#ifndef MLPP_TANH_REG_OLD_H #define MLPP_TANH_REG_OLD_H // // TanhReg.hpp // // Created by Marc Melikyan on 10/2/20. // #include "core/math/math_defs.h" #include <string> #include <vector> class MLPPTanhRegOld { public: MLPPTanhRegOld(std::vector<std::vector<real_t>> inputSet, std::vector<real_t> outputSet, std::string reg = "None", real_t lambda = 0.5, real_t alpha = 0.5); std::vector<real_t> modelSetTest(std::vector<std::vector<real_t>> X); real_t modelTest(std::vector<real_t> x); void gradientDescent(real_t learning_rate, int max_epoch, bool UI = false); void SGD(real_t learning_rate, int max_epoch, bool UI = false); void MBGD(real_t learning_rate, int max_epoch, int mini_batch_size, bool UI = false); real_t score(); void save(std::string fileName); private: real_t Cost(std::vector<real_t> y_hat, std::vector<real_t> y); std::vector<real_t> Evaluate(std::vector<std::vector<real_t>> X); std::vector<real_t> propagate(std::vector<std::vector<real_t>> X); real_t Evaluate(std::vector<real_t> x); real_t propagate(std::vector<real_t> x); void forwardPass(); std::vector<std::vector<real_t>> inputSet; std::vector<real_t> outputSet; std::vector<real_t> z; std::vector<real_t> y_hat; std::vector<real_t> weights; real_t bias; int n; int k; // UI Portion void UI(int epoch, real_t cost_prev); // Regularization Params std::string reg; real_t lambda; real_t alpha; /* This is the controlling param for Elastic Net*/ }; #endif /* TanhReg_hpp */