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/* multifit/covar.c
*
* Copyright (C) 1996, 1997, 1998, 1999, 2000, 2004 Brian Gough
*
* 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 2 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, write to the Free Software
* Foundation, Inc., 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301, USA.
*/
#include <config.h>
#include <gsl/gsl_math.h>
#include <gsl/gsl_errno.h>
#include <gsl/gsl_permutation.h>
#include <gsl/gsl_linalg.h>
#include <gsl/gsl_multifit_nlin.h>
/* Compute the covariance matrix
cov = inv (J^T J)
by QRP^T decomposition of J
*/
int
gsl_multifit_covar (const gsl_matrix * J, double epsrel, gsl_matrix * covar)
{
double tolr;
size_t i, j, k;
size_t kmax = 0;
gsl_matrix * r;
gsl_vector * tau;
gsl_vector * norm;
gsl_permutation * perm;
size_t m = J->size1, n = J->size2 ;
if (m < n)
{
GSL_ERROR ("Jacobian be rectangular M x N with M >= N", GSL_EBADLEN);
}
if (covar->size1 != covar->size2 || covar->size1 != n)
{
GSL_ERROR ("covariance matrix must be square and match second dimension of jacobian", GSL_EBADLEN);
}
r = gsl_matrix_alloc (m, n);
tau = gsl_vector_alloc (n);
perm = gsl_permutation_alloc (n) ;
norm = gsl_vector_alloc (n) ;
{
int signum = 0;
gsl_matrix_memcpy (r, J);
gsl_linalg_QRPT_decomp (r, tau, perm, &signum, norm);
}
/* Form the inverse of R in the full upper triangle of R */
tolr = epsrel * fabs(gsl_matrix_get(r, 0, 0));
for (k = 0 ; k < n ; k++)
{
double rkk = gsl_matrix_get(r, k, k);
if (fabs(rkk) <= tolr)
{
break;
}
gsl_matrix_set(r, k, k, 1.0/rkk);
for (j = 0; j < k ; j++)
{
double t = gsl_matrix_get(r, j, k) / rkk;
gsl_matrix_set (r, j, k, 0.0);
for (i = 0; i <= j; i++)
{
double rik = gsl_matrix_get (r, i, k);
double rij = gsl_matrix_get (r, i, j);
gsl_matrix_set (r, i, k, rik - t * rij);
}
}
kmax = k;
}
/* Form the full upper triangle of the inverse of R^T R in the full
upper triangle of R */
for (k = 0; k <= kmax ; k++)
{
for (j = 0; j < k; j++)
{
double rjk = gsl_matrix_get (r, j, k);
for (i = 0; i <= j ; i++)
{
double rij = gsl_matrix_get (r, i, j);
double rik = gsl_matrix_get (r, i, k);
gsl_matrix_set (r, i, j, rij + rjk * rik);
}
}
{
double t = gsl_matrix_get (r, k, k);
for (i = 0; i <= k; i++)
{
double rik = gsl_matrix_get (r, i, k);
gsl_matrix_set (r, i, k, t * rik);
};
}
}
/* Form the full lower triangle of the covariance matrix in the
strict lower triangle of R and in w */
for (j = 0 ; j < n ; j++)
{
size_t pj = gsl_permutation_get (perm, j);
for (i = 0; i <= j; i++)
{
size_t pi = gsl_permutation_get (perm, i);
double rij;
if (j > kmax)
{
gsl_matrix_set (r, i, j, 0.0);
rij = 0.0 ;
}
else
{
rij = gsl_matrix_get (r, i, j);
}
if (pi > pj)
{
gsl_matrix_set (r, pi, pj, rij);
}
else if (pi < pj)
{
gsl_matrix_set (r, pj, pi, rij);
}
}
{
double rjj = gsl_matrix_get (r, j, j);
gsl_matrix_set (covar, pj, pj, rjj);
}
}
/* symmetrize the covariance matrix */
for (j = 0 ; j < n ; j++)
{
for (i = 0; i < j ; i++)
{
double rji = gsl_matrix_get (r, j, i);
gsl_matrix_set (covar, j, i, rji);
gsl_matrix_set (covar, i, j, rji);
}
}
gsl_matrix_free (r);
gsl_permutation_free (perm);
gsl_vector_free (tau);
gsl_vector_free (norm);
return GSL_SUCCESS;
}