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64 lines
1.6 KiB
C++
64 lines
1.6 KiB
C++
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#ifndef MLPP_C_LOG_LOG_REG_H
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#define MLPP_C_LOG_LOG_REG_H
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//
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// CLogLogReg.hpp
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//
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// Created by Marc Melikyan on 10/2/20.
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//
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#include "core/math/math_defs.h"
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#include <string>
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#include <vector>
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class MLPPCLogLogReg {
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public:
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std::vector<real_t> model_set_test(std::vector<std::vector<real_t>> X);
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real_t model_test(std::vector<real_t> x);
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void gradient_descent(real_t learning_rate, int max_epoch, bool ui = false);
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void mle(real_t learning_rate, int max_epoch, bool ui = false);
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void sgd(real_t learning_rate, int max_epoch, bool ui = false);
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void mbgd(real_t learning_rate, int max_epoch, int mini_batch_size, bool ui = false);
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real_t score();
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MLPPCLogLogReg(std::vector<std::vector<real_t>> p_input_set, std::vector<real_t> p_output_set, std::string p_reg = "None", real_t p_lambda = 0.5, real_t p_alpha = 0.5);
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MLPPCLogLogReg();
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~MLPPCLogLogReg();
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private:
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void weight_initialization(int k);
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void bias_initialization();
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real_t cost(std::vector<real_t> y_hat, std::vector<real_t> y);
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real_t evaluatev(std::vector<real_t> x);
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real_t propagatev(std::vector<real_t> x);
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std::vector<real_t> evaluatem(std::vector<std::vector<real_t>> X);
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std::vector<real_t> propagatem(std::vector<std::vector<real_t>> X);
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void forward_pass();
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std::vector<std::vector<real_t>> _input_set;
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std::vector<real_t> _output_set;
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std::vector<real_t> _y_hat;
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std::vector<real_t> _z;
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std::vector<real_t> _weights;
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real_t bias;
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int _n;
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int _k;
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// Regularization Params
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std::string _reg;
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real_t _lambda;
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real_t _alpha; /* This is the controlling param for Elastic Net*/
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};
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#endif /* CLogLogReg_hpp */
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