svm.h

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/*============================================================================
 
The Medical Imaging Interaction Toolkit (MITK)
 
Copyright (c) German Cancer Research Center (DKFZ)
All rights reserved.
 
Use of this source code is governed by a 3-clause BSD license that can be
found in the LICENSE file.
 
============================================================================*/
 
/*===================================================================
 
Copyright (c) 2000-2014 Chih-Chung Chang and Chih-Jen Lin
All rights reserved.
 
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#ifndef _LIBSVM_H
#define _LIBSVM_H
 
#define LIBSVM_VERSION 318
 
#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;
  double *W; /* instance weight */
};
 
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;
  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 *));
 
#ifdef __cplusplus
}
#endif
 
#endif /* _LIBSVM_H */