:
    
    att - The attribute.
  - trainCV
```
public MyDataset trainCV(int numFolds,
                         int numFold)
```
    Creates the training set for one fold of a cross-validation on the dataset.
    
    Parameters:
    
    numFolds - the number of folds in the cross-validation. Must be greater than 1.
    
    numFold - 0 for the first fold, 1 for the second, ...
    
    Returns:
    
    the training set as a set of weighted itemsets
    
    Throws:
    
    java.lang.IllegalArgumentException - if the number of folds is less than 2 or greater than the number of itemsets.
  - testCV
```
public MyDataset testCV(int numFolds,
                        int numFold)
```
    Creates the test set for one fold of a cross-validation on the dataset.
    
    Parameters:
    
    numFolds - the number of folds in the cross-validation. Must be greater than 1.
    
    numFold - 0 for the first fold, 1 for the second, ...
    
    Returns:
    
    the test set as a set of weighted itemsets
    
    Throws:
    
    java.lang.IllegalArgumentException - if the number of folds is less than 2 or greater than the number of itemsets.
  - 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
  - ruleDeviation
```
public final double ruleDeviation(Rule r)
```
    Computes the deviation of a rule from the predicted class values. RMS=SQRT(Sum((Yi-yi)^2)/Nr) Yi=actual class value of the itemset i yi=predicted class value for the itemset i Nr=covered instances
    
    Parameters:
    
    r - the rule
    
    Returns:
    
    the deviation of a rule from the predicted class values.
    
    Throws:
    
    java.lang.IllegalArgumentException - if the class is not numeric
  - ruleMeanAbsoluteError
```
public final double ruleMeanAbsoluteError(Rule r)
```
    Computes the mean absolute error of a rule for the predicted class values. MAE/Cover=Sum(|Yi-yi|)/2Nr Yi=actual class value of the itemset i yi=predicted class value for the itemset i Nr=covered instances
    
    Parameters:
    
    r - the rule
    
    Returns:
    
    the mean absolute error of a rule for the predicted class values.
    
    Throws:
    
    java.lang.IllegalArgumentException - if the class is not numeric
  - ruleCorrelation
```
public final double ruleCorrelation(Rule r)
```
    Computes the third heuristic exposed in [Holmes99]. _ _ CC*Cover=Nr*Sum((Yi-Y)(yi-y))/(Nr*std(Y)*std(y)) Yi=actual class value of the itemset i yi=predicted class value for the itemset i Nr=covered instances (obvied->Nr/Nr) _ _ Y,y = means of its respective sets of values std(Y),std(y) = standard deviation of its respective sets of values
    
    Parameters:
    
    r - the rule
    
    Returns:
    
    the heuristic value.
    
    Throws:
    
    java.lang.IllegalArgumentException - if the class is not numeric
  - classify
```
public java.lang.String[] classify(Mask actives,
                                   Ruleset[] rulesets,
                                   int length)
```
    Classifies the entries' classes according to several sets of rules.
    
    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.
  - 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 double[] 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.
  - classify
```
public double[] 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.
  - 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.
  - 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).
  - averageClassValue
```
public double averageClassValue()
```
    Returns the average class value of the set.
    
    Returns:
    
    the average class value of the set.
  - averageClassValue
```
public double averageClassValue(Rule r)
```
    Returns the average class value of the instances covered by a rule.
    
    Parameters:
    
    r - the rule
    
    Returns:
    
    the average class value of the instances covered by a rule.
  - averagePredictedClassValue
```
public double averagePredictedClassValue(Rule r)
```
    Returns the average predicted class value of the instances covered by a rule.
    
    Parameters:
    
    r - the rule
    
    Returns:
    
    the average predicted class value of the instances covered by a rule.
  - averageValue
```
public double averageValue(int att)
```
    Returns the average value for a given attribute of the set.
    
    Parameters:
    
    att - the attribute's index
    
    Returns:
    
    the average value for a given attribute of the set.
  - discretToBinary
```
public MyDataset discretToBinary()
```
    Transforms the discret attribute into numValues()-1 synthetic binary attributes.
    
    Returns:
    
    a copy of this dataset with the aforementioned changes.
  - sortByAverageClassValues
```
public int[][] sortByAverageClassValues()
```
    Computes the average class values for each attribute and value, and sort them by it.
    
    Returns:
    
    the sorting of the values of each attribute, according its average class value.
  - 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.

Class MyDataset

Field Summary

Constructor Summary

Method Summary

Methods inherited from class java.lang.Object

Field Detail

name

attributes

itemsets

classIndex

IS

m_ValueBuffer

m_IndicesBuffer

Constructor Detail

MyDataset

MyDataset

MyDataset

MyDataset

MyDataset

Method Detail

addItemset

getName

setName

getAttribute

getAttribute

attributeToDoubleArray

getClassAttribute

setClass

getClassIndex

setClassIndex

variance

variance

classVariance

classSTD

classVariance

classSTD

classPredictedVariance

classPredictedSTD

meanOrMode

meanOrMode

numAttributes

numClasses

numItemsets

compactify

delete

delete

checkInstance

insertAttributeAt

deleteAttributeAt

deleteWithMissing

deleteWithMissing

deleteWithMissingClass

enumerateAttributes

enumerateItemsets

stratify

firstInstance

itemset

lastItemset

sumOfWeights

equalHeaders

sort

sort

trainCV

testCV

filter

filter

filter

filter

filter

filterByClass

substract

substract

substract

substract

substract

ruleDeviation

ruleMeanAbsoluteError

ruleCorrelation

classify

isMissing