libsvm.h

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#ifndef GRT_LIB_SVM_HEADER
#define GRT_LIB_SVM_HEADER

//Define the following to stop issues with visual studio

// Must be defined before following inclusions
// May be already defined : check with ifndef
#ifndef _CRT_SECURE_NO_WARNINGS
#define _CRT_SECURE_NO_WARNINGS
#endif
#ifndef _CRT_NONSTDC_NO_DEPRECATE
#define _CRT_NONSTDC_NO_DEPRECATE
#endif

#include <math.h>
#include <stdio.h>
#include <stdlib.h>
#include <ctype.h>
#include <float.h>
#include <string.h>
#include <stdarg.h>
#include <limits.h>
#include <locale.h>
#include <iostream>
#include <algorithm>

namespace LIBSVM {

#define LIBSVM_VERSION 312
    
#ifdef __cplusplus
extern "C" {
#endif

extern int libsvm_version;

struct svm_node
{
 int index;
 double value;
};

struct svm_problem
{
    svm_problem(){
        y = NULL;
        x = NULL;
    }

 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
{
    svm_parameter(){
        weight_label = NULL;
        weight = NULL;
    }
 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

} //End of namespace LIBSVM

#endif //GRT_LIB_SVM_HEADER