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101 lines
2.8 KiB
C++
101 lines
2.8 KiB
C++
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#ifndef MLPP_LIN_REG_H
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#define MLPP_LIN_REG_H
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//
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// LinReg.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 "core/object/reference.h"
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#include "../lin_alg/mlpp_matrix.h"
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#include "../lin_alg/mlpp_vector.h"
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#include "../regularization/reg.h"
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#include <string>
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#include <vector>
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class MLPPLinReg : public Reference {
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GDCLASS(MLPPLinReg, Reference);
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public:
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/*
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Ref<MLPPMatrix> get_input_set();
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void set_input_set(const Ref<MLPPMatrix> &val);
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Ref<MLPPVector> get_output_set();
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void set_output_set(const Ref<MLPPVector> &val);
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MLPPReg::RegularizationType get_reg();
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void set_reg(const MLPPReg::RegularizationType val);
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real_t get_lambda();
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void set_lambda(const real_t val);
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real_t get_alpha();
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void set_alpha(const real_t val);
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*/
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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 newton_raphson(real_t learning_rate, int max_epoch, bool ui = false);
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void gradient_descent(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 momentum(real_t learning_rate, int max_epoch, int mini_batch_size, real_t gamma, bool ui = false);
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void nag(real_t learning_rate, int max_epoch, int mini_batch_size, real_t gamma, bool ui = false);
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void adagrad(real_t learning_rate, int max_epoch, int mini_batch_size, real_t e, bool ui = false);
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void adadelta(real_t learning_rate, int max_epoch, int mini_batch_size, real_t b1, real_t e, bool ui = false);
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void adam(real_t learning_rate, int max_epoch, int mini_batch_size, real_t b1, real_t b2, real_t e, bool ui = false);
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void adamax(real_t learning_rate, int max_epoch, int mini_batch_size, real_t b1, real_t b2, real_t e, bool ui = false);
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void nadam(real_t learning_rate, int max_epoch, int mini_batch_size, real_t b1, real_t b2, real_t e, 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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void normal_equation();
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real_t score();
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void save(std::string fileName);
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bool is_initialized();
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void initialize();
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MLPPLinReg(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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MLPPLinReg();
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~MLPPLinReg();
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protected:
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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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std::vector<real_t> evaluatem(std::vector<std::vector<real_t>> X);
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void forward_pass();
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static void _bind_methods();
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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> _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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int _lambda;
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int _alpha; /* This is the controlling param for Elastic Net*/
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bool _initialized;
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};
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#endif /* LinReg_hpp */
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