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2021-10-14 13:47:35 +02:00
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+
+Copyright (c) 2000-2013 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
+LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING
+NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
+SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
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+#pragma once
+
+#define LIBSVM_VERSION 324
+
+extern "C" {
+
+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 
+
+/* Table help you to transforms Enums to strings */
+//extern static const char *svm_type_table[5];
+//extern static const char *kernel_type_table[5];
+inline const char* get_svm_type(const unsigned int code)
+{
+	static const char* svm_type_table[] = { "c_svc", "nu_svc", "one_class", "epsilon_svr", "nu_svr", nullptr };
+	return svm_type_table[code];
+}
+
+inline const char* get_kernel_type(const unsigned int code)
+{
+	static const char* types[] = { "linear", "polynomial", "rbf", "sigmoid", "precomputed", nullptr };
+	return types[code];
+}
+	
+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* 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*));
+
+}