keel.Algorithms.Preprocess.NoiseFilters.ANR

Class ProcessDataset

java.lang.Object

keel.Algorithms.Preprocess.NoiseFilters.ANR.ProcessDataset

public class ProcessDataset
extends java.lang.Object

Wrapper for KEEL's Dataset class.

Since:

JDK1.4

Version:

1.0

Author:

Written by Luciano Sánchez (University of Oviedo) 15/02/2004, Modified by Enrique A. de la Cal (University of Oviedo) 13/12/2008

Constructor Summary

Constructors

Constructor and Description
ProcessDataset() A constructor that inits a new set of instances

Method Summary

Modifier and Type	Method and Description
int[]	getC() Returns classes for classification problems.
double[]	getImaximum() Returns maximum value for each variable.
double[]	getIminimum() Returns maximum value for each variable.
int	getNclasses() Returns the number of classes for classification problems.
int	getNdata() Returns the size of input data.
int	getNinputs() Returns the number of input variables.
int	getNvariables() Returns the number of input variables plus output variables.
double	getOmaximum() Returns maximum value for output.
double	getOminimum() Returns maximum value for output.
double[][]	getX() Returns input examples.
double[]	getY() Returns input examples.
boolean	isMissing(int i, int j) Returns if an example is missing.
void	oldClassificationProcess(java.lang.String nfejemplos) Process a old format dataset file for a modelling problem.
void	oldClusteringProcess(java.lang.String nfejemplos) Process an old format dataset file for a classification problem.
void	procesa_clustering_old(java.lang.String nfejemplos) Process an old format dataset file for a clustering problem.
void	processClassifierDataset(java.lang.String nfejemplos, boolean train) Process a dataset file for a classification problem.
void	processClusterDataset(java.lang.String nfexamples, boolean train) Process a dataset file for a clustering problem.
void	processModelDataset(java.lang.String nfexamples, boolean train) Process a dataset file for a modelling problem.
void	showDatasetStatistics() prints to standard output statistics about the dataset.

Methods inherited from class java.lang.Object

clone, equals, finalize, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait

Constructor Detail

ProcessDataset

public ProcessDataset()

A constructor that inits a new set of instances

Method Detail

getX

public double[][] getX()

Returns input examples.

Returns:

vector with input examples.

getY

public double[] getY()

Returns input examples.

Returns:

vector with input examples.

getC

public int[] getC()

Returns classes for classification problems.

Returns:

vector with classes.

getImaximum

public double[] getImaximum()

Returns maximum value for each variable.

Returns:

vector maximum value for each variable.

getIminimum

public double[] getIminimum()

Returns maximum value for each variable.

Returns:

vector maximum value for each variable.

getOmaximum

public double getOmaximum()

Returns maximum value for output.

Returns:

maximum value for output.

getOminimum

public double getOminimum()

Returns maximum value for output.

Returns:

maximum value for output.

getNdata

public int getNdata()

Returns the size of input data.

Returns:

the size of input data.

getNvariables

public int getNvariables()

Returns the number of input variables plus output variables.

Returns:

the number of variables.

getNinputs

public int getNinputs()

Returns the number of input variables.

Returns:

the number of input variables.

getNclasses

public int getNclasses()

Returns the number of classes for classification problems.

Returns:

the number of classes.

isMissing

public boolean isMissing(int i,
                         int j)

Returns if an example is missing.

Parameters:

i - example id.

j - attribute id.

Returns:

true is the value is missing (0 in the table); false otherwise;

processClassifierDataset

public void processClassifierDataset(java.lang.String nfejemplos,
                                     boolean train)
                              throws java.io.IOException

Process a dataset file for a classification problem.

Parameters:

nfejemplos - Name of the dataset file

train - The dataset file is for training or for test

Throws:

java.io.IOException - if the file can not be read

processModelDataset

public void processModelDataset(java.lang.String nfexamples,
                                boolean train)
                         throws java.io.IOException

Process a dataset file for a modelling problem.

Parameters:

nfexamples - Name of the dataset file

train - The dataset file is for training or for test

Throws:

java.io.IOException - if the file can not be read

processClusterDataset

public void processClusterDataset(java.lang.String nfexamples,
                                  boolean train)
                           throws java.io.IOException

Process a dataset file for a clustering problem.

Parameters:

nfexamples - Name of the dataset file

train - The dataset file is for training or for test

Throws:

java.io.IOException - if the file can not be read.

oldClassificationProcess

public void oldClassificationProcess(java.lang.String nfejemplos)

Process a old format dataset file for a modelling problem.

Parameters:

nfejemplos - Name of the dataset file

oldClusteringProcess

public void oldClusteringProcess(java.lang.String nfejemplos)

Process an old format dataset file for a classification problem.

Parameters:

nfejemplos - Name of the dataset file.

procesa_clustering_old

public void procesa_clustering_old(java.lang.String nfejemplos)

Process an old format dataset file for a clustering problem.

Parameters:

nfejemplos - Name of the dataset file.

showDatasetStatistics

public void showDatasetStatistics()

prints to standard output statistics about the dataset.