2021-10-14 13:47:35 +02:00

113 lines
3.7 KiB
C

#pragma once
#define LIBSVM_VERSION 324
extern "C" {
extern int libsvm_version;
struct svm_node
{
int index;
double value;
};
struct svm_problem
{
int l;
double* y;
struct svm_node** x;
};
enum { C_SVC, NU_SVC, ONE_CLASS, EPSILON_SVR, NU_SVR }; // svm_type
enum { LINEAR, POLY, RBF, SIGMOID, PRECOMPUTED }; // kernel_type
/* Table help you to transforms Enums to strings */
//extern static const char *svm_type_table[5];
//extern static const char *kernel_type_table[5];
inline const char* get_svm_type(const unsigned int code)
{
static const char* svm_type_table[] = { "c_svc", "nu_svc", "one_class", "epsilon_svr", "nu_svr", nullptr };
return svm_type_table[code];
}
inline const char* get_kernel_type(const unsigned int code)
{
static const char* types[] = { "linear", "polynomial", "rbf", "sigmoid", "precomputed", nullptr };
return types[code];
}
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;
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* 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*));
}