svm.h

变化检测源程序

#ifndef _LIBSVM_H
#define _LIBSVM_H

#ifdef __cplusplus
extern "C" {
#endif

struct svm_node
{
	int index;//index = -1 indicates the end of one vector
	double value;
};

struct svm_problem
{
	int l;//the number of training data
	double *y;//an array containing their target values. (integers in classification, real numbers in regression) 
	struct svm_node **x;//an array of pointers, each of which points to a sparse
    //representation (array of svm_node) of one training vector
};

enum { C_SVC, NU_SVC, ONE_CLASS, ONE_CLASS_MY,EPSILON_SVR, NU_SVR };	/* svm_type */
enum { LINEAR, POLY, RBF, SIGMOID,MY };	/* 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 */
	int probability; /* do probability estimates */
};
struct svm_model
{
	svm_parameter param;	// parameter
	int nr_class;		// number of classes, = 2 in regression/one class svm
	int l;			// total #SV
	svm_node **SV;		// SVs (SV[l])
	svm_node **SV2;		// SVs (SV[l])
	double **sv_coef;	// coefficients for SVs in decision functions (sv_coef[n-1][l])
	double *rho;		// constants in decision functions (rho[n*(n-1)/2])
	double *probA;          // pariwise probability information
	double *probB;

	// for classification only

	int *label;		// label of each class (label[n])
	int *nSV;		// number of SVs for each class (nSV[n])
				// nSV[0] + nSV[1] + ... + nSV[n-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
};
/*This function constructs and returns an SVM model according to
    the given training data and parameters*/
struct svm_model *svm_trainmy(const svm_problem *prob,const struct svm_problem *prob2, const svm_parameter *param);

int svm_save_model(const char *model_file_name, const struct svm_model *model);//保存模型
struct svm_model *svm_load_model(const char *model_file_name);//登陆模型

void svm_cross_validation(const struct svm_problem *prob, const struct svm_parameter *param, int nr_fold, double *target);
/*This function conducts cross validation. Data are separated to
    nr_fold folds. Under given parameters, sequentially each fold is
    validated using the model from training the remaining. Predicted
    labels in the validation process are stored in the array called
    target*/


int svm_get_svm_type(const struct svm_model *model);

int svm_get_nr_class(const struct svm_model *model);
/* For a classification model, this function gives the number of
    classes. For a regression or an one-class model, 2 is returned*/

void svm_get_labels(const struct svm_model *model, int *label);
/*  For a classification model, this function outputs the name of
    labels into an array called label. For regression and one-class
    models, label is unchanged*/

double svm_get_svr_probability(const struct svm_model *model);
/* For a regression model with probability information, this function
    outputs a value sigma > 0. For test data, we consider the
    probability model: target value = predicted value + z, z: Laplace
    distribution e^(-|z|/sigma)/(2sigma)
    If the model is not for svr or does not contain required
    information, 0 is returned*/

void svm_predict_values(const struct svm_model *model, const struct svm_node *x,const struct svm_node *x2, double* dec_values);
/*  This function gives decision values on a test vector x given a
    model.
    For a classification model with nr_class classes, this function
    gives nr_class*(nr_class-1)/2 decision values in the array
    dec_values, where nr_class can be obtained from the function
    svm_get_nr_class. The order is label[0] vs. label[1], ...,
    label[0] vs. label[nr_class-1], label[1] vs. label[2], ...,
    label[nr_class-2] vs. label[nr_class-1], where label can be
    obtained from the function svm_get_labels.
    For a regression model, label[0] is the function value of x
    calculated using the model. For one-class model, label[0] is +1 or
    -1.*/

double svm_predict(const struct svm_model *model, const struct svm_node *x, const struct svm_node *x2);
 /*This function does classification or regression on a test vector x
    given a model.
    For a classification model, the predicted class for x is returned.
    For a regression model, the function value of x calculated using
    the model is returned. For an one-class model, +1 or -1 is
    returned*/

double svm_predict_probability(const struct svm_model *model, const struct svm_node *x, double* prob_estimates);
/*This function does classification or regression on a test vector x
    given a model with probability information.

    For a classification model with probability information, this
    function gives nr_class probability estimates in the array
    prob_estimates. nr_class can be obtained from the function
    svm_get_nr_class. The class with the highest probability is
    returned. For all other situations, the array prob_estimates is
    unchanged and the returned value is the same as that of
    svm_predict */

void svm_destroy_model(struct svm_model *model);//释放内存
void svm_destroy_param(struct svm_parameter *param);//释放内存

const char *svm_check_parameter(const struct svm_problem *prob, const struct svm_parameter *param);
/*This function checks whether the parameters are within the feasible
    range of the problem. This function should be called before calling
    svm_train(). It returns NULL if the parameters are feasible, 
    otherwise an error message is returned */

int svm_check_probability_model(const struct svm_model *model);
/* This function checks whether the model contains required
    information to do probability estimates. If so, it returns
    +1. Otherwise, 0 is returned. This function should be called
    before calling svm_get_svr_probability and
    svm_predict_probability*/

#ifdef __cplusplus
}
#endif

#endif /* _LIBSVM_H */