pmlpp/mlpp/mlp/mlp_old.h

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#ifndef MLPP_MLP_OLD_H
#define MLPP_MLP_OLD_H
//
// MLP.hpp
//
// Created by Marc Melikyan on 11/4/20.
//
#include "core/containers/vector.h"
#include "core/math/math_defs.h"
#include "core/string/ustring.h"
#include "core/variant/variant.h"
#include "core/object/reference.h"
#include "../regularization/reg.h"
#include "../lin_alg/mlpp_matrix.h"
#include "../lin_alg/mlpp_vector.h"
#include <map>
#include <string>
#include <vector>
class MLPPMLPOld {
public:
MLPPMLPOld(std::vector<std::vector<real_t>> inputSet, std::vector<real_t> outputSet, int n_hidden, 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::tuple<std::vector<std::vector<real_t>>, std::vector<std::vector<real_t>>> propagate(std::vector<std::vector<real_t>> X);
real_t Evaluate(std::vector<real_t> x);
std::tuple<std::vector<real_t>, std::vector<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> y_hat;
std::vector<std::vector<real_t>> weights1;
std::vector<real_t> weights2;
std::vector<real_t> bias1;
real_t bias2;
std::vector<std::vector<real_t>> z2;
std::vector<std::vector<real_t>> a2;
int n;
int k;
int n_hidden;
// Regularization Params
std::string reg;
real_t lambda; /* Regularization Parameter */
real_t alpha; /* This is the controlling param for Elastic Net*/
};
#endif /* MLP_hpp */