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56 lines
1.5 KiB
C++
56 lines
1.5 KiB
C++
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#ifndef MLPP_SVC_OLD_H
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#define MLPP_SVC_OLD_H
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//
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// SVC.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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// https://towardsdatascience.com/svm-implementation-from-scratch-python-2db2fc52e5c2
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// Illustratd a practical definition of the Hinge Loss function and its gradient when optimizing with SGD.
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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 MLPPSVCOld {
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public:
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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 = 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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void save(std::string fileName);
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MLPPSVCOld(std::vector<std::vector<real_t>> inputSet, std::vector<real_t> outputSet, real_t C);
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private:
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real_t Cost(std::vector<real_t> y_hat, std::vector<real_t> y, std::vector<real_t> weights, real_t C);
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std::vector<real_t> Evaluate(std::vector<std::vector<real_t>> X);
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std::vector<real_t> propagate(std::vector<std::vector<real_t>> X);
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real_t Evaluate(std::vector<real_t> x);
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real_t propagate(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> z;
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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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real_t C;
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int n;
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int k;
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// UI Portion
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void UI(int epoch, real_t cost_prev);
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};
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#endif /* SVC_hpp */
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