mirror of https://github.com/Relintai/pmlpp.git
56 lines
1.4 KiB
C++
56 lines
1.4 KiB
C++
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#ifndef MLPP_SVC_H
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#define MLPP_SVC_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 <string>
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#include <vector>
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class MLPPSVC {
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public:
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MLPPSVC(std::vector<std::vector<double>> inputSet, std::vector<double> outputSet, double C);
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std::vector<double> modelSetTest(std::vector<std::vector<double>> X);
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double modelTest(std::vector<double> x);
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void gradientDescent(double learning_rate, int max_epoch, bool UI = 1);
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void SGD(double learning_rate, int max_epoch, bool UI = 1);
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void MBGD(double learning_rate, int max_epoch, int mini_batch_size, bool UI = 1);
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double score();
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void save(std::string fileName);
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private:
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double Cost(std::vector<double> y_hat, std::vector<double> y, std::vector<double> weights, double C);
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std::vector<double> Evaluate(std::vector<std::vector<double>> X);
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std::vector<double> propagate(std::vector<std::vector<double>> X);
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double Evaluate(std::vector<double> x);
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double propagate(std::vector<double> x);
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void forwardPass();
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std::vector<std::vector<double>> inputSet;
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std::vector<double> outputSet;
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std::vector<double> z;
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std::vector<double> y_hat;
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std::vector<double> weights;
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double bias;
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double 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, double cost_prev);
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};
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#endif /* SVC_hpp */
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