#ifndef nl_system_H
#define nl_system_H
#include <ls_system.h>



/*Non Linear generic model fitting data structure, contain all needed callback functions*/
typedef struct NL_Model_t{
  /*Mandatory members -  without then the fitting procedures will crash ! */
  int type; /*This is meant to be used for debugging and enum types*/
  copy_data_function* copy_data; /*Make a copy of ls_data, it is needed to "save" the results of an previous iteration*/
  compare_function* compare; /*compare the results of different iterations*/
  release_data_function* release_data; /*Release a ls_data pointer from memory, mandatory if you dont wish to have your memory taken by multiple iterations !*/

  /*Optional members - they wont be called by the fitting functions and can be NULL*/
  evaluate_function* evaluate; /* compute the value of the fitting function, it is optional*/
  write_function*  write_param; /*write the params in human readable manner into a file*/
  read_function*  read_param; /*read the params from a file in human readable manner*/
  
    
} nl_model_t;

double non_linear_compute_S_star(
    nl_model_t* nl,
    double** X,
    double* F,
    int n,
    double* parameters,
    void* l_data_original,
    int nSteps
);
/*
Given a non-linear model {nl}, the ${n} points {X} and function points ${F}, packed
parameters {parameters}m approximates the model  by LS with ${nSteps} iterations and returns
the mean-square error of the approximation. {l_data_original} is used solely to unpacking data  */


void * non_linear_approximate(
  nl_model_t* nl,
  void * l_data_original,
  double* deviations,
  double** X,
  double* F,
  int n,
  int LSSteps,
  int NLSteps
  );
  
  /* Given a non-linear model {nl}, the lighting data {l_data} as first estimative,
  the {n} points {X} and its image {F} it efetuates a non-linear approximation with
  {NLSteps} maximum iterations with internal LS approximation of ${LSSteps} iterations*/
  
  #endif