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81 lines
2.0 KiB
C++
81 lines
2.0 KiB
C++
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#ifndef MLPP_MANN_H
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#define MLPP_MANN_H
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//
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// MANN.hpp
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//
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// Created by Marc Melikyan on 11/4/20.
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//
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#include "core/math/math_defs.h"
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#include "core/object/reference.h"
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#include "../regularization/reg.h"
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#include "../lin_alg/mlpp_matrix.h"
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#include "../lin_alg/mlpp_vector.h"
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#include "../hidden_layer/hidden_layer.h"
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#include "../multi_output_layer/multi_output_layer.h"
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#include "../hidden_layer/hidden_layer_old.h"
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#include "../multi_output_layer/multi_output_layer_old.h"
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#include <string>
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#include <vector>
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class MLPPMANN : public Reference {
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GDCLASS(MLPPMANN, Reference);
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public:
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/*
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Ref<MLPPMatrix> get_input_set();
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void set_input_set(const Ref<MLPPMatrix> &val);
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Ref<MLPPMatrix> get_output_set();
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void set_output_set(const Ref<MLPPMatrix> &val);
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*/
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std::vector<std::vector<real_t>> model_set_test(std::vector<std::vector<real_t>> X);
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std::vector<real_t> model_test(std::vector<real_t> x);
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void gradient_descent(real_t learning_rate, int max_epoch, bool ui = false);
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real_t score();
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void save(std::string file_name);
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void add_layer(int n_hidden, std::string activation, std::string weight_init = "Default", std::string reg = "None", real_t lambda = 0.5, real_t alpha = 0.5);
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void add_output_layer(std::string activation, std::string loss, std::string weight_init = "Default", std::string reg = "None", real_t lambda = 0.5, real_t alpha = 0.5);
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bool is_initialized();
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void initialize();
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MLPPMANN(std::vector<std::vector<real_t>> p_input_set, std::vector<std::vector<real_t>> p_output_set);
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MLPPMANN();
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~MLPPMANN();
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private:
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real_t cost(std::vector<std::vector<real_t>> y_hat, std::vector<std::vector<real_t>> y);
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void forward_pass();
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static void _bind_methods();
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std::vector<std::vector<real_t>> _input_set;
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std::vector<std::vector<real_t>> _output_set;
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std::vector<std::vector<real_t>> _y_hat;
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std::vector<MLPPOldHiddenLayer> _network;
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MLPPOldMultiOutputLayer *_output_layer;
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int _n;
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int _k;
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int _n_output;
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bool _initialized;
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};
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#endif /* MANN_hpp */ |