#ifndef MLPP_LIN_REG_H #define MLPP_LIN_REG_H // // LinReg.hpp // // Created by Marc Melikyan on 10/2/20. // #include "core/math/math_defs.h" #include #include class MLPPLinReg { public: MLPPLinReg(std::vector> inputSet, std::vector outputSet, std::string reg = "None", real_t lambda = 0.5, real_t alpha = 0.5); std::vector modelSetTest(std::vector> X); real_t modelTest(std::vector x); void NewtonRaphson(real_t learning_rate, int max_epoch, bool UI); void gradientDescent(real_t learning_rate, int max_epoch, bool UI = 1); void SGD(real_t learning_rate, int max_epoch, bool UI = 1); void Momentum(real_t learning_rate, int max_epoch, int mini_batch_size, real_t gamma, bool UI = 1); void NAG(real_t learning_rate, int max_epoch, int mini_batch_size, real_t gamma, bool UI = 1); void Adagrad(real_t learning_rate, int max_epoch, int mini_batch_size, real_t e, bool UI = 1); void Adadelta(real_t learning_rate, int max_epoch, int mini_batch_size, real_t b1, real_t e, bool UI = 1); void Adam(real_t learning_rate, int max_epoch, int mini_batch_size, real_t b1, real_t b2, real_t e, bool UI = 1); void Adamax(real_t learning_rate, int max_epoch, int mini_batch_size, real_t b1, real_t b2, real_t e, bool UI = 1); void Nadam(real_t learning_rate, int max_epoch, int mini_batch_size, real_t b1, real_t b2, real_t e, bool UI = 1); void MBGD(real_t learning_rate, int max_epoch, int mini_batch_size, bool UI = 1); void normalEquation(); real_t score(); void save(std::string fileName); private: real_t Cost(std::vector y_hat, std::vector y); std::vector Evaluate(std::vector> X); real_t Evaluate(std::vector x); void forwardPass(); std::vector> inputSet; std::vector outputSet; std::vector y_hat; std::vector weights; real_t bias; int n; int k; // Regularization Params std::string reg; int lambda; int alpha; /* This is the controlling param for Elastic Net*/ }; #endif /* LinReg_hpp */