/***********************************************************************
This file is part of JavaNPST, a Java library of NonParametric
Statistical Tests.
Copyright (C) 2011
J. Derrac (jderrac@decsai.ugr.es)
S. Garcia (sglopez@ujaen.es)
F. Herrera (herrera@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 javanpst.distributions.tests;
import java.io.IOException;
import javax.xml.parsers.ParserConfigurationException;
import javax.xml.parsers.SAXParser;
import javax.xml.parsers.SAXParserFactory;
import org.xml.sax.SAXException;
import javanpst.distributions.DistributionDefinitions;
import javanpst.data.distributions.criticalTable.Critical1KeyTable;
import javanpst.data.readers.distributionReaders.LillieforsNormalReaderFormat;
import javanpst.data.readers.distributionReaders.LillieforsExponentialReaderFormat;
/**
* An implementation of the Lilliefors distribution.
*
* Uses a Singleton pattern to ensure only an instance of the class is allowed.
*
* @author Joaqu�n Derrac Rus (jderrac@decsai.ugr.es)
* @version 1.0
*/
public class LillieforsDistribution extends TestDistribution{
/**
* Table of the distribution for Normal adjustment
*/
private static Critical1KeyTable tableNormal;
/**
* Table of the distribution for Exponential adjustment
*/
private static Critical1KeyTable tableExponential;
/**
* Flag showing if the last estimation was approximate
*/
private static boolean aproximated;
/**
* Private default builder.
*
* Initializes distribution tables
*/
private LillieforsDistribution(){
initialize();
}//end-method
/**
* Singleton pattern access to the distribution
*
* @return the instance of the distribution
*/
public static LillieforsDistribution getInstance(){
return SingletonHolder.distribution;
}//end-method
/**
* Singleton holder for the only instance of the distribution
*
* SingletonHolder is loaded on the first execution of getInstance()
* or the first access to SingletonHolder.distribution, not before.
*/
private static class SingletonHolder {
public static final LillieforsDistribution distribution = new LillieforsDistribution();
}//end-method
/**
* Generate distribution tables
*/
protected void generateTables(){
double [] tags={0.100,0.050,0.010,0.001};
tableNormal=new Critical1KeyTable(101,tags);
tableExponential=new Critical1KeyTable(101,tags);
loadFiles();
}//end-method
/**
* Load data files to fill distribution tables
*/
protected void loadFiles(){
try{
SAXParserFactory spf=SAXParserFactory.newInstance();
SAXParser sp = spf.newSAXParser();
sp.parse(this.getClass().getResourceAsStream("/tables/Lilliefors/LillieforsNormal.xml"), new LillieforsNormalReaderFormat());
}catch(ParserConfigurationException e){
System.err.println("Parser error");
}catch(SAXException e2){
System.err.println("SAX error: " + e2.getStackTrace());
} catch (IOException e3) {
System.err.println("Input/Output error: " + e3.getMessage() );
}
try{
SAXParserFactory spf=SAXParserFactory.newInstance();
SAXParser sp = spf.newSAXParser();
sp.parse(this.getClass().getResourceAsStream("/tables/Lilliefors/LillieforsExponential.xml"), new LillieforsExponentialReaderFormat());
}catch(ParserConfigurationException e){
System.err.println("Parser error");
}catch(SAXException e2){
System.err.println("SAX error: " + e2.getStackTrace());
} catch (IOException e3) {
System.err.println("Input/Output error: " + e3.getMessage() );
}
}//end-method
/**
* Gets the table of the distribution (normal)
*
* @return Reference to the table
*/
public static Critical1KeyTable getNormalTable(){
return tableNormal;
}//end-method
/**
* Gets the table of the distribution (exponential)
*
* @return Reference to the table
*/
public static Critical1KeyTable getExponentialTable(){
return tableExponential;
}//end-method
/**
* Computes p-value of the Lilliefors distribution (adjust to a normal distribution).
*
* @param n size of the population
* @param Dn Lilliefors statistic
* @return p-value computed
*/
public double computeProbabilityNormal(int n,double Dn){
double asymptoticValuesNormal []={0.816,0.888,1.038,1.212};
if(n>3){
if(tableNormal.get(n, 0)==DistributionDefinitions.UNDEFINED){
aproximated=false;
for(int i=3;i>-1;i--){
if(Dn>=tableNormal.get(n, i)){
return tableNormal.getHeader(i);
}
}
}
else{
aproximated=true;
double size=Math.sqrt(n);
//adjust to a normal distribution
for(int i=3;i>-1;i--){
if(Dn>=(asymptoticValuesNormal[i]/size)){
return tableNormal.getHeader(i);
}
}
}
}
else{
aproximated=true;
return 1.0;
}
return DistributionDefinitions.ALL;
}//end-method
/**
* Computes p-value of the Lilliefors distribution (adjust to a normal distribution).
*
* @param n size of the population
* @param Dn Lilliefors statistic
* @return p-value computed
*/
public double computeProbabilityExponential(int n,double Dn){
double asymptoticValuesExponential []={0.980,1.077,1.274,1.501};
if(n>3){
if(tableExponential.get(n, 0)==DistributionDefinitions.UNDEFINED){
aproximated=false;
for(int i=3;i>-1;i--){
if(Dn>=tableExponential.get(n, i)){
return tableExponential.getHeader(i);
}
}
}
else{
aproximated=true;
double size=Math.sqrt(n);
for(int i=3;i>-1;i--){
if(Dn>=(asymptoticValuesExponential[i]/size)){
return tableExponential.getHeader(i);
}
}
}
}
else{
aproximated=true;
return 1.0;
}
return DistributionDefinitions.ALL;
}//end-method
/**
* Tests if the last estimation was approximate
*
* @return true if the last estimation was approximate. False, otherwise
*/
public boolean isApproximate(){
return aproximated;
}//end-method
}//end-class
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