205 lines
7.2 KiB
Diff
205 lines
7.2 KiB
Diff
From a9d24f91057441bbd2e3ed9e7536b071121526cb Mon Sep 17 00:00:00 2001
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From: "D. V. Wiebe" <dvw@ketiltrout.net>
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Date: Thu, 10 Mar 2016 14:09:26 -0800
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Subject: [PATCH] GSL-2.x support.
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---
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src/plugins/fits/non_linear.h | 67 +++++++++++++++++++++-------------
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src/plugins/fits/non_linear_weighted.h | 66 ++++++++++++++++++++-------------
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2 files changed, 82 insertions(+), 51 deletions(-)
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diff --git a/src/plugins/fits/non_linear.h b/src/plugins/fits/non_linear.h
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index 4506704..74e82e7 100644
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--- a/src/plugins/fits/non_linear.h
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+++ b/src/plugins/fits/non_linear.h
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@@ -18,6 +18,7 @@
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#include <gsl/gsl_blas.h>
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#include <gsl/gsl_multifit_nlin.h>
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#include <gsl/gsl_statistics.h>
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+#include <gsl/gsl_version.h>
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#include "common.h"
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struct data {
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@@ -100,6 +101,7 @@ bool kstfit_nonlinear(
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gsl_multifit_function_fdf function;
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gsl_vector_view vectorViewInitial;
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gsl_matrix* pMatrixCovariance;
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+ gsl_matrix *pMatrixJacobian;
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struct data d;
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double dXInitial[NUM_PARAMS];
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double* pInputX;
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@@ -177,37 +179,50 @@ bool kstfit_nonlinear(
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}
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iIterations++;
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} while( iStatus == GSL_CONTINUE && iIterations < MAX_NUM_ITERATIONS );
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- gsl_multifit_covar( pSolver->J, 0.0, pMatrixCovariance );
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-
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- //
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- // determine the fitted values...
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- //
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- for( i=0; i<NUM_PARAMS; i++ ) {
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- dXInitial[i] = gsl_vector_get( pSolver->x, i );
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- }
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-
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- for( i=0; i<iLength; i++ ) {
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- vectorOutYFitted->value()[i] = function_calculate( pInputX[i], dXInitial );
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- vectorOutYResiduals->value()[i] = pInputY[i] - vectorOutYFitted->value()[i];
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- }
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+#if GSL_MAJOR_VERSION >= 2
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+ pMatrixJacobian = gsl_matrix_alloc( iLength, NUM_PARAMS );
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+#else
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+ pMatrixJacobian = pSolver->J;
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+#endif
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+ if ( pMatrixJacobian != NULL) {
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+#if GSL_MAJOR_VERSION >= 2
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+ gsl_multifit_fdfsolver_jac( pSolver, pMatrixJacobian );
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+#endif
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+ gsl_multifit_covar( pMatrixJacobian, 0.0, pMatrixCovariance );
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+
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+ //
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+ // determine the fitted values...
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+ //
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+ for( i=0; i<NUM_PARAMS; i++ ) {
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+ dXInitial[i] = gsl_vector_get( pSolver->x, i );
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+ }
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- //
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- // fill in the parameter values and covariance matrix...
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- //
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- for( i=0; i<NUM_PARAMS; i++ ) {
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- vectorOutYParameters->value()[i] = gsl_vector_get( pSolver->x, i );
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- for( j=0; j<NUM_PARAMS; j++ ) {
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- vectorOutYCovariance->value()[(i*NUM_PARAMS)+j] = gsl_matrix_get( pMatrixCovariance, i, j );
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+ for( i=0; i<iLength; i++ ) {
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+ vectorOutYFitted->value()[i] = function_calculate( pInputX[i], dXInitial );
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+ vectorOutYResiduals->value()[i] = pInputY[i] - vectorOutYFitted->value()[i];
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}
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- }
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- //
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- // determine the value of chi^2/nu
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- //
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- scalarOutChi->setValue(gsl_blas_dnrm2( pSolver->f ));
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+ //
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+ // fill in the parameter values and covariance matrix...
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+ //
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+ for( i=0; i<NUM_PARAMS; i++ ) {
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+ vectorOutYParameters->value()[i] = gsl_vector_get( pSolver->x, i );
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+ for( j=0; j<NUM_PARAMS; j++ ) {
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+ vectorOutYCovariance->value()[(i*NUM_PARAMS)+j] = gsl_matrix_get( pMatrixCovariance, i, j );
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+ }
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+ }
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- bReturn = true;
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+ //
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+ // determine the value of chi^2/nu
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+ //
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+ scalarOutChi->setValue(gsl_blas_dnrm2( pSolver->f ));
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+ bReturn = true;
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+
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+#if GSL_MAJOR_VERSION >= 2
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+ gsl_matrix_free( pMatrixJacobian );
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+#endif
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+ }
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gsl_matrix_free( pMatrixCovariance );
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}
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gsl_multifit_fdfsolver_free( pSolver );
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diff --git a/src/plugins/fits/non_linear_weighted.h b/src/plugins/fits/non_linear_weighted.h
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index 6ca7d6f..347ae9d 100644
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--- a/src/plugins/fits/non_linear_weighted.h
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+++ b/src/plugins/fits/non_linear_weighted.h
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@@ -18,6 +18,7 @@
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#include <gsl/gsl_blas.h>
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#include <gsl/gsl_multifit_nlin.h>
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#include <gsl/gsl_statistics.h>
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+#include <gsl/gsl_version.h>
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#include "common.h"
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struct data {
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@@ -101,6 +102,7 @@ bool kstfit_nonlinear_weighted(
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gsl_multifit_function_fdf function;
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gsl_vector_view vectorViewInitial;
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gsl_matrix* pMatrixCovariance;
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+ gsl_matrix *pMatrixJacobian;
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struct data d;
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double dXInitial[NUM_PARAMS];
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double* pInputs[3];
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@@ -193,37 +195,51 @@ bool kstfit_nonlinear_weighted(
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}
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while( iStatus == GSL_CONTINUE && iIterations < MAX_NUM_ITERATIONS );
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- gsl_multifit_covar( pSolver->J, 0.0, pMatrixCovariance );
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-
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- //
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- // determine the fitted values...
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- //
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- for( i=0; i<NUM_PARAMS; i++ ) {
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- dXInitial[i] = gsl_vector_get( pSolver->x, i );
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- }
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+#if GSL_MAJOR_VERSION >= 2
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+ pMatrixJacobian = gsl_matrix_alloc( iLength, NUM_PARAMS );
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+#else
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+ pMatrixJacobian = pSolver->J;
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+#endif
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+
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+ if ( pMatrixJacobian != NULL) {
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+#if GSL_MAJOR_VERSION >= 2
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+ gsl_multifit_fdfsolver_jac( pSolver, pMatrixJacobian );
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+#endif
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+ gsl_multifit_covar( pMatrixJacobian, 0.0, pMatrixCovariance );
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+
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+ //
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+ // determine the fitted values...
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+ //
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+ for( i=0; i<NUM_PARAMS; i++ ) {
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+ dXInitial[i] = gsl_vector_get( pSolver->x, i );
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+ }
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- for( i=0; i<iLength; i++ ) {
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- vectorOutYFitted->value()[i] = function_calculate( pInputs[XVALUES][i], dXInitial );
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- vectorOutYResiduals->value()[i] = pInputs[YVALUES][i] - vectorOutYFitted->value()[i];
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- }
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+ for( i=0; i<iLength; i++ ) {
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+ vectorOutYFitted->value()[i] = function_calculate( pInputs[XVALUES][i], dXInitial );
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+ vectorOutYResiduals->value()[i] = pInputs[YVALUES][i] - vectorOutYFitted->value()[i];
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+ }
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- //
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- // fill in the parameter values and covariance matrix...
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- //
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- for( i=0; i<NUM_PARAMS; i++ ) {
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- vectorOutYParameters->value()[i] = gsl_vector_get( pSolver->x, i );
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- for( j=0; j<NUM_PARAMS; j++ ) {
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- vectorOutYCovariance->value()[(i*NUM_PARAMS)+j] = gsl_matrix_get( pMatrixCovariance, i, j );
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+ //
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+ // fill in the parameter values and covariance matrix...
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+ //
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+ for( i=0; i<NUM_PARAMS; i++ ) {
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+ vectorOutYParameters->value()[i] = gsl_vector_get( pSolver->x, i );
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+ for( j=0; j<NUM_PARAMS; j++ ) {
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+ vectorOutYCovariance->value()[(i*NUM_PARAMS)+j] = gsl_matrix_get( pMatrixCovariance, i, j );
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+ }
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}
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- }
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- //
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- // determine the value of chi^2/nu
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- //
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- scalarOutChi->setValue(gsl_blas_dnrm2( pSolver->f ));
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+ //
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+ // determine the value of chi^2/nu
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+ //
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+ scalarOutChi->setValue(gsl_blas_dnrm2( pSolver->f ));
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- bReturn = true;
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+ bReturn = true;
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+#if GSL_MAJOR_VERSION >= 2
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+ gsl_matrix_free( pMatrixJacobian );
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+#endif
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+ }
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gsl_matrix_free( pMatrixCovariance );
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}
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gsl_multifit_fdfsolver_free( pSolver );
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