gboolean
_ncm_fit_gsl_ls_run (NcmFit *fit, NcmFitRunMsgs mtype)
{
NcmFitGSLLS *fit_gsl_ls = NCM_FIT_GSL_LS (fit);
gint status, info = 0;
if (ncm_fit_has_equality_constraints (fit) || ncm_fit_has_inequality_constraints (fit))
g_error ("_ncm_fit_gsl_ls_run: GSL algorithms do not support constraints.");
g_assert (fit->fstate->fparam_len != 0);
ncm_mset_fparams_get_vector (fit->mset, fit->fstate->fparams);
gsl_multifit_fdfsolver_set (fit_gsl_ls->ls, &fit_gsl_ls->f, ncm_vector_gsl (fit->fstate->fparams));
status = gsl_multifit_fdfsolver_driver (fit_gsl_ls->ls,
fit->maxiter,
ncm_fit_get_params_reltol (fit),
ncm_fit_get_m2lnL_reltol (fit),
ncm_fit_get_m2lnL_reltol (fit),
&info
);
{
NcmVector *_x = ncm_vector_new_gsl_static (fit_gsl_ls->ls->x);
NcmVector *_f = ncm_vector_new_gsl_static (fit_gsl_ls->ls->f);
NcmMatrix *_J = ncm_matrix_new (fit_gsl_ls->f.p, fit_gsl_ls->f.p);
gsl_multifit_fdfsolver_jac (fit_gsl_ls->ls, ncm_matrix_gsl (_J));
ncm_fit_params_set_vector (fit, _x);
ncm_fit_state_set_params_prec (fit->fstate, ncm_fit_get_params_reltol (fit));
ncm_fit_state_set_ls (fit->fstate, _f, _J);
ncm_fit_state_set_niter (fit->fstate, gsl_multifit_fdfsolver_niter (fit_gsl_ls->ls));
ncm_vector_free (_x);
ncm_vector_free (_f);
ncm_matrix_free (_J);
}
if (status == GSL_SUCCESS)
return TRUE;
else
return FALSE;
}
int
main (void)
{
const gsl_multifit_fdfsolver_type *T = gsl_multifit_fdfsolver_lmsder;
gsl_multifit_fdfsolver *s;
int status, info;
size_t i;
const size_t n = N;
const size_t p = 3;
gsl_matrix *J = gsl_matrix_alloc(n, p);
gsl_matrix *covar = gsl_matrix_alloc (p, p);
double y[N], weights[N];
struct data d = { n, y };
gsl_multifit_function_fdf f;
double x_init[3] = { 1.0, 0.0, 0.0 };
gsl_vector_view x = gsl_vector_view_array (x_init, p);
gsl_vector_view w = gsl_vector_view_array(weights, n);
const gsl_rng_type * type;
gsl_rng * r;
gsl_vector *res_f;
double chi, chi0;
const double xtol = 1e-8;
const double gtol = 1e-8;
const double ftol = 0.0;
gsl_rng_env_setup();
type = gsl_rng_default;
r = gsl_rng_alloc (type);
f.f = &expb_f;
f.df = &expb_df; /* set to NULL for finite-difference Jacobian */
f.n = n;
f.p = p;
f.params = &d;
/* This is the data to be fitted */
for (i = 0; i < n; i++)
{
double t = i;
double yi = 1.0 + 5 * exp (-0.1 * t);
double si = 0.1 * yi;
double dy = gsl_ran_gaussian(r, si);
weights[i] = 1.0 / (si * si);
y[i] = yi + dy;
printf ("data: %zu %g %g\n", i, y[i], si);
};
s = gsl_multifit_fdfsolver_alloc (T, n, p);
/* initialize solver with starting point and weights */
gsl_multifit_fdfsolver_wset (s, &f, &x.vector, &w.vector);
/* compute initial residual norm */
res_f = gsl_multifit_fdfsolver_residual(s);
chi0 = gsl_blas_dnrm2(res_f);
/* solve the system with a maximum of 20 iterations */
status = gsl_multifit_fdfsolver_driver(s, 20, xtol, gtol, ftol, &info);
gsl_multifit_fdfsolver_jac(s, J);
gsl_multifit_covar (J, 0.0, covar);
/* compute final residual norm */
chi = gsl_blas_dnrm2(res_f);
#define FIT(i) gsl_vector_get(s->x, i)
#define ERR(i) sqrt(gsl_matrix_get(covar,i,i))
fprintf(stderr, "summary from method '%s'\n",
gsl_multifit_fdfsolver_name(s));
fprintf(stderr, "number of iterations: %zu\n",
gsl_multifit_fdfsolver_niter(s));
fprintf(stderr, "function evaluations: %zu\n", f.nevalf);
fprintf(stderr, "Jacobian evaluations: %zu\n", f.nevaldf);
fprintf(stderr, "reason for stopping: %s\n",
(info == 1) ? "small step size" : "small gradient");
fprintf(stderr, "initial |f(x)| = %g\n", chi0);
fprintf(stderr, "final |f(x)| = %g\n", chi);
{
double dof = n - p;
double c = GSL_MAX_DBL(1, chi / sqrt(dof));
fprintf(stderr, "chisq/dof = %g\n", pow(chi, 2.0) / dof);
fprintf (stderr, "A = %.5f +/- %.5f\n", FIT(0), c*ERR(0));
fprintf (stderr, "lambda = %.5f +/- %.5f\n", FIT(1), c*ERR(1));
fprintf (stderr, "b = %.5f +/- %.5f\n", FIT(2), c*ERR(2));
}
fprintf (stderr, "status = %s\n", gsl_strerror (status));
gsl_multifit_fdfsolver_free (s);
gsl_matrix_free (covar);
gsl_matrix_free (J);
gsl_rng_free (r);
return 0;
}