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55 lines
1.4 KiB
C++
55 lines
1.4 KiB
C++
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#ifndef MLPP_LOG_REG_H
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#define MLPP_LOG_REG_H
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//
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// LogReg.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 MLPPLogReg {
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public:
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MLPPLogReg(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);
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std::vector<real_t> modelSetTest(std::vector<std::vector<real_t>> X);
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real_t modelTest(std::vector<real_t> x);
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void gradientDescent(real_t learning_rate, int max_epoch, bool UI = 1);
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void MLE(real_t learning_rate, int max_epoch, bool UI = 1);
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void SGD(real_t learning_rate, int max_epoch, bool UI = 1);
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void MBGD(real_t learning_rate, int max_epoch, int mini_batch_size, bool UI = 1);
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real_t score();
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void save(std::string fileName);
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private:
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real_t Cost(std::vector<real_t> y_hat, std::vector<real_t> y);
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std::vector<real_t> Evaluate(std::vector<std::vector<real_t>> X);
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real_t Evaluate(std::vector<real_t> x);
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void forwardPass();
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std::vector<std::vector<real_t>> inputSet;
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std::vector<real_t> outputSet;
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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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real_t learning_rate;
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// Regularization Params
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std::string reg;
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real_t lambda; /* Regularization Parameter */
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real_t alpha; /* This is the controlling param for Elastic Net*/
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};
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#endif /* LogReg_hpp */
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