myDataset

java.lang.Object
- keel.Algorithms.PSO_Learning.CPSO.myDataset

```
public class myDataset
extends java.lang.Object
```
Title: Dataset

Description: It contains the methods to read a Classification/Regression Dataset

Company: KEEL

Version:

1.0

Author:

Jose A. Saez Munoz

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
`int`	`ClasePredominante()` Returns the majority class with the not removed classes.
`void`	`computeInstancesPerClass()` It computes the number the instances per class.
`java.lang.String`	`copyHeader()` It copies the header of the dataset
`double`	`Desnormalizar(java.lang.Double valor, int numAtributo, double t)` Denormalizes a value given with the attribute and the fraction given
`double[][]`	`devuelveRangos()` Returns the minimum and maximum values of every attributes as a matrix.
`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`	`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
`boolean`	`getRemoved(int i)` Returns True if the i-th instance was removed, false otherwise.
`int`	`getTipo(int variable)` It returns the type of the attribute specified
`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
`int`	`noClasificadas()` Number of instances not removed.
`void`	`normalize(double t)` It transform the input space into the [0,t] 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
`int`	`NumClassesNotRemoved()` Returns the number of classes of the instances not removed.
`java.lang.Boolean`	`QuedanMasInstancias(double porc)` Checks if there are more instances to remove, depending on a threshold.
`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.
`void`	`setRemoved(int i, boolean value)` Sets the flag that indicate if an instance has been removed or not.
`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

Methods inherited from class java.lang.Object
clone, equals, finalize, getClass, hashCode, notify, notifyAll, toString, 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
  - 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
  - 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
  - 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
  - getTipo
```
public int getTipo(int variable)
```
    It returns the type of the attribute specified
    
    Parameters:
    
    variable - index of the attribute
    
    Returns:
    
    the type of the attribute specified
  - 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.
  - Desnormalizar
```
public double Desnormalizar(java.lang.Double valor,
                            int numAtributo,
                            double t)
```
    Denormalizes a value given with the attribute and the fraction given
    
    Parameters:
    
    valor - value to denormilize.
    
    numAtributo - attribute of the value to denormalize.
    
    t - fraction for the denormalization.
    
    Returns:
    
    result of the denormalization
  - ClasePredominante
```
public int ClasePredominante()
```
    Returns the majority class with the not removed classes.
    
    Returns:
    
    the majority class with the not removed classes.
  - normalize
```
public void normalize(double t)
```
    It transform the input space into the [0,t] range
    
    Parameters:
    
    t - maximum value in the interval.
  - NumClassesNotRemoved
```
public int NumClassesNotRemoved()
```
    Returns the number of classes of the instances not removed.
    
    Returns:
    
    the number of classes of the instances not removed.
  - getRemoved
```
public boolean getRemoved(int i)
```
    Returns True if the i-th instance was removed, false otherwise.
    
    Parameters:
    
    i - instance id.
    
    Returns:
    
    True if the i-th instance was removed, false otherwise.
  - setRemoved
```
public void setRemoved(int i,
                       boolean value)
```
    Sets the flag that indicate if an instance has been removed or not.
    
    Parameters:
    
    i - instance id.
    
    value - boolean flag. (true removed, false not)
  - noClasificadas
```
public int noClasificadas()
```
    Number of instances not removed.
    
    Returns:
    
    Number of instances not removed.
  - QuedanMasInstancias
```
public java.lang.Boolean QuedanMasInstancias(double porc)
```
    Checks if there are more instances to remove, depending on a threshold.
    
    Parameters:
    
    porc - threshold in percentage of the whole data size.
    
    Returns:
    
    True if there are more to remove, false otherwise.

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

getOutputAsInteger

getOutputAsReal

getOutputAsString

getOutputAsString

getOutputAsInteger

getOutputAsReal

getemax

getemin

getMax

getMin

getnData

getnVars

getnInputs

getnClasses

isMissing

readClassificationSet

readRegressionSet

copyHeader

hasRealAttributes

hasNumericalAttributes

hasMissingAttributes

sizeWithoutMissing

size

stdDev

average

computeInstancesPerClass

numberInstances

numberValues

getOutputValue

getTipo

devuelveRangos

Desnormalizar

ClasePredominante

normalize

NumClassesNotRemoved

getRemoved

setRemoved

noClasificadas

QuedanMasInstancias