text: #ifndef _LIBSVM_H
  #define _LIBSVM_H
  #define _DENSE_REP
  #define LIBSVM_VERSION 317
  
  #ifdef __cplusplus
  extern "C" {
  #endif
  
  extern int libsvm_version;
  
  #ifdef _DENSE_REP
  struct svm_node
  {
  	int dim;
  	double *values;
  };
  
  struct svm_problem
  {
  	int l;
  	double *y;
  	struct svm_node *x;
  };
  
  #else
  struct svm_node
  {
  	int index;
  	double value;
  };
  
  struct svm_problem
  {
  	int l;
  	double *y;
  	struct svm_node **x;
  };
  #endif
  
  enum { C_SVC, NU_SVC, ONE_CLASS, EPSILON_SVR, NU_SVR };	/* svm_type */
  enum { LINEAR, POLY, RBF, SIGMOID, PRECOMPUTED }; /* kernel_type */
  
  struct svm_parameter
  {
  	int svm_type;
  	int kernel_type;
  	int degree;	/* for poly */
  	double gamma;	/* for poly/rbf/sigmoid */
  	double coef0;	/* for poly/sigmoid */
  
  	/* these are for training only */
  	double cache_size; /* in MB */
  	double eps;	/* stopping criteria */
  	double C;	/* for C_SVC, EPSILON_SVR and NU_SVR */
  	int nr_weight;		/* for C_SVC */
  	int *weight_label;	/* for C_SVC */
  	double* weight;		/* for C_SVC */
  	double nu;	/* for NU_SVC, ONE_CLASS, and NU_SVR */
  	double p;	/* for EPSILON_SVR */
  	int shrinking;	/* use the shrinking heuristics */
  	int probability; /* do probability estimates */
  };
  
  //
  // svm_model
  //
  struct svm_model
  {
  	struct svm_parameter param;	/* parameter */
  	int nr_class;		/* number of classes, = 2 in regression/one class svm */
  	int l;			/* total #SV */
  #ifdef _DENSE_REP
  	struct svm_node *SV;		/* SVs (SV[l]) */
  #else
  	struct svm_node **SV;		/* SVs (SV[l]) */
  #endif
  	double **sv_coef;	/* coefficients for SVs in decision functions (sv_coef[k-1][l]) */
  	double *rho;		/* constants in decision functions (rho[k*(k-1)/2]) */
  	double *probA;		/* pariwise probability information */
  	double *probB;
  	int *sv_indices;        /* sv_indices[0,...,nSV-1] are values in [1,...,num_traning_data] to indicate SVs in the training set */
  
  	/* for classification only */
  
  	int *label;		/* label of each class (label[k]) */
  	int *nSV;		/* number of SVs for each class (nSV[k]) */
  				/* nSV[0] + nSV[1] + ... + nSV[k-1] = l */
  	/* XXX */
  	int free_sv;		/* 1 if svm_model is created by svm_load_model*/
  				/* 0 if svm_model is created by svm_train */
  };
  
  struct svm_model *svm_train(const struct svm_problem *prob, const struct svm_parameter *param);
  void svm_cross_validation(const struct svm_problem *prob, const struct svm_parameter *param, int nr_fold, double *target);
  
  int svm_save_model(const char *model_file_name, const struct svm_model *model);
  struct svm_model *svm_load_model(const char *model_file_name);
  
  int svm_get_svm_type(const struct svm_model *model);
  int svm_get_nr_class(const struct svm_model *model);
  void svm_get_labels(const struct svm_model *model, int *label);
  void svm_get_sv_indices(const struct svm_model *model, int *sv_indices);
  int svm_get_nr_sv(const struct svm_model *model);
  double svm_get_svr_probability(const struct svm_model *model);
  
  double svm_predict_values(const struct svm_model *model, const struct svm_node *x, double* dec_values);
  double svm_predict(const struct svm_model *model, const struct svm_node *x);
  double svm_predict_probability(const struct svm_model *model, const struct svm_node *x, double* prob_estimates);
  
  void svm_free_model_content(struct svm_model *model_ptr);
  void svm_free_and_destroy_model(struct svm_model **model_ptr_ptr);
  void svm_destroy_param(struct svm_parameter *param);
  
  const char *svm_check_parameter(const struct svm_problem *prob, const struct svm_parameter *param);
  int svm_check_probability_model(const struct svm_model *model);
  
  void svm_set_print_string_function(void (*print_func)(const char *));
  
  #ifdef __cplusplus
  }
  #endif
  
  #endif /* _LIBSVM_H */
  