mirror of https://github.com/Relintai/pmlpp.git
55 lines
2.6 KiB
C++
55 lines
2.6 KiB
C++
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#ifndef MLPP_UTILITIES_OLD_H
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#define MLPP_UTILITIES_OLD_H
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//
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// Utilities.hpp
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//
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// Created by Marc Melikyan on 1/16/21.
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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 <tuple>
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#include <vector>
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class MLPPUtilitiesOld {
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public:
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// Weight Init
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static std::vector<real_t> weightInitialization(int n, std::string type = "Default");
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static real_t biasInitialization();
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static std::vector<std::vector<real_t>> weightInitialization(int n, int m, std::string type = "Default");
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static std::vector<real_t> biasInitialization(int n);
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// Cost/Performance related Functions
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real_t performance(std::vector<real_t> y_hat, std::vector<real_t> y);
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real_t performance(std::vector<std::vector<real_t>> y_hat, std::vector<std::vector<real_t>> y);
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// Parameter Saving Functions
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void saveParameters(std::string fileName, std::vector<real_t> weights, real_t bias, bool app = false, int layer = -1);
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void saveParameters(std::string fileName, std::vector<real_t> weights, std::vector<real_t> initial, real_t bias, bool app = false, int layer = -1);
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void saveParameters(std::string fileName, std::vector<std::vector<real_t>> weights, std::vector<real_t> bias, bool app = false, int layer = -1);
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// Gradient Descent related
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static void UI(std::vector<real_t> weights, real_t bias);
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static void UI(std::vector<real_t> weights, std::vector<real_t> initial, real_t bias);
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static void UI(std::vector<std::vector<real_t>> weights, std::vector<real_t> bias);
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static void CostInfo(int epoch, real_t cost_prev, real_t Cost);
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static std::vector<std::vector<std::vector<real_t>>> createMiniBatches(std::vector<std::vector<real_t>> inputSet, int n_mini_batch);
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static std::tuple<std::vector<std::vector<std::vector<real_t>>>, std::vector<std::vector<real_t>>> createMiniBatches(std::vector<std::vector<real_t>> inputSet, std::vector<real_t> outputSet, int n_mini_batch);
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static std::tuple<std::vector<std::vector<std::vector<real_t>>>, std::vector<std::vector<std::vector<real_t>>>> createMiniBatches(std::vector<std::vector<real_t>> inputSet, std::vector<std::vector<real_t>> outputSet, int n_mini_batch);
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// F1 score, Precision/Recall, TP, FP, TN, FN, etc.
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std::tuple<real_t, real_t, real_t, real_t> TF_PN(std::vector<real_t> y_hat, std::vector<real_t> y); //TF_PN = "True", "False", "Positive", "Negative"
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real_t recall(std::vector<real_t> y_hat, std::vector<real_t> y);
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real_t precision(std::vector<real_t> y_hat, std::vector<real_t> y);
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real_t accuracy(std::vector<real_t> y_hat, std::vector<real_t> y);
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real_t f1_score(std::vector<real_t> y_hat, std::vector<real_t> y);
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};
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#endif /* Utilities_hpp */
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