thirdParty/PCL 1.12.0/include/pcl-1.12/pcl/ml/svm.h

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 *  Point Cloud Library (PCL) - www.pointclouds.org
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#pragma once

#define LIBSVM_VERSION 311

#ifdef __cplusplus
extern "C" {
#endif

extern int libsvm_version;

struct svm_node {
  int index;
  double value;
};

struct svm_problem {
  int l;
  double* y;

  struct svm_node** x;
};

struct svm_scaling {
  // index = 1 if usable, index = 0 if not

  struct svm_node* obj;

  // max features scaled
  int max;

  svm_scaling() : max(0) {}
};

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 */

  struct svm_node** SV; /* SVs (SV[l]) */
  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;

  /* 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 */

  /* for scaling */

  struct svm_node* scaling;
};

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);

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