113 lines
3.7 KiB
C
113 lines
3.7 KiB
C
#pragma once
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#define LIBSVM_VERSION 324
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extern "C" {
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extern int libsvm_version;
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struct svm_node
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{
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int index;
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double value;
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};
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struct svm_problem
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{
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int l;
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double* y;
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struct svm_node** x;
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};
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enum { C_SVC, NU_SVC, ONE_CLASS, EPSILON_SVR, NU_SVR }; // svm_type
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enum { LINEAR, POLY, RBF, SIGMOID, PRECOMPUTED }; // kernel_type
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/* Table help you to transforms Enums to strings */
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//extern static const char *svm_type_table[5];
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//extern static const char *kernel_type_table[5];
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inline const char* get_svm_type(const unsigned int code)
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{
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static const char* svm_type_table[] = { "c_svc", "nu_svc", "one_class", "epsilon_svr", "nu_svr", nullptr };
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return svm_type_table[code];
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}
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inline const char* get_kernel_type(const unsigned int code)
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{
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static const char* types[] = { "linear", "polynomial", "rbf", "sigmoid", "precomputed", nullptr };
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return types[code];
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}
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struct svm_parameter
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{
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int svm_type;
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int kernel_type;
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int degree; // for poly
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double gamma; // for poly/rbf/sigmoid
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double coef0; // for poly/sigmoid
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// these are for training only
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double cache_size; // in MB
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double eps; // stopping criteria
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double C; // for C_SVC, EPSILON_SVR and NU_SVR
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int nr_weight; // for C_SVC
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int* weight_label; // for C_SVC
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double* weight; // for C_SVC
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double nu; // for NU_SVC, ONE_CLASS, and NU_SVR
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double p; // for EPSILON_SVR
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int shrinking; // use the shrinking heuristics
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int probability; // do probability estimates
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};
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//
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// svm_model
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//
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struct svm_model
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{
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struct svm_parameter param; // parameter
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int nr_class; // number of classes, = 2 in regression/one class svm
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int l; // total #SV
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struct svm_node** SV; // SVs (SV[l])
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double** sv_coef; // coefficients for SVs in decision functions (sv_coef[k-1][l])
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double* rho; // constants in decision functions (rho[k*(k-1)/2])
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double* probA; // pariwise probability information
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double* probB;
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int* sv_indices; // sv_indices[0,...,nSV-1] are values in [1,...,num_traning_data] to indicate SVs in the training set
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// for classification only
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int* label; // label of each class (label[k])
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int* nSV; // number of SVs for each class (nSV[k])
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// nSV[0] + nSV[1] + ... + nSV[k-1] = l
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// XXX
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int free_sv; // 1 if svm_model is created by svm_load_model
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// 0 if svm_model is created by svm_train
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};
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struct svm_model* svm_train(const struct svm_problem* prob, const struct svm_parameter* param);
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void svm_cross_validation(const struct svm_problem* prob, const struct svm_parameter* param, int nr_fold, double* target);
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int svm_save_model(const char* model_file_name, const struct svm_model* model);
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struct svm_model* svm_load_model(const char* model_file_name);
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int svm_get_svm_type(const struct svm_model* model);
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int svm_get_nr_class(const struct svm_model* model);
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void svm_get_labels(const struct svm_model* model, int* label);
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void svm_get_sv_indices(const struct svm_model* model, int* indices);
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int svm_get_nr_sv(const struct svm_model* model);
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double svm_get_svr_probability(const struct svm_model* model);
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double svm_predict_values(const struct svm_model* model, const struct svm_node* x, double* dec_values);
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double svm_predict(const struct svm_model* model, const struct svm_node* x);
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double svm_predict_probability(const struct svm_model* model, const struct svm_node* x, double* prob_estimates);
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void svm_free_model_content(struct svm_model* model_ptr);
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void svm_free_and_destroy_model(struct svm_model** model_ptr_ptr);
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void svm_destroy_param(struct svm_parameter* param);
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const char* svm_check_parameter(const struct svm_problem* prob, const struct svm_parameter* param);
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int svm_check_probability_model(const struct svm_model* model);
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void svm_set_print_string_function(void (*print_func)(const char*));
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}
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