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55 lines
1.4 KiB
C++
55 lines
1.4 KiB
C++
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#ifndef MLPP_PROBIT_REG_H
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#define MLPP_PROBIT_REG_H
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//
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// ProbitReg.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 <string>
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#include <vector>
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class ProbitReg {
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public:
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ProbitReg(std::vector<std::vector<double>> inputSet, std::vector<double> outputSet, std::string reg = "None", double lambda = 0.5, double alpha = 0.5);
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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 = 0, bool UI = 1);
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void MLE(double learning_rate, int max_epoch = 0, bool UI = 1);
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void SGD(double learning_rate, int max_epoch = 0, 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);
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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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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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double lambda;
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double alpha; /* This is the controlling param for Elastic Net*/
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};
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#endif /* ProbitReg_hpp */
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