myDataset

java.lang.Object
- keel.Algorithms.ImbalancedClassification.Ensembles.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:

Alberto Fernandez

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
`myDataset(myDataset copia)` Generates a new binary dataset by copying it from the dataset given.
`myDataset(myDataset copia, double[] distribution)` Generates a new binary dataset by copying it from the dataset given.
`myDataset(myDataset copia, int majC, double[][] Xmaj, int minC, double[][] Xmin)` It generates a new binary dataset
`myDataset(myDataset copia, int clase_1, int clase_2)` It generates a new binary dataset
`myDataset(myDataset copia, int clase_1, int clase_2, int[] empate)` It generates a new binary dataset by copying the instances indicated by the value 1 in the array given.
`myDataset(myDataset copia, java.lang.String bagType)` Generates a new binary dataset by copying it from the dataset given and preprocessing it.

Method Summary

All Methods Static Methods Instance Methods Concrete Methods
Modifier and Type	Method and Description
`java.util.List<java.lang.Integer>`	`asList(int[] is)` Returns a List with the elements of the array given.
`double`	`average(int position)` It return the average of an specific attribute
`java.lang.String`	`claseMasFrecuente()` It returns the most frequent class in the dataset
`int`	`claseNumerica(java.lang.String valorNominal)` Returns a numeric representation of a class nominal value given as argument.
`void`	`computeInstancesPerClass()` It computes the number of examples per class.
`void`	`computeIR()` Computes the Imbalanced Rate for each class.
`void`	`computeStatisticsPerClass()` It computes the average and standard deviation of the input attributes
`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`	`discretize(int intervalos)` Uniform width discretization
`double[][]`	`getAveragePerClass()` Returns the average values per 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`	`getIR(int clase)` Returns the Imbalanced Rate for the class given.
`InstanceSet`	`getIS()` Returns the Instance set stored.
`double`	`getMax(int variable)` It returns the maximum value of the given attribute
`double`	`getMin(int variable)` It returns the minimum value of the given attribute
`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 output value (string) which matchs with a given integer value
`double[][]`	`getStdPerClass()` Returns the standard deviation per class.
`int`	`getTipo(int variable)` It returns the type of an attribute
`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[]`	`importanceSampling(myDataset copia, int size, boolean[] oob, double oobErr)` Importance undersampling of the dataset given.
`boolean`	`isMissing(int i, int j)` This function checks if the attribute value is missing
`java.lang.String`	`nombreClase(int clase)` Returns the nominal value for a class represented by the integer given.
`java.lang.String[]`	`nombres()` It returns the names for all input variables
`java.lang.String`	`nombreVar(int pos)` It returns the attribute name of a given variable
`void`	`normalize()` It transform the input space into the [0,1] range
`int`	`numberInstances(int clas)` It returns the number of instances in the data set for a given class.
`int`	`numberValues(int attribute)` Function to get the number of different feasible values for a given attribute
`int`	`numEjemplos(int clase)` It returns the number of instances in the data set for a given class.
`java.lang.String`	`printDataSet()` Returns a string representation of the dataset.
`int[]`	`randomSampling(myDataset copia, int majC, int minC, int a)` Random undersampling of the dataset given.
`int[]`	`randomSampling(myDataset copia, int majC, int minC, int nMaj, int nMin)` Random undersampling of the dataset given.
`int[]`	`randomUnderSampling(myDataset copia, int majC, int N)` Original dataset to take examples from and the % of majority class in the new data set
`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`	`readInstanceSet(InstanceSet IS)` 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 return the size of the data-set
`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
`int`	`totalNominales(int atributo)` It returns the number of nominal values for a given variable
`boolean`	`vacio()` Checks if there is a class without instances.
`static java.lang.String`	`valorNominal(int atributo, double valorReal)` Returns a nominal representation of a attribute's real value given as argument.
`static double`	`valorReal(int atributo, java.lang.String valorNominal)` Returns a real representation of a attribute's nominal value given as argument.

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
  - myDataset
```
public myDataset(myDataset copia,
                 double[] distribution)
```
    Generates a new binary dataset by copying it from the dataset given.
    
    Parameters:
    
    copia - the original training dataset
    
    distribution - dataset distribution.
  - myDataset
```
public myDataset(myDataset copia)
```
    Generates a new binary dataset by copying it from the dataset given.
    
    Parameters:
    
    copia - the original training dataset
  - myDataset
```
public myDataset(myDataset copia,
                 int clase_1,
                 int clase_2)
```
    It generates a new binary dataset
    
    Parameters:
    
    copia - the original training dataset
    
    clase_1 - first class
    
    clase_2 - second class
  - myDataset
```
public myDataset(myDataset copia,
                 java.lang.String bagType)
```
    Generates a new binary dataset by copying it from the dataset given and preprocessing it.
    
    Parameters:
    
    copia - the original training dataset
    
    bagType - type of preprocessing algorithm (OVERBAGGING, UNDERBAGGING)
  - myDataset
```
public myDataset(myDataset copia,
                 int clase_1,
                 int clase_2,
                 int[] empate)
```
    It generates a new binary dataset by copying the instances indicated by the value 1 in the array given.
    
    Parameters:
    
    copia - the original training dataset
    
    clase_1 - first class
    
    clase_2 - second class
    
    empate - the instances with the value 1 in this array will be copied.
  - myDataset
```
public myDataset(myDataset copia,
                 int majC,
                 double[][] Xmaj,
                 int minC,
                 double[][] Xmin)
```
    It generates a new binary dataset
    
    Parameters:
    
    copia - the original training dataset
    
    majC - majority class.
    
    Xmaj - instances belonging to majority class.
    
    minC - minority class.
    
    Xmin - instances belonging to minority class.
- Method Detail
  - randomUnderSampling
```
public int[] randomUnderSampling(myDataset copia,
                                 int majC,
                                 int N)
```
    Original dataset to take examples from and the % of majority class in the new data set
    
    Parameters:
    
    copia - original training dataset.
    
    majC - majority class.
    
    N - number instances to be selected
    
    Returns:
    
    array of selected instances indeces.
  - randomSampling
```
public int[] randomSampling(myDataset copia,
                            int majC,
                            int minC,
                            int a)
```
    Random undersampling of the dataset given. Original dataset to take examples from and the % of majority class in the new data set
    
    Parameters:
    
    copia - original dataset.
    
    majC - majority class.
    
    minC - minority class.
    
    a - % of majority class to be selected.
    
    Returns:
    
    array of selected instances indeces.
  - randomSampling
```
public int[] randomSampling(myDataset copia,
                            int majC,
                            int minC,
                            int nMaj,
                            int nMin)
```
    Random undersampling of the dataset given.
    
    Parameters:
    
    copia - original dataset.
    
    majC - majority class.
    
    minC - minority class.
    
    nMaj - number of majority class instances to be selected.
    
    nMin - number of minority class instances to be selected.
    
    Returns:
    
    array of selected instances indeces.
  - importanceSampling
```
public boolean[] importanceSampling(myDataset copia,
                                    int size,
                                    boolean[] oob,
                                    double oobErr)
```
    Importance undersampling of the dataset given.
    
    Parameters:
    
    copia - original dataset.
    
    size - size of the sampling.
    
    oob - oob to be considered.
    
    oobErr - oob error.
    
    Returns:
    
    boolean array of selected instances (true if they are).
  - 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 given attribute
    
    Parameters:
    
    variable - the index of the attribute
    
    Returns:
    
    the maximum value of the given attribute
  - getMin
```
public double getMin(int variable)
```
    It returns the minimum value of the given attribute
    
    Parameters:
    
    variable - the index of the attribute
    
    Returns:
    
    the minimum value of the given 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
  - readInstanceSet
```
public void readInstanceSet(InstanceSet IS)
                     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:
    
    IS - Instance set given.
    
    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
  - computeStatisticsPerClass
```
public void computeStatisticsPerClass()
```
    It computes the average and standard deviation of the input attributes
  - getAveragePerClass
```
public double[][] getAveragePerClass()
```
    Returns the average values per class.
    
    Returns:
    
    the average values per class.
  - getStdPerClass
```
public double[][] getStdPerClass()
```
    Returns the standard deviation per class.
    
    Returns:
    
    the standard deviation per class.
  - 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 return the size of the data-set
    
    Returns:
    
    the size of the data-set
  - 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 examples per class.
  - numberInstances
```
public int numberInstances(int clas)
```
    It returns the number of instances in the data set for a given class.
    
    Parameters:
    
    clas - int Given class.
    
    Returns:
    
    int Number of instances for the given class.
  - computeIR
```
public void computeIR()
```
    Computes the Imbalanced Rate for each class.
  - getIR
```
public double getIR(int clase)
```
    Returns the Imbalanced Rate for the class given.
    
    Parameters:
    
    clase - class id.
    
    Returns:
    
    the Imbalanced Rate for the class given.
  - numberValues
```
public int numberValues(int attribute)
```
    Function to get the number of different feasible values for a given attribute
    
    Parameters:
    
    attribute - int Given attribute
    
    Returns:
    
    int Number of different feasible values for a given attribute
  - getOutputValue
```
public java.lang.String getOutputValue(int intValue)
```
    It returns the output value (string) which matchs with a given integer value
    
    Parameters:
    
    intValue - int Given value
    
    Returns:
    
    String Output value in an understanding way
  - getTipo
```
public int getTipo(int variable)
```
    It returns the type of an attribute
    
    Parameters:
    
    variable - Given attribute
    
    Returns:
    
    int Type of the attribute, it is an integer which corresponds to an enummerate field
  - 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.
  - nombreVar
```
public java.lang.String nombreVar(int pos)
```
    It returns the attribute name of a given variable
    
    Parameters:
    
    pos - variable id.
    
    Returns:
    
    attribute name of a given variable
  - nombreClase
```
public java.lang.String nombreClase(int clase)
```
    Returns the nominal value for a class represented by the integer given.
    
    Parameters:
    
    clase - integer representation of the class.
    
    Returns:
    
    String nominal value for the class
  - discretize
```
public void discretize(int intervalos)
```
    Uniform width discretization
    
    Parameters:
    
    intervalos - int Number of intervals
  - nombres
```
public java.lang.String[] nombres()
```
    It returns the names for all input variables
    
    Returns:
    
    names for all input variables
  - valorReal
```
public static double valorReal(int atributo,
                               java.lang.String valorNominal)
```
    Returns a real representation of a attribute's nominal value given as argument.
    
    Parameters:
    
    atributo - Attribute given.
    
    valorNominal - Nominal value of the attribute given.
    
    Returns:
    
    Returns a real representation of a attribute's nominal value.
  - claseNumerica
```
public int claseNumerica(java.lang.String valorNominal)
```
    Returns a numeric representation of a class nominal value given as argument.
    
    Parameters:
    
    valorNominal - class nominal value.
    
    Returns:
    
    Numeric representation of a class nominal value.
  - valorNominal
```
public static java.lang.String valorNominal(int atributo,
                                            double valorReal)
```
    Returns a nominal representation of a attribute's real value given as argument.
    
    Parameters:
    
    atributo - Attribute given.
    
    valorReal - Real value of the attribute given.
    
    Returns:
    
    Returns a nominal representation of a attribute's real value.
  - totalNominales
```
public int totalNominales(int atributo)
```
    It returns the number of nominal values for a given variable
    
    Parameters:
    
    atributo - variable id
    
    Returns:
    
    the number of nominal values for a given variable
  - claseMasFrecuente
```
public java.lang.String claseMasFrecuente()
```
    It returns the most frequent class in the dataset
    
    Returns:
    
    the most frequent class in the dataset
  - numEjemplos
```
public int numEjemplos(int clase)
```
    It returns the number of instances in the data set for a given class.
    
    Parameters:
    
    clase - int Given class.
    
    Returns:
    
    int Number of instances for the given class.
  - vacio
```
public boolean vacio()
```
    Checks if there is a class without instances.
    
    Returns:
    
    true if there is a class without instances, false otherwise.
  - printDataSet
```
public java.lang.String printDataSet()
```
    Returns a string representation of the dataset.
    
    Returns:
    
    a string representation of the dataset.
  - asList
```
public java.util.List<java.lang.Integer> asList(int[] is)
```
    Returns a List with the elements of the array given.
    
    Parameters:
    
    is - array given.
    
    Returns:
    
    a List with the elements of the array given.
  - getIS
```
public InstanceSet getIS()
```
    Returns the Instance set stored.
    
    Returns:
    
    the Instance set stored.

Class myDataset

Field Summary

Constructor Summary

Method Summary

Methods inherited from class java.lang.Object

Field Detail

REAL

INTEGER

NOMINAL

Constructor Detail

myDataset

myDataset

myDataset

myDataset

myDataset

myDataset

myDataset

Method Detail

randomUnderSampling

randomSampling

randomSampling

importanceSampling

getX

getExample

getOutputAsInteger

getOutputAsReal

getOutputAsString

getOutputAsString

getOutputAsInteger

getOutputAsReal

getemax

getemin

getMax

getMin

getnData

getnVars

getnInputs

getnClasses

isMissing

readClassificationSet

readInstanceSet

copyHeader

normalize

computeStatisticsPerClass

getAveragePerClass

getStdPerClass

hasRealAttributes

hasNumericalAttributes

hasMissingAttributes

sizeWithoutMissing

size

stdDev

average

computeInstancesPerClass

numberInstances

computeIR

getIR

numberValues

getOutputValue

getTipo

devuelveRangos

nombreVar

nombreClase

discretize

nombres

valorReal

claseNumerica

valorNominal

totalNominales

claseMasFrecuente

numEjemplos

vacio

printDataSet

asList

getIS