myDataset

java.lang.Object
- keel.Algorithms.Genetic_Rule_Learning.LogenPro.myDataset

```
public class myDataset
extends java.lang.Object
```
It contains the methods to read a Classification/Regression Dataset.

Since:

JDK 1.6

Version:

1.0

Author:

Written by Alberto Fernández (University of Granada) 01/01/2007, Modified by Francisco José Berlanga (University of Jaén) 09/12/2008

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
`void`	`computeInstancesPerClass()` It computes the number of intances per class
`java.lang.String`	`copyHeader()` It copies the header of the dataset
`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 "variable"
`double`	`getMin(int variable)` It returns the minimum value of the attribute "variable"
`boolean[]`	`getMissing(int pos)` It returns an array showing if the value of each attribute for the instance "pos" is missing (TRUE) or not (FALSE)
`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 nominal value for the class in the position "intValue"
`int`	`getType(int variable)` It returns the type for the attribute "variable"
`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 (real or integer) 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
`java.lang.String`	`nameVar(int pos)` It returns the name of the atribute in position "pos"
`void`	`normalize()` It transform the input space into the [0,1] range
`int`	`numberInstances(java.lang.String clas)` It computes the number of intances for the class "clas"
`int`	`numberValues(int attribute)` It returns the number of nominal values for the atributte "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`	`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 nominal 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 "variable"
    
    Parameters:
    
    variable - int Variable id
    
    Returns:
    
    int the maximum value of the attribute "variable"
  - getMin
```
public double getMin(int variable)
```
    It returns the minimum value of the attribute "variable"
    
    Parameters:
    
    variable - int Variable id
    
    Returns:
    
    int the minimum value of the attribute "variable"
  - 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
  - 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 (real or integer) 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
  - 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 of intances per class
  - numberInstances
```
public int numberInstances(java.lang.String clas)
```
    It computes the number of intances for the class "clas"
    
    Parameters:
    
    clas - String Name of the class
    
    Returns:
    
    int the number of intances for the class "clas"
  - numberValues
```
public int numberValues(int attribute)
```
    It returns the number of nominal values for the atributte "attribute"
    
    Parameters:
    
    attribute - int attribute id (position of the attribute)
    
    Returns:
    
    int the number of nominal values for the atributte "attribute"
  - getOutputValue
```
public java.lang.String getOutputValue(int intValue)
```
    It returns the nominal value for the class in the position "intValue"
    
    Parameters:
    
    intValue - int class id (position of the class)
    
    Returns:
    
    String the nominal value for the class in the position "intValue"
  - getType
```
public int getType(int variable)
```
    It returns the type for the attribute "variable"
    
    Parameters:
    
    variable - int attribute id (position of the attribute)
    
    Returns:
    
    int 1 if "variable" is an integer attribute; 0 if "variable" is a real attribute; 2 if "variable" is a nominal attribute; 0 (real) by default;
  - getMissing
```
public boolean[] getMissing(int pos)
```
    It returns an array showing if the value of each attribute for the instance "pos" is missing (TRUE) or not (FALSE)
    
    Parameters:
    
    pos - int Instance id
    
    Returns:
    
    bolean[] an array showing if the value of each attribute for the instance "pos" is missing (TRUE) or not (FALSE)
  - nameVar
```
public java.lang.String nameVar(int pos)
```
    It returns the name of the atribute in position "pos"
    
    Parameters:
    
    pos - int Variable id
    
    Returns:
    
    String the name of the atribute in position "pos"

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

normalize

hasRealAttributes

hasNumericalAttributes

hasMissingAttributes

sizeWithoutMissing

stdDev

average

computeInstancesPerClass

numberInstances

numberValues

getOutputValue

getType

getMissing

nameVar