/***********************************************************************

	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.tests.bivariate.danielTrendTest;

import java.util.Arrays;

import javanpst.data.structures.dataTable.DataTable;
import javanpst.distributions.tests.SpearmanDistribution;
import javanpst.tests.StatisticalTest;
import javanpst.utils.Pair;

/**
 * The Daniel Trend test.
 * 
 * This test computes Spearman's correlation coefficient to
 * measure association between bivariate samples.
 * 
 * Two samples, with 3 o more values are needed. A exact p-value
 * is computed if the size of the sample is equal or lower than 10. If
 * the size is between 11 and 30, a quantile approximation is computed.
 * Ties are broken arbitrarily.
 * 
 * Furthermore, an asymptotic p-value, measuring positive, negative, and no dependence
 * is computed for any sample size.
 * 
 * @author Joaqu�n Derrac Rus (jderrac@decsai.ugr.es)
 * @version 1.0
 */
public class DanielTrendTest extends StatisticalTest{

	/**
	 * Distribution of the test
	 */
	private SpearmanDistribution distribution;
	
	/**
	 * Data to analyze
	 */
	private DataTable data;
	
	/**
	 * First sample
	 */
	private double sample1 [];
	
	/**
	 * Second sample
	 */
	private double sample2 [];
	
	/**
	 * Ranks of the first sample
	 */
	private double ranks1 [];
	
	/**
	 * Ranks of the second sample
	 */
	private double ranks2 [];
	
	/**
	 * R statistic (Spearman's correlation coefficient)
	 */
	private double R;
	
	/**
	 * Z statistic
	 */
	private double Z;
	
	/**
	 * Number of elements
	 */
	private double n;
	
	/**
	 * Positive dependence p-value
	 */
	private double positiveDependencePvalue;
	
	/**
	 * Negative dependence p-value
	 */
	private double negativeDependencePvalue;
	
	/**
	 * No dependence p-value
	 */
	private double noDependencePvalue;
	
	/**
	 * Exact p-value
	 */
	private double exactPValue;

	/**
	 * Default builder
	 */
	public DanielTrendTest(){
		
		distribution=SpearmanDistribution.getInstance();
		setReportFormat();
		clearData();
		
	}//end-method
	
	/**
	 * Clears the data stored in the test
	 */
	public void clearData(){
		
		data=new DataTable();
		
		performed=false;
		dataReady=false;
		
		R=0.0;
		Z=0.0;
		n=0;
		positiveDependencePvalue=-1.0;
		negativeDependencePvalue=-1.0;
		noDependencePvalue=-1.0;
		exactPValue=-1.0;
		
	}//end-method
	
	/**
	 * Builder. Load data to test.
	 * 
	 * @param newData data to test.
	 */
	public DanielTrendTest(DataTable newData){
		
		distribution=SpearmanDistribution.getInstance();
		setReportFormat();
		
		if(newData.getColumns()!=2){
			System.out.println("This test only can be employed with two samples");
			clearData();
			return;
		}
		
		if(newData.getRows()<3){
			System.out.println("This test need samples of size 3 or more");
			clearData();
			return;
		}
		
		for(int i=0;i0){
				System.out.println("No null values allowed in this test.");
				clearData();
				return;
			}
		}
		
		data=DataTable.newInstance(newData);
		dataReady=true;
		performed=false;
		
		n=data.getRows();
		sample1=new double [(int)n];
		sample2=new double [(int)n];

		for(int i=0;i0){
				System.out.println("No null values allowed in this test.");
				return;
			}
		}
		
		data=DataTable.newInstance(newData);
		dataReady=true;
		performed=false;
		
		n=data.getRows();
		sample1=new double [(int)n];
		sample2=new double [(int)n];

		for(int i=0;i2)&&(n<11)){
			exactPValue=distribution.computeExactProbability((int)n,R);
		}
		else{
			if((n>10)&&(n<31)){
				exactPValue=distribution.computeAproximatedProbability((int)n,R);
			}	
		}
		
		//compute asymptotic p-value
		
		Z=R*Math.sqrt(n-1.0);
		
		positiveDependencePvalue=distribution.computeAsymptoticProbability(Z,true);
		negativeDependencePvalue=distribution.computeAsymptoticProbability(Z,false);
		noDependencePvalue=2.0*Math.min(positiveDependencePvalue, negativeDependencePvalue);
		
		performed=true;
		
	}//end-method

	/**
	 * Compute ranks distribution of both samples
	 */
	private void computeRanks(){
		
		double DELETED = Double.MAX_VALUE;
		
		double copy1 [];
		double copy2 [];
		
		copy1=new double [sample1.length];
		copy2=new double [sample2.length];
		
		System.arraycopy(sample1, 0, copy1, 0, sample1.length);
		System.arraycopy(sample2, 0, copy2, 0, sample2.length);
		
		Arrays.sort(copy1);
		Arrays.sort(copy2);
		
		ranks1=new double [sample1.length];
		ranks2=new double [sample2.length];
		
		double value;
		boolean found;
		
		//assign ranks to sample 1
		for(int i=0;i2)&&(n<11)&&(exactPValue>-1.0)){
			report+="Exact P-Value: "+nf6.format(exactPValue)+"\n";
		}
		else{
			if((n>10)&&(n<31)){
				report+="Exact P-Value: <= "+nf6.format(exactPValue)+"\n";
			}	
		}
		
		report+="\nAsymptotic P-Values"+"\n\n";
		report+="Z statistic: "+nf6.format(Z)+"\n";
		report+="Positive dependence (Right tail): "+nf6.format(positiveDependencePvalue)+"\n";
		report+="Negative dependence (Left Tail): "+nf6.format(negativeDependencePvalue)+"\n";
		report+="No dependence (Double tail): "+nf6.format(noDependencePvalue)+"\n\n";
		
		report+="Ranks computed:\n\n";
		report+="S1 \t S2\n\n";
		
		for(int i=0;i