/***********************************************************************
This file is part of KEEL-software, the Data Mining tool for regression,
classification, clustering, pattern mining and so on.
Copyright (C) 2004-2010
F. Herrera (herrera@decsai.ugr.es)
L. S�nchez (luciano@uniovi.es)
J. Alcal�-Fdez (jalcala@decsai.ugr.es)
S. Garc�a (sglopez@ujaen.es)
A. Fern�ndez (alberto.fernandez@ujaen.es)
J. Luengo (julianlm@decsai.ugr.es)
This program is free software: you can redistribute it and/or modify
it under the terms of the GNU General Public License as published by
the Free Software Foundation, either version 3 of the License, or
(at your option) any later version.
This program is distributed in the hope that it will be useful,
but WITHOUT ANY WARRANTY; without even the implied warranty of
MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
GNU General Public License for more details.
You should have received a copy of the GNU General Public License
along with this program. If not, see http://www.gnu.org/licenses/
**********************************************************************/
package keel.Algorithms.Rule_Learning.Rules6;
/**
* <p>Title: Dataset</p>
*
* <p>Description: It contains the methods to read a Classification/Regression Dataset</p>
*
*
* <p>Company: KEEL </p>
*
* @author Alberto Fernandez
* @version 1.0
*/
import java.io.IOException;
import keel.Dataset.*;
public class myDataset {
public static final int REAL = 0;
public static final int INTEGER = 1;
public static final int NOMINAL = 2;
private double[][] X = null; //examples array
private boolean[][] missing = null; //possible missing values
private int[] outputInteger = null; //output of the data-set as integer values
private double[] outputReal = null; //output of the data-set as double values
private String[] output = null; //output of the data-set as string values
private double[] emax; //max value of an attribute
private double[] emin; //min value of an attribute
private int nData; // Number of examples
private int nVars; // Numer of variables
private int nInputs; // Number of inputs
private int nClasses; // Number of outputs
private InstanceSet IS; //The whole instance set
private double stdev[], average[]; //standard deviation and average of each attribute
private int instancesCl[];
/**
* Init a new set of instances
*/
public myDataset() {
IS = new InstanceSet();
}
/**
* Outputs an array of examples with their corresponding attribute values.
* @return double[][] an array of examples with their corresponding attribute values
*/
public double[][] getX() {
return X;
}
/**
* Output a specific example
* @param pos int position (id) of the example in the data-set
* @return double[] the attributes of the given example
*/
public double[] getExample(int pos) {
return X[pos];
}
/**
* Returns the output of the data-set as integer values
* @return int[] an array of integer values corresponding to the output values of the dataset
*/
public int[] getOutputAsInteger() {
int[] output = new int[outputInteger.length];
for (int i = 0; i < outputInteger.length; i++) {
output[i] = outputInteger[i];
}
return output;
}
/**
* Returns the output of the data-set as real values
* @return double[] an array of real values corresponding to the output values of the dataset
*/
public double[] getOutputAsReal() {
double[] output = new double[outputReal.length];
for (int i = 0; i < outputReal.length; i++) {
output[i] = outputInteger[i];
}
return output;
}
/**
* Returns the output of the data-set as nominal values
* @return String[] an array of nomianl values corresponding to the output values of the dataset
*/
public String[] getOutputAsString() {
String[] output = new String[this.output.length];
for (int i = 0; i < this.output.length; i++) {
output[i] = this.output[i];
}
return output;
}
/**
* It returns the output value of the example "pos"
* @param pos int the position (id) of the example
* @return String a string containing the output value
*/
public String getOutputAsString(int pos) {
return output[pos];
}
/**
* It returns the output value of the example "pos"
* @param pos int the position (id) of the example
* @return int an integer containing the output value
*/
public int getOutputAsInteger(int pos) {
return outputInteger[pos];
}
/**
* It returns the output value of the example "pos"
* @param pos int the position (id) of the example
* @return double a real containing the output value
*/
public double getOutputAsReal(int pos) {
return outputReal[pos];
}
/**
* It returns an array with the maximum values of the attributes
* @return double[] an array with the maximum values of the attributes
*/
public double[] getemax() {
return emax;
}
/**
* It returns an array with the minimum values of the attributes
* @return double[] an array with the minimum values of the attributes
*/
public double[] getemin() {
return emin;
}
public double getMax(int variable) {
return emax[variable];
}
public double getMin(int variable) {
return emin[variable];
}
/**
* It gets the size of the data-set
* @return int the number of examples in the data-set
*/
public int getnData() {
return nData;
}
/**
* It gets the number of variables of the data-set (including the output)
* @return int the number of variables of the data-set (including the output)
*/
public int getnVars() {
return nVars;
}
/**
* It gets the number of input attributes of the data-set
* @return int the number of input attributes of the data-set
*/
public int getnInputs() {
return nInputs;
}
/**
* It gets the number of output attributes of the data-set (for example number of classes in classification)
* @return int the number of different output values of the data-set
*/
public int getnClasses() {
return nClasses;
}
/**
* This function checks if the attribute value is missing
* @param i int Example id
* @param j int Variable id
* @return boolean True is the value is missing, else it returns false
*/
public boolean isMissing(int i, int j) {
return missing[i][j];
}
/**
* It reads the whole input data-set and it stores each example and its associated output value in
* local arrays to ease their use.
* @param datasetFile String name of the file containing the dataset
* @param train boolean It must have the value "true" if we are reading the training data-set
* @throws IOException If there ocurs any problem with the reading of the data-set
*/
public void readClassificationSet(String datasetFile, boolean train) throws
IOException {
try {
// Load in memory a dataset that contains a classification problem
IS.readSet(datasetFile, train);
nData = IS.getNumInstances();
nInputs = Attributes.getInputNumAttributes();
nVars = nInputs + Attributes.getOutputNumAttributes();
// outputIntegerheck that there is only one output variable
if (Attributes.getOutputNumAttributes() > 1) {
System.out.println(
"This algorithm can not process MIMO datasets");
System.out.println(
"All outputs but the first one will be removed");
System.exit(1);
}
boolean noOutputs = false;
if (Attributes.getOutputNumAttributes() < 1) {
System.out.println(
"This algorithm can not process datasets without outputs");
System.out.println("Zero-valued output generated");
noOutputs = true;
System.exit(1);
}
// Initialice and fill our own tables
X = new double[nData][nInputs];
missing = new boolean[nData][nInputs];
outputInteger = new int[nData];
outputReal = new double[nData];
output = new String[nData];
// Maximum and minimum of inputs
emax = new double[nInputs];
emin = new double[nInputs];
// All values are casted into double/integer
nClasses = 0;
for (int i = 0; i < nData; i++) {
Instance inst = IS.getInstance(i);
for (int j = 0; j < nInputs; j++) {
X[i][j] = IS.getInputNumericValue(i, j); //inst.getInputRealValues(j);
missing[i][j] = inst.getInputMissingValues(j);
if (X[i][j] > emax[j] || i == 0) {
emax[j] = X[i][j];
}
if (X[i][j] < emin[j] || i == 0) {
emin[j] = X[i][j];
}
}
if (noOutputs) {
outputInteger[i] = 0;
output[i] = "";
} else {
outputInteger[i] = (int) IS.getOutputNumericValue(i, 0);
output[i] = IS.getOutputNominalValue(i, 0);
}
if (outputInteger[i] > nClasses) {
nClasses = outputInteger[i];
}
}
nClasses++;
System.out.println("Number of classes=" + nClasses);
} catch (Exception e) {
System.out.println("DBG: Exception in readSet");
e.printStackTrace();
}
// computeStatistics();
this.computeInstancesPerClass();
}
/**
* It reads the whole input data-set and it stores each example and its associated output value in
* local arrays to ease their use.
* @param datasetFile String name of the file containing the dataset
* @param train boolean It must have the value "true" if we are reading the training data-set
* @throws IOException If there ocurs any problem with the reading of the data-set
*/
public void readRegressionSet(String datasetFile, boolean train) throws
IOException {
try {
// Load in memory a dataset that contains a regression problem
IS.readSet(datasetFile, train);
nData = IS.getNumInstances();
nInputs = Attributes.getInputNumAttributes();
nVars = nInputs + Attributes.getOutputNumAttributes();
// outputIntegerheck that there is only one output variable
if (Attributes.getOutputNumAttributes() > 1) {
System.out.println(
"This algorithm can not process MIMO datasets");
System.out.println(
"All outputs but the first one will be removed");
System.exit(1);
}
boolean noOutputs = false;
if (Attributes.getOutputNumAttributes() < 1) {
System.out.println(
"This algorithm can not process datasets without outputs");
System.out.println("Zero-valued output generated");
noOutputs = true;
System.exit(1);
}
// Initialice and fill our own tables
X = new double[nData][nInputs];
missing = new boolean[nData][nInputs];
outputInteger = new int[nData];
// Maximum and minimum of inputs
emax = new double[nInputs];
emin = new double[nInputs];
// All values are casted into double/integer
nClasses = 0;
for (int i = 0; i < nData; i++) {
Instance inst = IS.getInstance(i);
for (int j = 0; j < nInputs; j++) {
X[i][j] = IS.getInputNumericValue(i, j);
missing[i][j] = inst.getInputMissingValues(j);
if (X[i][j] > emax[j] || i == 0) {
emax[j] = X[i][j];
}
if (X[i][j] < emin[j] || i == 0) {
emin[j] = X[i][j];
}
}
if (noOutputs) {
outputReal[i] = outputInteger[i] = 0;
} else {
outputReal[i] = IS.getOutputNumericValue(i, 0);
outputInteger[i] = (int) outputReal[i];
}
}
} catch (Exception e) {
System.out.println("DBG: Exception in readSet");
e.printStackTrace();
}
computeStatistics();
}
/**
* It copies the header of the dataset
* @return String A string containing all the data-set information
*/
public String copyHeader() {
String p = new String("");
p = "@relation " + Attributes.getRelationName() + "\n";
p += Attributes.getInputAttributesHeader();
p += Attributes.getOutputAttributesHeader();
p += Attributes.getInputHeader() + "\n";
p += Attributes.getOutputHeader() + "\n";
p += "@data\n";
return p;
}
/**
* It transform the input space into the [0,1] range
*/
public void normalize() {
int atts = this.getnInputs();
double maxs[] = new double[atts];
for (int j = 0; j < atts; j++) {
maxs[j] = 1.0 / (emax[j] - emin[j]);
}
for (int i = 0; i < this.getnData(); i++) {
for (int j = 0; j < atts; j++) {
if (isMissing(i, j)) {
; //this process ignores missing values
} else {
X[i][j] = (X[i][j] - emin[j]) * maxs[j];
}
}
}
}
/**
* It checks if the data-set has any real value
* @return boolean True if it has some real values, else false.
*/
public boolean hasRealAttributes() {
return Attributes.hasRealAttributes();
}
public boolean hasNumericalAttributes() {
return (Attributes.hasIntegerAttributes() ||
Attributes.hasRealAttributes());
}
/**
* It checks if the data-set has any missing value
* @return boolean True if it has some missing values, else false.
*/
public boolean hasMissingAttributes() {
return (this.sizeWithoutMissing() < this.getnData());
}
/**
* It return the size of the data-set without having account the missing values
* @return int the size of the data-set without having account the missing values
*/
public int sizeWithoutMissing() {
int tam = 0;
for (int i = 0; i < nData; i++) {
int j;
for (j = 1; (j < nInputs) && (!isMissing(i, j)); j++) {
;
}
if (j == nInputs) {
tam++;
}
}
return tam;
}
public int size() {
return nData;
}
/**
* It computes the average and standard deviation of the input attributes
*/
private void computeStatistics() {
stdev = new double[this.getnVars()];
average = new double[this.getnVars()];
for (int i = 0; i < this.getnInputs(); i++) {
average[i] = 0;
for (int j = 0; j < X[i].length; j++) {
average[i] += X[i][j];
}
average[i] /= X[i].length;
}
average[average.length-1] = 0;
for (int j = 0; j < outputReal.length; j++) {
average[average.length-1] += outputReal[j];
}
average[average.length-1] /= outputReal.length;
for (int i = 0; i < this.getnInputs(); i++) {
double sum = 0;
for (int j = 0; j < X[i].length; j++) {
sum += (X[i][j] - average[i]) * (X[i][j] - average[i]);
}
sum /= X[i].length;
stdev[i] = Math.sqrt(sum);
}
double sum = 0;
for (int j = 0; j < outputReal.length; j++) {
sum += (outputReal[j] - average[average.length-1]) * (outputReal[j] - average[average.length-1]);
}
sum /= outputReal.length;
stdev[stdev.length-1] = Math.sqrt(sum);
}
/**
* It return the standard deviation of an specific attribute
* @param position int attribute id (position of the attribute)
* @return double the standard deviation of the attribute
*/
public double stdDev(int position) {
return stdev[position];
}
/**
* It return the average of an specific attribute
* @param position int attribute id (position of the attribute)
* @return double the average of the attribute
*/
public double average(int position) {
return average[position];
}
public void computeInstancesPerClass() {
instancesCl = new int[nClasses];
for (int i = 0; i < this.getnData(); i++) {
instancesCl[this.outputInteger[i]]++;
}
}
public int numberInstances(int clas) {
return instancesCl[clas];
}
public int numberValues(int attribute) {
return Attributes.getInputAttribute(attribute).getNumNominalValues();
}
public String getOutputValue(int intValue) {
return Attributes.getOutputAttribute(0).getNominalValue(intValue);
}
public int getTipo(int variable) {
if (Attributes.getAttribute(variable).getType() ==
Attributes.getAttribute(0).INTEGER) {
return this.INTEGER;
}
if (Attributes.getAttribute(variable).getType() ==
Attributes.getAttribute(0).REAL) {
return this.REAL;
}
if (Attributes.getAttribute(variable).getType() ==
Attributes.getAttribute(0).NOMINAL) {
return this.NOMINAL;
}
return 0;
}
/**
* Devuelve el universo de discuros de las variables de entrada y salida
* @return double[][] El rango minimo y maximo de cada variable
*/
public double [][] devuelveRangos(){
double [][] rangos = new double[this.getnVars()][2];
for (int i = 0; i < this.getnInputs(); i++){
if (Attributes.getInputAttribute(i).getNumNominalValues() > 0){
rangos[i][0] = 0;
rangos[i][1] = Attributes.getInputAttribute(i).getNumNominalValues()-1;
}else{
rangos[i][0] = Attributes.getInputAttribute(i).getMinAttribute();
rangos[i][1] = Attributes.getInputAttribute(i).getMaxAttribute();
}
}
rangos[this.getnVars()-1][0] = Attributes.getOutputAttribute(0).getMinAttribute();
rangos[this.getnVars()-1][1] = Attributes.getOutputAttribute(0).getMaxAttribute();
return rangos;
}
}