#ifndef MLPP_SVC_H
#define MLPP_SVC_H

//
//  SVC.hpp
//
//  Created by Marc Melikyan on 10/2/20.
//


// https://towardsdatascience.com/svm-implementation-from-scratch-python-2db2fc52e5c2
// Illustratd a practical definition of the Hinge Loss function and its gradient when optimizing with SGD.

#include <vector>
#include <string>

namespace MLPP {

    class SVC{
        
        public:
            SVC(std::vector<std::vector<double>> inputSet, std::vector<double> outputSet, double C);
            std::vector<double> modelSetTest(std::vector<std::vector<double>> X);
            double modelTest(std::vector<double> x);
            void gradientDescent(double learning_rate, int max_epoch, bool UI = 1);
            void SGD(double learning_rate, int max_epoch, bool UI = 1);
            void MBGD(double learning_rate, int max_epoch, int mini_batch_size, bool UI = 1);
            double score();
            void save(std::string fileName);
        private:

            double Cost(std::vector <double> y_hat, std::vector<double> y, std::vector<double> weights, double C);
        
            std::vector<double> Evaluate(std::vector<std::vector<double>> X);
            std::vector<double> propagate(std::vector<std::vector<double>> X);
            double Evaluate(std::vector<double> x);
            double propagate(std::vector<double> x);
            void forwardPass();
        
            std::vector<std::vector<double>> inputSet;
            std::vector<double> outputSet;
            std::vector<double> z;
            std::vector<double> y_hat;
            std::vector<double> weights;
            double bias;

            double C;
            int n; 
            int k;
        
            // UI Portion
            void UI(int epoch, double cost_prev);        
    };
}

#endif /* SVC_hpp */