/*     RegressionExampleFour

       Example of the use of the class Regression demonstrating
       the use of the non-linear regression method, Regression.simplexPlot
       in fitting data to the functions,
        y0 = (a+x)^2 + (b+x)^2   
        y1 = (a+x)^1.2 + (b+x)^1.2  
       with  a and b unknown

       Michael Thomas Flanagan
       http://www.ee.ucl.ac.uk/~mflanaga/java/Regression.html
       May 2008
   
       Copyright (c) 2008 - 2014

*   PERMISSION TO COPY:
*   Permission to use, copy and modify this software and its documentation for 
*   NON-COMMERCIAL purposes is granted, without fee, provided that an acknowledgement 
*   to the author, Dr Michael Thomas Flanagan at www.ee.ucl.ac.uk/~mflanaga, 
*   appears in all copies and associated documentation or publications. 
*   
*   Public listing of the source codes on the internet is not permitted. 
*   
*   Redistribution of the source codes or of the flanagan.jar file is not permitted. 
*   
*   Redistribution in binary form of all or parts of these classes is not permitted. 
*   
*   Dr Michael Thomas Flanagan makes no representations about the suitability 
*   or fitness of the software for any or for a particular purpose. 
*   Dr Michael Thomas Flanagan shall not be liable for any damages suffered 
*   as a result of using, modifying or distributing this software or its derivatives.
*
***************************************************************************************/

import flanagan.analysis.Regression;
import flanagan.analysis.RegressionFunction2;
import flanagan.analysis.Stat;

public class RegressionExampleFour{

    public static void main(String[] args){

        int nData = 10;
        double[] xData = new double[nData];
        double[][] yData = new double[2][nData];

        double aa = 2.0;
        double bb = 5.0;

        // Create example data arrays
        for(int i=0; i<nData; i++){
            xData[i] = i;
            yData[0][i] = Math.pow(i+aa, 2) + Math.pow(i+bb, 2);
            yData[1][i] = Math.pow(i+aa, 1.2) + Math.pow(i+bb, 1.2);
        }

        // Add noise
        double[] y0Ran = Stat.gaussianRand(0, 0.1, nData);
        double[] y1Ran = Stat.gaussianRand(0, 0.1, nData);
        for(int i=0; i<nData; i++){
            yData[0][i] += yData[0][i]*y0Ran[i]/100.0;
            yData[1][i] += yData[1][i]*y1Ran[i]/100.0;
        }

        // Create an instance of the function implenting an interface
        Funct2 functA = new Funct2();

        // Set nData in functA
        functA.setNdata(nData);

        // Create an instance of Regression
        Regression reg = new Regression(xData, yData);

        // make an estimate of the the parameter values and provide step sizes
        double[] start = {1.6, 5.7};
        double[] step = {0.2, 0.5};

        // call regression and plotting method
        reg.simplexPlot(functA, start, step);
    }
}

/*  Regression function  -  returns
        y = (a+x)^2 + (b+x)^2   for point index = 0 to n-1;
        y = (a+x)^1.2 + (b+x)^1.2  for point index = n to 2n-1
*/

class Funct2 implements RegressionFunction2{

    private double nData = -1;

    public double function(double[] param, double[] x, int ii){

        if(ii<this.nData){
             return Math.pow(x[0]+param[0], 2) + Math.pow(x[0]+param[1], 2);
        }
        else{
             return Math.pow(x[0]+param[0], 1.2) + Math.pow(x[0]+param[1], 1.2);
        }
    }

    // method to set the value of nData
    public void setNdata(double nData){
        this.nData = nData;
    }
}