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#ifndef _LIBSVM_H
#define _LIBSVM_H
#define LIBSVM_VERSION 310
#ifdef __cplusplus
extern "C" {
#endif
#include "_svm_cython_blas_helpers.h"
struct svm_node
{
int dim;
int ind; /* index. A bit redundant, but needed if using a
precomputed kernel */
double *values;
};
struct svm_problem
{
int l;
double *y;
struct svm_node *x;
double *W; /* instance weights */
};
struct svm_csr_node
{
int index;
double value;
};
struct svm_csr_problem
{
int l;
double *y;
struct svm_csr_node **x;
double *W; /* instance weights */
};
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 */
int max_iter; /* ceiling on Solver runtime */
int random_seed; /* seed for random number generator */
};
//
// 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]) */
int *n_iter; /* number of iterations run by the optimization routine to fit the model */
int *sv_ind; /* index of support vectors */
double *rho; /* constants in decision functions (rho[k*(k-1)/2]) */
double *probA; /* pairwise 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_csr_model
{
struct svm_parameter param; /* parameter */
int nr_class; /* number of classes, = 2 in regression/one class svm */
int l; /* total #SV */
struct svm_csr_node **SV; /* SVs (SV[l]) */
double **sv_coef; /* coefficients for SVs in decision functions (sv_coef[k-1][l]) */
int *n_iter; /* number of iterations run by the optimization routine to fit the model */
int *sv_ind; /* index of support vectors */
double *rho; /* constants in decision functions (rho[k*(k-1)/2]) */
double *probA; /* pairwise 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 */
};
/* svm_ functions are defined by libsvm_template.cpp from generic versions in svm.cpp */
struct svm_model *svm_train(const struct svm_problem *prob, const struct svm_parameter *param, int *status, BlasFunctions *blas_functions);
void svm_cross_validation(const struct svm_problem *prob, const struct svm_parameter *param, int nr_fold, double *target, BlasFunctions *blas_functions);
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, BlasFunctions *blas_functions);
double svm_predict(const struct svm_model *model, const struct svm_node *x, BlasFunctions *blas_functions);
double svm_predict_probability(const struct svm_model *model, const struct svm_node *x, double* prob_estimates, BlasFunctions *blas_functions);
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);
void svm_set_print_string_function(void (*print_func)(const char *));
/* sparse version */
/* svm_csr_ functions are defined by libsvm_template.cpp from generic versions in svm.cpp */
struct svm_csr_model *svm_csr_train(const struct svm_csr_problem *prob, const struct svm_parameter *param, int *status, BlasFunctions *blas_functions);
void svm_csr_cross_validation(const struct svm_csr_problem *prob, const struct svm_parameter *param, int nr_fold, double *target, BlasFunctions *blas_functions);
int svm_csr_get_svm_type(const struct svm_csr_model *model);
int svm_csr_get_nr_class(const struct svm_csr_model *model);
void svm_csr_get_labels(const struct svm_csr_model *model, int *label);
double svm_csr_get_svr_probability(const struct svm_csr_model *model);
double svm_csr_predict_values(const struct svm_csr_model *model, const struct svm_csr_node *x, double* dec_values, BlasFunctions *blas_functions);
double svm_csr_predict(const struct svm_csr_model *model, const struct svm_csr_node *x, BlasFunctions *blas_functions);
double svm_csr_predict_probability(const struct svm_csr_model *model, const struct svm_csr_node *x, double* prob_estimates, BlasFunctions *blas_functions);
void svm_csr_free_model_content(struct svm_csr_model *model_ptr);
void svm_csr_free_and_destroy_model(struct svm_csr_model **model_ptr_ptr);
void svm_csr_destroy_param(struct svm_parameter *param);
const char *svm_csr_check_parameter(const struct svm_csr_problem *prob, const struct svm_parameter *param);
/* end sparse version */
#ifdef __cplusplus
}
#endif
#endif /* _LIBSVM_H */
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