101 lines
3.1 KiB
Text
101 lines
3.1 KiB
Text
#ifndef _LIBSVM_H
|
|
#define _LIBSVM_H
|
|
|
|
#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;
|
|
};
|
|
|
|
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 */
|
|
};
|
|
|
|
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
|
|
|
|
#endif /* _LIBSVM_H */
|