svm.h

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/*
 * Copyright (c) 2000-2014 Chih-Chung Chang and Chih-Jen Lin
 * All rights reserved.
 *
 * Redistribution and use in source and binary forms, with or without
 * modification, are permitted provided that the following conditions
 * are met:
 *
 * 1. Redistributions of source code must retain the above copyright
 * notice, this list of conditions and the following disclaimer.
 *
 * 2. Redistributions in binary form must reproduce the above copyright
 * notice, this list of conditions and the following disclaimer in the
 * documentation and/or other materials provided with the distribution.
 *
 * 3. Neither name of copyright holders nor the names of its contributors
 * may be used to endorse or promote products derived from this software
 * without specific prior written permission.
 *
 *
 * THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS
 * ``AS IS'' AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT
 * LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR
 * A PARTICULAR PURPOSE ARE DISCLAIMED.  IN NO EVENT SHALL THE REGENTS OR
 * CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL,
 * EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO,
 * PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR
 * PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF
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 * NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
 * SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
 */
 
#ifndef _LIBSVM_H
#define _LIBSVM_H
 
#include <cstdlib>
 
#define LIBSVM_VERSION 314
 
#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;          /* pairwise 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);
 
  double svm_predict_values_twoclass(const struct svm_model* model, const struct svm_node* x);
  double svm_hyper_w_normsqr_twoclass(const struct svm_model* model);
 
 
  double k_function(const svm_node* x, const svm_node* y, const svm_parameter& param);
 
 
  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 */