Class fit_nonlin (o2scl)¶

O2scl : Class List

template<class func_t = gen_fit_funct<>, class vec_t = boost::numeric::ublas::vector<double>, class mat_t = boost::numeric::ublas::matrix<double>> class fit_nonlin : public o2scl::fit_nonlin_b<boost::numeric::ublas::vector<double>, boost::numeric::ublas::matrix<double>>, public o2scl::fit_base<gen_fit_funct<>, boost::numeric::ublas::vector<double>, boost::numeric::ublas::matrix<double>>¶

Non-linear least-squares fitting class (GSL)

The GSL-based fitting class using a Levenberg-Marquardt type algorithm. The algorithm stops when

\[ |dx_i| < \mathrm{tol\_abs}+\mathrm{tol\_rel}\times|x_i| \]

where \(dx\) is the last step and \(x\) is the current position. If test_gradient is true, then additionally fit() requires that

\[ \sum_i |g_i| < \mathrm{tol\_abs} \]

where \(g_i\) is the \(i\)-th component of the gradient of the function \(\Phi(x)\) where

\[ \Phi(x) = || F(x) ||^2 \]

Default template arguments

func_t - gen_fit_funct<>
vec_t - boost::numeric::ublas::vector <double >
mat_t - boost::numeric::ublas::matrix <double >

Todo

In class fit_nonlin:

Allow the user to specify the derivatives
Fix so that the user can specify automatic scaling of the fitting parameters, where the initial guess are used for scaling so that the fitting parameters are near unity.

Idea for Future:: Some of these member functions (like update_diag()) don’t depend on member data and could be possibly be moved to a parent class?

Public Functions

inline fit_nonlin()¶

inline virtual ~fit_nonlin()¶

inline virtual int print_iter_nonlin(size_t nv, vec_t &x, vec_t &dx, int iter2, double l_epsabs, double l_epsrel)¶: Print the progress in the current iteration.

inline void resize(size_t n, size_t p)¶: Allocate memory with n data points and p parameters.

inline int set(size_t npar, vec_t &parms, func_t &fitfun)¶: Set the initial values of the parameters and the fitting function to use for the next call to iterate()

inline int iterate()¶: Perform an iteration.

inline virtual int fit(size_t npar, vec_t &parms, mat_t &covar, double &chi2, func_t &fitfun)¶

Fit the data specified in (xdat,ydat) to the function fitfun with the parameters in par.

The covariance matrix for the parameters is returned in covar and the value of \( \chi^2 \) is returned in chi2.

inline virtual const char *type()¶: Return string denoting type (“fit_nonlin”)

Public Members

bool err_nonconv¶: If true, call the error handler if fit() does not converge (default true)

vec_t dx_¶: The last step taken in parameter space.

vec_t f_¶: Desc.

bool test_gradient¶: If true, test the gradient also (default false)

bool use_scaled¶: Use the scaled routine if true (default true)

Protected Functions

inline void free()¶: Free allocated memory.

Protected Attributes

func_t *cff¶: Function to fit.

vec_t x_trial¶: Trial step.

vec_t f_trial¶: Trial function value.

size_t iter¶: Number of iterations.

double xnorm¶: Desc.

double fnorm¶: Desc.

double delta¶: Desc.

double par¶: Desc.

mat_t r¶: Desc.

vec_t tau¶: Desc.

vec_t diag¶: Desc.

vec_t qtf¶: Desc.

vec_t df¶: Desc.

vec_t rptdx¶: Desc.

vec_t newton¶: Desc.

vec_t gradient¶: Desc.

vec_t sdiag¶: Desc.

vec_t w¶: Desc.

vec_t work1¶: Desc.

permutation perm¶: Desc.

size_t ndata¶: Number of data points.

size_t nparm¶: Number of parameters.

vec_t g_¶: Desc.

mat_t J_¶: Desc.

vec_t *x_¶: Desc.