svm.h

基于支持向量机的分类方法

#ifndef _LIBSVM_H
#define _LIBSVM_H

#ifdef __cplusplus
extern "C" {
#endif

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, MATRIX };	/* kernel_type */

struct svm_parameter
{
	int svm_type;
	int kernel_type;
	double 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 */
};

struct svm_model *svm_train(const struct svm_problem *prob,
			    const struct svm_parameter *param);


int svm_save_model(const char *model_file_name, const struct svm_model *model);

void svm_copy_model(double *ALPHAY,double *SVs,double *BIAS,double *NSV,double *NLABEL, struct svm_model *model);

int get_svm_model_l(struct svm_model* model);

int get_svm_model_nrclass(struct svm_model* model);

void svm_copy_model(double ALPHAY[],double SVs[],double BIAS[],double NSV[],double NLABEL[],struct svm_model* model);
struct svm_model *svm_read_model(double ALPHAY[],double SVs[],double BIAS[],double NSV[],double NLABEL[], double params[], int n, int l);

struct svm_model *svm_load_model(const char *model_file_name);

double svm_predict(const struct svm_model *model, const struct svm_node *x);

void svm_destroy_model(struct svm_model *model);

const char *svm_check_parameter(const struct svm_problem *prob, const struct svm_parameter *param);

int kernel_type_matrix(struct svm_model *model);

#ifdef __cplusplus
}
#endif

#endif /* _LIBSVM_H */