MyDataset

java.lang.Object
- keel.Algorithms.Rule_Learning.Slipper.MyDataset

```
public class MyDataset
extends java.lang.Object
```
Contains the methods to read a Classification/Regression Dataset

Since:

JDK1.2

Version:

1.1

Author:

Written by Alberto Fernández (University of Granada) 19/08/2008, Modified by Xavi Solé (La Salle, Ramón Llull University - Barcelona) 03/12/2008

Field Summary

Fields
Modifier and Type	Field and Description
`static int`	`INTEGER` Number to represent type of variable integer.
`static int`	`NOMINAL` Number to represent type of variable nominal.
`static int`	`REAL` Number to represent type of variable real or double.

Constructor Summary

Constructors
Constructor and Description

MyDataset()
Init a new set of instances

Constructors
Constructor and Description
`MyDataset()` Init a new set of instances

Method Summary

All Methods Instance Methods Concrete Methods
Modifier and Type	Method and Description
`double`	`average(int position)` It return the average of an specific attribute
`java.lang.String[]`	`classify(Mask actives, Ruleset[] rulesets, int length)` Classifies the entries' classes according to several sets of rules.
`java.lang.String[]`	`classify(Ruleset[] rulesets, int length)` Classifies the entries' classes according to several sets of rules.
`void`	`computeInstancesPerClass()` It computes the number the instances per class.
`java.lang.String`	`copyHeader()` It copies the header of the dataset
`double[][]`	`devuelveRangos()` Returns the minimum and maximum values of every attributes as a matrix.
`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, 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 value)` It filters the instances of a given class from this dataset; i.e., it deactivates the instances from the other class.
`double[]`	`getemax()` It returns an array with the maximum values of the attributes
`double[]`	`getemin()` It returns an array with the minimum values of the attributes
`double[]`	`getExample(int pos)` Output a specific example
`double[]`	`getExample(Mask mask)` Output a specific example
`double`	`getMax(int variable)` It returns the maximum value of the attribute specified
`double`	`getMin(int variable)` It returns the minimum value of the attribute specified
`int`	`getnClasses()` It gets the number of output attributes of the data-set (for example number of classes in classification)
`int`	`getnData()` It gets the size of the data-set
`int`	`getnInputs()` It gets the number of input attributes of the data-set
`int`	`getnVars()` It gets the number of variables of the data-set (including the output)
`int[]`	`getOutputAsInteger()` Returns the output of the data-set as integer values
`int`	`getOutputAsInteger(int pos)` It returns the output value of the example "pos"
`double[]`	`getOutputAsReal()` Returns the output of the data-set as real values
`double`	`getOutputAsReal(int pos)` It returns the output value of the example "pos"
`java.lang.String[]`	`getOutputAsString()` Returns the output of the data-set as nominal values
`java.lang.String`	`getOutputAsString(int pos)` It returns the output value of the example "pos"
`java.lang.String`	`getOutputValue(int intValue)` It returns the name of the class of index intValue
`double[][]`	`getX()` Outputs an array of examples with their corresponding attribute values.
`boolean`	`hasMissingAttributes()` It checks if the data-set has any missing value
`boolean`	`hasNumericalAttributes()` It checks if the data-set has any numerical value
`boolean`	`hasRealAttributes()` It checks if the data-set has any real value
`boolean`	`isMissing(int i, int j)` This function checks if the attribute value is missing
`boolean`	`isMissing(Mask mask, int j)` This function checks if the attribute value is missing
`void`	`normalize()` It transform the input space into the [0,1] range
`int`	`numberInstances(int clas)` It returns the number of instances in the dataset of the given class
`int`	`numberValues(int attribute)` It returns the number of different values of an attribute
`void`	`readClassificationSet(java.lang.String datasetFile, boolean train)` It reads the whole input data-set and it stores each example and its associated output value in local arrays to ease their use.
`void`	`readRegressionSet(java.lang.String datasetFile, boolean train)` It reads the whole input data-set and it stores each example and its associated output value in local arrays to ease their use.
`int`	`size()` It returns the number of examples
`int`	`sizeWithoutMissing()` It return the size of the data-set without having account the missing values
`double`	`stdDev(int position)` It return the standard deviation of an specific attribute
`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.
`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.
`java.lang.String`	`toString(Mask mask, double[] distribution)` Returns a string representation of the active entries of this MyDataset wiht its associated weights.

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

- Field Detail
  - REAL
```
public static final int REAL
```
    Number to represent type of variable real or double.
    
    See Also:
    
    Constant Field Values
  - INTEGER
```
public static final int INTEGER
```
    Number to represent type of variable integer.
    
    See Also:
    
    Constant Field Values
  - NOMINAL
```
public static final int NOMINAL
```
    Number to represent type of variable nominal.
    
    See Also:
    
    Constant Field Values
- Constructor Detail
  - MyDataset
```
public MyDataset()
```
    Init a new set of instances
- Method Detail
  - getX
```
public double[][] getX()
```
    Outputs an array of examples with their corresponding attribute values.
    
    Returns:
    
    double[][] an array of examples with their corresponding attribute values
  - 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
  - getOutputAsInteger
```
public int[] getOutputAsInteger()
```
    Returns the output of the data-set as integer values
    
    Returns:
    
    int[] an array of integer values corresponding to the output values of the dataset
  - getOutputAsReal
```
public double[] getOutputAsReal()
```
    Returns the output of the data-set as real values
    
    Returns:
    
    double[] an array of real values corresponding to the output values of the dataset
  - getOutputAsString
```
public java.lang.String[] getOutputAsString()
```
    Returns the output of the data-set as nominal values
    
    Returns:
    
    String[] an array of nomianl values corresponding to the output values of the dataset
  - getOutputAsString
```
public java.lang.String getOutputAsString(int pos)
```
    It returns the output value of the example "pos"
    
    Parameters:
    
    pos - int the position (id) of the example
    
    Returns:
    
    String a string containing the output value
  - getOutputAsInteger
```
public int getOutputAsInteger(int pos)
```
    It returns the output value of the example "pos"
    
    Parameters:
    
    pos - int the position (id) of the example
    
    Returns:
    
    int an integer containing the output value
  - getOutputAsReal
```
public double getOutputAsReal(int pos)
```
    It returns the output value of the example "pos"
    
    Parameters:
    
    pos - int the position (id) of the example
    
    Returns:
    
    double a real containing the output value
  - getemax
```
public double[] getemax()
```
    It returns an array with the maximum values of the attributes
    
    Returns:
    
    double[] an array with the maximum values of the attributes
  - getemin
```
public double[] getemin()
```
    It returns an array with the minimum values of the attributes
    
    Returns:
    
    double[] an array with the minimum values of the attributes
  - getMax
```
public double getMax(int variable)
```
    It returns the maximum value of the attribute specified
    
    Parameters:
    
    variable - index of the attribute
    
    Returns:
    
    the maximum value of the attribute
  - getMin
```
public double getMin(int variable)
```
    It returns the minimum value of the attribute specified
    
    Parameters:
    
    variable - index of the attribute
    
    Returns:
    
    the minimum value of the attribute
  - getnData
```
public int getnData()
```
    It gets the size of the data-set
    
    Returns:
    
    int the number of examples in the data-set
  - getnVars
```
public int getnVars()
```
    It gets the number of variables of the data-set (including the output)
    
    Returns:
    
    int the number of variables of the data-set (including the output)
  - getnInputs
```
public int getnInputs()
```
    It gets the number of input attributes of the data-set
    
    Returns:
    
    int the number of input attributes of the data-set
  - getnClasses
```
public int getnClasses()
```
    It gets the number of output attributes of the data-set (for example number of classes in classification)
    
    Returns:
    
    int the number of different output values of the data-set
  - isMissing
```
public boolean isMissing(int i,
                         int j)
```
    This function checks if the attribute value is missing
    
    Parameters:
    
    i - int Example id
    
    j - int Variable id
    
    Returns:
    
    boolean True is the value is missing, else it returns false
  - isMissing
```
public boolean isMissing(Mask mask,
                         int j)
```
    This function checks if the attribute value is missing
    
    Parameters:
    
    mask - Mask
    
    j - int Variable id
    
    Returns:
    
    boolean True is the value is missing, else it returns false
  - readClassificationSet
```
public void readClassificationSet(java.lang.String datasetFile,
                                  boolean train)
                           throws java.io.IOException
```
    It reads the whole input data-set and it stores each example and its associated output value in local arrays to ease their use.
    
    Parameters:
    
    datasetFile - String name of the file containing the dataset
    
    train - boolean It must have the value "true" if we are reading the training data-set
    
    Throws:
    
    java.io.IOException - If there ocurs any problem with the reading of the data-set
  - readRegressionSet
```
public void readRegressionSet(java.lang.String datasetFile,
                              boolean train)
                       throws java.io.IOException
```
    It reads the whole input data-set and it stores each example and its associated output value in local arrays to ease their use.
    
    Parameters:
    
    datasetFile - String name of the file containing the dataset
    
    train - boolean It must have the value "true" if we are reading the training data-set
    
    Throws:
    
    java.io.IOException - If there ocurs any problem with the reading of the data-set
  - copyHeader
```
public java.lang.String copyHeader()
```
    It copies the header of the dataset
    
    Returns:
    
    String A string containing all the data-set information
  - normalize
```
public void normalize()
```
    It transform the input space into the [0,1] range
  - hasRealAttributes
```
public boolean hasRealAttributes()
```
    It checks if the data-set has any real value
    
    Returns:
    
    boolean True if it has some real values, else false.
  - hasNumericalAttributes
```
public boolean hasNumericalAttributes()
```
    It checks if the data-set has any numerical value
    
    Returns:
    
    boolean True if it has some numerical values, else false.
  - hasMissingAttributes
```
public boolean hasMissingAttributes()
```
    It checks if the data-set has any missing value
    
    Returns:
    
    boolean True if it has some missing values, else false.
  - sizeWithoutMissing
```
public int sizeWithoutMissing()
```
    It return the size of the data-set without having account the missing values
    
    Returns:
    
    int the size of the data-set without having account the missing values
  - size
```
public int size()
```
    It returns the number of examples
    
    Returns:
    
    the number of examples
  - stdDev
```
public double stdDev(int position)
```
    It return the standard deviation of an specific attribute
    
    Parameters:
    
    position - int attribute id (position of the attribute)
    
    Returns:
    
    double the standard deviation of the attribute
  - average
```
public double average(int position)
```
    It return the average of an specific attribute
    
    Parameters:
    
    position - int attribute id (position of the attribute)
    
    Returns:
    
    double the average of the attribute
  - computeInstancesPerClass
```
public void computeInstancesPerClass()
```
    It computes the number the instances per class.
  - numberInstances
```
public int numberInstances(int clas)
```
    It returns the number of instances in the dataset of the given class
    
    Parameters:
    
    clas - the index of the class
    
    Returns:
    
    the number of instances in the dataset of the given class
  - numberValues
```
public int numberValues(int attribute)
```
    It returns the number of different values of an attribute
    
    Parameters:
    
    attribute - the index of the attribute
    
    Returns:
    
    the number of different values of an attribute
  - getOutputValue
```
public java.lang.String getOutputValue(int intValue)
```
    It returns the name of the class of index intValue
    
    Parameters:
    
    intValue - the index of the class
    
    Returns:
    
    the name of the class of index intValue
  - devuelveRangos
```
public double[][] devuelveRangos()
```
    Returns the minimum and maximum values of every attributes as a matrix. The matrix has a size of number_of_attributes x 2 ([nAttributes][2]). The minimum value is located at the first position of each array and the maximum, at the second.
    
    Returns:
    
    Matrix which stores the minimum and maximum values of every attributes.
  - 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: >,<= or = (Rule.GREATER,Rule.LOWER,Rule.EQUAL)
  - 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. An instance is not covered by the rule if the sum of the confidece of all the rules that "fire" it is smaller than the default rule's confidence. H(x) = sign (sum(Cr/r:x e R))
    
    Parameters:
    
    mask - Mask the mask with the active entries of the dataset
    
    rules - Ruleset the ruleset
  - filterByClass
```
public void filterByClass(Mask mask,
                          java.lang.String value)
```
    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
    
    value - 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: >,<= or = (Rule.GREATER,Rule.LOWER,Rule.EQUAL)
  - 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. An instance is covered by the rule if the sum of the confidece of all the rules that "fire" it is greater than the default rule's confidence. H(x) = sign (sum(Cr/r:x e R))
    
    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
  - 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.
  - 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.
  - 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(Mask mask,
                                 double[] distribution)
```
    Returns a string representation of the active entries of this MyDataset wiht its associated weights.
    
    Parameters:
    
    mask - Mask active entries
    
    distribution - a distribution of weights
    
    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

REAL

INTEGER

NOMINAL

Constructor Detail

MyDataset

Method Detail

getX

getExample

getExample

getOutputAsInteger

getOutputAsReal

getOutputAsString

getOutputAsString

getOutputAsInteger

getOutputAsReal

getemax

getemin

getMax

getMin

getnData

getnVars

getnInputs

getnClasses

isMissing

isMissing

readClassificationSet

readRegressionSet

copyHeader

normalize

hasRealAttributes

hasNumericalAttributes

hasMissingAttributes

sizeWithoutMissing

size

stdDev

average

computeInstancesPerClass

numberInstances

numberValues

getOutputValue

devuelveRangos

filter

filter

filter

filter

filterByClass

substract

substract

substract

substract

substract

classify

classify

toString

toString

toString