keel.Algorithms.Neural_Networks.RBFN_decremental

Class ProcDataset

java.lang.Object

keel.Algorithms.Neural_Networks.RBFN_decremental.ProcDataset

public class ProcDataset
extends java.lang.Object

Process the KEEL dataset. Wrapper for KEEL's Dataset class

Since:

JDK1.5

Version:

1.0

Author:

Writen by Luciando Sánchez (University of Oviedo), Modified by Antonio J. Rivera Rivas (University of Jaén), Modified by María Dolores Pérez Godoy (University of Jaén) 17/12/2008

Constructor Summary

Constructors

Constructor and Description
ProcDataset(java.lang.String nfexamples, boolean train) Init a new set of instances

Method Summary

Modifier and Type	Method and Description
int	datasetType() Returs the type of the dataset 0-Modelling, 1-Clasiffication, 2-Clustering
void	generateResultsClasification(java.lang.String Foutput, int[] real, int[] obtained) Generates output file for a clasification problem
void	generateResultsModeling(java.lang.String Foutput, double[] real, double[] obtained) Generates output file for a modelling problem
void	generateResultsModeling(java.lang.String Foutput, int[] real, int[] obtained) Generates output file for a modelling problem
int[]	getC() Returns the outputs of each example (classification).
double[]	getimax() Returns the maximum values of each input attribute.
double[]	getimin() Returns the minimum values of each input attribute.
int	getnclasses() Returns the number of classes.
int	getndata() Returns the number of examples.
int	getninputs() Returns the number of input attributes.
int	getnvariables() Returns the number of variables.
double	getomax() Returns the maximum value of the output.
double	getomin() Returns the minimun value of the output.
double[][]	getX() Returns the whole input data.
double[]	getY() Returns the outputs of each example (regression).
boolean	isMissing(int i, int j) Returns True if the value of j-th attribute of the i-th instance is missing and false, otherwise.
void	processClassifierDataset() Process a dataset for classification
void	processClusterDataset(java.lang.String nfexamples, boolean train) Process a Dataset for clustering
void	processModelDataset() Process a dataset for modelling

Methods inherited from class java.lang.Object

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

Constructor Detail

ProcDataset

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

Init a new set of instances

Parameters:

nfexamples - Name of the dataset file

train - The dataset file is for training or for test

Throws:

java.io.IOException - if there is any semantical, lexical or sintactical error in the input file.

Method Detail

getX

public double[][] getX()

Returns the whole input data.

Returns:

the whole input data.

getY

public double[] getY()

Returns the outputs of each example (regression).

Returns:

the outputs of each example (regression).

getC

public int[] getC()

Returns the outputs of each example (classification).

Returns:

the outputs of each example (classification).

getimax

public double[] getimax()

Returns the maximum values of each input attribute.

Returns:

the maximum values of each input attribute.

getimin

public double[] getimin()

Returns the minimum values of each input attribute.

Returns:

the minimum values of each input attribute.

getomax

public double getomax()

Returns the maximum value of the output.

Returns:

the maximum value of the output.

getomin

public double getomin()

Returns the minimun value of the output.

Returns:

the minimun value of the output.

getndata

public int getndata()

Returns the number of examples.

Returns:

the number of examples.

getnvariables

public int getnvariables()

Returns the number of variables.

Returns:

the number of variables.

getninputs

public int getninputs()

Returns the number of input attributes.

Returns:

the number of input attributes.

getnclasses

public int getnclasses()

Returns the number of classes.

Returns:

the number of classes.

isMissing

public boolean isMissing(int i,
                         int j)

Returns True if the value of j-th attribute of the i-th instance is missing and false, otherwise.

Parameters:

i - instance's position.

j - attribute's position.

Returns:

True if the value of j-th attribute of the i-th instance is missing and false, otherwise.

datasetType

public int datasetType()

Returs the type of the dataset 0-Modelling, 1-Clasiffication, 2-Clustering

Returns:

type of the dataset 0-Modelling, 1-Clasiffication, 2-Clustering

processClassifierDataset

public void processClassifierDataset()
                              throws java.io.IOException

Process a dataset for classification

Throws:

java.io.IOException - if the dataset is not appropriate for this algorithm

processModelDataset

public void processModelDataset()
                         throws java.io.IOException

Process a dataset for modelling

Throws:

java.io.IOException - if the dataset is not appropriate for this algorithm

processClusterDataset

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

Process a Dataset for clustering

Parameters:

nfexamples -

train -

Throws:

java.io.IOException - if the dataset is not appropriate for this algorithm

generateResultsModeling

public void generateResultsModeling(java.lang.String Foutput,
                                    int[] real,
                                    int[] obtained)

Generates output file for a modelling problem

Parameters:

Foutput - Name of the output file

real - Vector of outputs instances

obtained - Vector of net outputs

generateResultsModeling

public void generateResultsModeling(java.lang.String Foutput,
                                    double[] real,
                                    double[] obtained)

Generates output file for a modelling problem

Parameters:

Foutput - Name of the output file

real - Vector of outputs instances

obtained - Vector of net outputs

generateResultsClasification

public void generateResultsClasification(java.lang.String Foutput,
                                         int[] real,
                                         int[] obtained)

Generates output file for a clasification problem

Parameters:

Foutput - Name of the output file

real - Vector of outputs instances

obtained - Vector of net outputs