All/C_CPP/001/svm/svm.h

#ifndef _LIBSVM_H
#define _LIBSVM_H

#define LIBSVM_VERSION 332

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;
    double *prob_density_marks;    /* probability information for ONE_CLASS */
    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 *sv_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 *));

#endif