23 #include <gsl/gsl_multifit_nlinear.h> 24 #include <gsl/gsl_blas.h> 35 return std::make_shared<GSLEngine>(max_iterations);
41 m_itmax{itmax}, m_xtol{xtol}, m_gtol{gtol}, m_ftol{ftol}, m_delta{delta} {
43 gsl_set_error_handler_off();
70 return gsl_vector_get(
m_v,
m_i);
74 return *gsl_vector_ptr(
m_v,
m_i);
81 const gsl_vector *
m_v;
102 return gsl_vector_get(
m_v,
m_i);
111 auto adata =
std::tie(parameter_manager, residual_estimator);
114 const gsl_multifit_nlinear_type *type = gsl_multifit_nlinear_trust;
119 gsl_multifit_nlinear_parameters params = gsl_multifit_nlinear_default_parameters();
123 params.trs = gsl_multifit_nlinear_trs_lm;
126 params.scale = gsl_multifit_nlinear_scale_levenberg;
128 params.solver = gsl_multifit_nlinear_solver_cholesky;
130 params.fdtype = GSL_MULTIFIT_NLINEAR_FWDIFF;
135 gsl_multifit_nlinear_workspace *workspace = gsl_multifit_nlinear_alloc(
139 if (workspace ==
nullptr) {
146 gsl_vector_view gsl_param_view = gsl_vector_view_array(param_values.data(), param_values.size());
149 auto function = [](
const gsl_vector *
x,
void *extra, gsl_vector *f) ->
int {
150 auto *extra_ptr = (decltype(adata) *) extra;
157 gsl_multifit_nlinear_fdf fdf;
166 gsl_multifit_nlinear_init(&gsl_param_view.vector, &fdf, workspace);
170 gsl_vector *residual = gsl_multifit_nlinear_residual(workspace);
171 gsl_blas_ddot(residual, residual, &chisq0);
175 int ret = gsl_multifit_nlinear_driver(
188 gsl_blas_ddot(residual, residual, &chisq);
195 summary.success_flag = (ret == GSL_SUCCESS);
196 summary.iteration_no = gsl_multifit_nlinear_niter(workspace);
197 summary.parameter_sigmas = {};
200 gsl_matrix *J = gsl_multifit_nlinear_jac(workspace);
201 gsl_matrix_view covar = gsl_matrix_view_array(covariance_matrix.data(), parameter_manager.
numberOfParameters(),
203 gsl_multifit_nlinear_covar(J, 0.0, &covar.matrix);
209 for (
size_t i = 0; i < residual->size; ++i) {
210 auto v = gsl_vector_get(residual, i);
213 sigma2 /= (fdf.n - fdf.p);
215 for (
auto ci = covariance_matrix.begin(); ci != covariance_matrix.end(); ++ci) {
225 int levmar_reason = 0;
226 if (ret == GSL_SUCCESS) {
227 levmar_reason = (info == 1) ? 2 : 1;
229 else if (ret == GSL_EMAXITER) {
236 gsl_blas_dnrm2(workspace->g),
237 gsl_blas_dnrm2(workspace->dx),
239 static_cast<double>(summary.iteration_no),
240 static_cast<double>(levmar_reason),
241 static_cast<double>(fdf.nevalf),
242 static_cast<double>(fdf.nevaldf),
247 gsl_multifit_nlinear_free(workspace);
std::vector< double > convertCovarianceMatrixToWorldSpace(std::vector< double > covariance_matrix) const
GslVectorConstIterator(const gsl_vector *v)
GslVectorIterator & operator++()
Class containing the summary information of solving a least square minimization problem.
std::shared_ptr< DependentParameter< std::shared_ptr< EngineParameter > > > x
GSLEngine(int itmax=1000, double xtol=1e-8, double gtol=1e-8, double ftol=1e-8, double delta=1e-4)
Constructs a new instance of the engine.
LeastSquareSummary solveProblem(EngineParameterManager ¶meter_manager, ResidualEstimator &residual_estimator) override
GslVectorConstIterator & operator++()
void updateEngineValues(DoubleIter new_values_iter)
Updates the managed parameters with the given engine values.
void populateResiduals(DoubleIter output_iter) const
static std::shared_ptr< LeastSquareEngine > createLevmarEngine(unsigned max_iterations)
std::size_t numberOfResiduals() const
GslVectorIterator(gsl_vector *v)
static LeastSquareEngineManager::StaticEngine levmar_engine
GslVectorIterator operator++(int)
Class responsible for managing the parameters the least square engine minimizes.
GslVectorConstIterator operator++(int)
Provides to the LeastSquareEngine the residual values.
std::size_t numberOfParameters()
Returns the number of parameters managed by the manager.
void getEngineValues(DoubleIter output_iter) const
Returns the engine values of the managed parameters.