svmClassification.h

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#ifndef svm_h
#define svm_h

#include <vector>
#include "baseModel.h"
#include "../dependencies/libsvm/libsvm.h"

template<typename T>
class svmClassification : public baseModel<T> {
    
public:
    enum SVMType{ C_SVC = 0, NU_SVC, ONE_CLASS, EPSILON_SVR, NU_SVR };
    enum KernelType{ LINEAR_KERNEL = 0, POLY_KERNEL, RBF_KERNEL, SIGMOID_KERNEL, PRECOMPUTED_KERNEL };
    
    svmClassification(
                      KernelType kernelType = LINEAR_KERNEL,
                      SVMType svmType = C_SVC,
                      bool useScaling = true,
                      bool useNullRejection = false,
                      bool useAutoGamma = true,
                      float gamma = 0.1,
                      unsigned int degree = 3,
                      float coef0 = 0,
                      float nu = 0.5,
                      float C = 1,
                      bool useCrossValidation = false,
                      unsigned int kFoldValue = 10
                      );
    
    svmClassification(int numInputs);
    
    ~svmClassification();
    
    void train(const std::vector<trainingExampleTemplate<T> > &trainingSet);
    
    T run(const std::vector<T> &inputVector);
    
    void reset();
    
    bool init(KernelType kernelType,SVMType svmType,bool useScaling,bool useNullRejection,bool useAutoGamma,
              float gamma,
              unsigned int degree,
              float coef0,
              float nu,
              float C,
              bool useCrossValidation,
              unsigned int kFoldValue
              );
    
    int getNumInputs() const;
    std::vector<int> getWhichInputs() const;
    
    
#ifndef EMSCRIPTEN
    void getJSONDescription(Json::Value &currentModel);
#endif
    
private:
    bool problemSet;
    struct LIBSVM::svm_model *model;
    struct LIBSVM::svm_parameter param;
    struct LIBSVM::svm_problem problem;
    
    int numInputs;
    
    std::vector<double> inRanges;
    std::vector<double> inBases;
    
    bool trained;
};

#endif