/* * Project:  LevenbergMarquardtLeastSquaresFitting * * File:     lm_eval.c * * Contents: Default implementation of  *           - user-defined data evalution function, *           - user-defined printout routine. *           Declarations are in lm_eval.h. * * Usage:    Least-squares fitting of a simple one-dimensional data set *           is shown in lm_test.c. For other applications, the data  *           must be modified. * * Author:   Joachim Wuttke 2004-8  *  * Homepage: www.messen-und-deuten.de/lmfit * * Licence:  Public domain. */ #include "lmmin.h"#include "lm_eval.h"#include <stdio.h>void lm_evaluate_default(double *par, int m_dat, double *fvec,			 void *data, int *info)/*  *	par is an input array. At the end of the minimization, it contains *        the approximate solution vector. * *	m_dat is a positive integer input variable set to the number *	  of functions. * *	fvec is an output array of length m_dat which contains the function *        values the square sum of which ought to be minimized. * *	data is a read-only pointer to lm_data_type, as specified by lm_eval.h. * *      info is an integer output variable. If set to a negative value, the *        minimization procedure will stop. */{    int i;    lm_data_type *mydata;    mydata = (lm_data_type *) data;    for (i = 0; i < m_dat; i++)	fvec[i] = mydata->user_y[i]	    - mydata->user_func(mydata->user_t[i], par);    *info = *info;		/* to prevent a 'unused variable' warning */    /* if <parameters drifted away> { *info = -1; } */}void lm_print_default(int n_par, double *par, int m_dat, double *fvec,		      void *data, int iflag, int iter, int nfev)/* *       data  : for soft control of printout behaviour, add control *                 variables to the data struct *       iflag : 0 (init) 1 (outer loop) 2(inner loop) -1(terminated) *       iter  : outer loop counter *       nfev  : number of calls to *evaluate */{    double f, y, t;    int i;    lm_data_type *mydata;    mydata = (lm_data_type *) data;    if (iflag == 2) {	printf("trying step in gradient direction\n");    } else if (iflag == 1) {	printf("determining gradient (iteration %d)\n", iter);    } else if (iflag == 0) {	printf("starting minimization\n");    } else if (iflag == -1) {	printf("terminated after %d evaluations\n", nfev);    }    printf("  par: ");    for (i = 0; i < n_par; ++i)	printf(" %12g", par[i]);    printf(" => norm: %12g\n", lm_enorm(m_dat, fvec));    if (iflag == -1) {	printf("  fitting data as follows:\n");	for (i = 0; i < m_dat; ++i) {	    t = (mydata->user_t)[i];	    y = (mydata->user_y)[i];	    f = mydata->user_func(t, par);	    printf("    t[%2d]=%12g y=%12g fit=%12g residue=%12g\n",		   i, t, y, f, y - f);	}    }}