d9r2.txt

解具有导数的多元函数的无约束极小值点或具有导数的非线性方程组的近似解

import java.text.*;
public class d9r2
{
	public static void main (String[] args)
	{
        //program d9r2
        //driver for routine lfit
        d9r2F g = new d9r2F();
		DecimalFormat form = new DecimalFormat("0.000000");
        int i, j, npt = 100;
        double chisq, spread = 0.1;
        int mfit, nterm = 3;
        double x[] = new double[101];
		double y[] = new double[101];
		double sig[] = new double[101];
		double a[] = new double[4];
		double covar[] = new double[10];
		int lista[] = new int[4];
        int idum = -911;
		chisq = 0.0;
        for (i = 1; i <= npt; i++)
		{
            x[i] = 0.1 * i;
            y[i] = nterm;
            for (j = nterm - 1; j >= 1; j--)
			{
                y[i] = j + y[i] * x[i];
            }
            y[i] = y[i] + spread * g.gasdev(idum);
			idum = g.gasdev_idum;
            sig[i] = spread;
        }
        mfit = nterm;
        for (i = 1; i <= mfit; i++)
		{
            lista[i] = i;
        }
        g.lfit(x, y, sig, npt, a, nterm, lista, mfit,covar, nterm, chisq);
		chisq = g.lift_chisq;
        System.out.println("Parameter               Uncertainty");
        for (i = 1; i <= nterm; i++)
		{
            System.out.print("a(" + i + ")= ");
            System.out.print(form.format(a[i]) + "  ");
            System.out.println(form.format(Math.sqrt(covar[(i - 1) * nterm + i])) + "   ");
        }
        System.out.println("Chi-squared =  " + form.format(chisq));
        System.out.println("Full covariance matrix");
        for (i = 1; i <= nterm; i++)
		{
            for (j = 1; j <= nterm; j++)
			{
                System.out.print(form.format(covar[(i - 1) * nterm + j]) + "   ");
            }
			System.out.println();
        }
        //now test the lista feature
        for (i = 1; i <= nterm; i++)
		{
            lista[i] = nterm + 1 - i;
        }
        g.lfit(x, y, sig, npt, a, nterm, lista, mfit, covar, nterm, chisq);
		chisq = g.lift_chisq;
        System.out.println("Parameter               Uncertainty");
        for (i = 1; i <= nterm; i++)
		{
            System.out.print("a(" + i + ")= ");
            System.out.print(form.format(a[i]) + "        ");
            System.out.println(form.format(Math.sqrt(covar[(i - 1) * nterm + i])) + "    ");
        }
        System.out.println("Chi-squared =  " + form.format(chisq));
        System.out.println("Full covariance matrix");
        for (i = 1; i <= nterm; i++)
		{
            for (j = 1; j <= nterm; j++)
			{
                System.out.print(form.format(covar[(i - 1) * nterm + j]) + "   ");
            }
            System.out.println();
        }
        //now check results of restricting fit parameters
        int ii = 1;
		int aaa;
        for (i = 1; i <= nterm; i++)
		{
            aaa = i - (int)(i / 2) * 2;
            if (aaa == 1)
			{
                lista[ii] = i;
                ii = ii + 1;
            }
        }
        mfit = ii - 1;
        g.lfit(x, y, sig, npt, a, nterm, lista, mfit, covar, nterm, chisq);
		chisq = g.lift_chisq;
        System.out.println("Parameter               Uncertainty");
        for (i = 1; i <= nterm; i++)
		{
            System.out.print("a(" + i + ")= ");
            System.out.print(form.format(a[i]) + "        ");
            System.out.println(form.format(Math.sqrt(covar[(i - 1) * nterm + i])) + "   ");
        }
        System.out.println("Chi-squared =  " + form.format(chisq));
        System.out.println("Full covariance matrix");
        for (i = 1; i <= nterm; i++)
		{
            for (j = 1; j <= nterm; j++)
            {
                System.out.print(form.format(covar[(i - 1) * nterm + j]) + "   ");
            }
			System.out.println();
        }
    }
}
public class d9r2F 
{
	void funcs(double x, double afunc[], int ma)
	{
        afunc[1] = 1.0;
        for(int i = 2; i <= ma; i++)
        {
            afunc[i] = x * afunc[i - 1];
		}
    }
}