#ifndef parameters_h#define parameters_h 1#include <stdlib.h>#include <iostream.h>#include <fstream.h>#include "globals.h"/** * Class for all SVM-parameters * @li read and write access to all parameters * @li stream input and output * * @author Stefan Rueping <rueping@ls8.cs.uni-dortmund.de> * @version 0.1 **/class parameters_c{  // neg means "f(x) < y-eps" (<=> "*")  // machine and kernel type    // capacity public:  // input and output functions  friend istream& operator >> (istream& data_stream, parameters_c& the_parameters);  friend ostream& operator << (ostream& data_stream, parameters_c& the_parameters);  // default methods  parameters_c();  void clear();  // capacity constraint  SVMFLOAT realC;   // loss function  SVMFLOAT Lpos, Lneg;  SVMFLOAT epsilon_pos, epsilon_neg;  int quadraticLossPos, quadraticLossNeg;  int balance_cost;  // default parameters for examples  int do_scale; // scale examples  int do_scale_y; // scale y-values  example_format default_example_format;  // type of SVM  int is_pattern; // set Lpos=0 for y>0 and Lneg=0 for y<0  int is_linear; // kernel=dot, use folding  int is_distribution;  int biased; // biased hyperplane (w*x+b) or unbiased (w*x)?  // do cross validation?  SVMINT cross_validation; // cross-validate on training set  int cv_window; // do cross-validation by means of a sliding window  int cv_inorder; // do cross-validation in given order of examples  // parameters for search of C  char search_c;  SVMINT search_stop;  SVMFLOAT c_min; // search for C to minimize loss  SVMFLOAT c_max;  SVMFLOAT c_delta;  // numerical optimization parameters  SVMFLOAT is_zero;  // when is a lagrangian multiplier considered 0  SVMFLOAT nu; // nu-SVM  int is_nu;  SVMINT max_iterations;  SVMINT working_set_size;  SVMINT shrink_const;  SVMFLOAT descend;  // make at least this much descend on WS  SVMFLOAT convergence_epsilon;  SVMINT kernel_cache;  int use_min_prediction;  SVMFLOAT min_prediction; // let pred =  max(min_prediction,f(x))  /**   * Verbosity (higher level includes smaller):   * 0 : only critical errors   * 1 : information about success of algorithm   * 2 : small summary about training and test   * 3 : larger summary about training   * 4 : information about each iteration   * 5 : flood   */  int verbosity;  int print_w; // print whole hyperplane?  int loo_estim; // print loo estim?  SVMFLOAT get_Cpos(){ return(Lpos*realC); };  SVMFLOAT get_Cneg(){ return(Lneg*realC); };};istream& operator >> (istream& data_stream, parameters_c& the_parameters);ostream& operator << (ostream& data_stream, parameters_c& the_parameters);#endif