New translations svm.h (Chinese Simplified)

lang/zh/gklearn/gedlib/lib/libsvm.3.22/svm.h

@@ -0,0 +1,104 @@
+#ifndef _LIBSVM_H
+#define _LIBSVM_H
+
+#define LIBSVM_VERSION 322
+
+#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;
+};
+
+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 */