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113 lines
5.1 KiB
C++
113 lines
5.1 KiB
C++
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#ifndef MLPP_UTILITIES_H
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#define MLPP_UTILITIES_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/containers/vector.h"
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#include "core/math/math_defs.h"
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#include "core/string/ustring.h"
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#include "core/variant/variant.h"
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#include "core/object/reference.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 <string>
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#include <tuple>
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#include <vector>
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class MLPPUtilities : public Reference {
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GDCLASS(MLPPUtilities, Reference);
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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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enum WeightDistributionType {
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WEIGHT_DISTRIBUTION_TYPE_DEFAULT = 0,
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WEIGHT_DISTRIBUTION_TYPE_XAVIER_NORMAL,
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WEIGHT_DISTRIBUTION_TYPE_XAVIER_UNIFORM,
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WEIGHT_DISTRIBUTION_TYPE_HE_NORMAL,
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WEIGHT_DISTRIBUTION_TYPE_HE_UNIFORM,
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WEIGHT_DISTRIBUTION_TYPE_LE_CUN_NORMAL,
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WEIGHT_DISTRIBUTION_TYPE_LE_CUN_UNIFORM,
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WEIGHT_DISTRIBUTION_TYPE_UNIFORM,
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};
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void weight_initializationv(Ref<MLPPVector> weights, WeightDistributionType type = WEIGHT_DISTRIBUTION_TYPE_DEFAULT);
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void weight_initializationm(Ref<MLPPMatrix> weights, WeightDistributionType type = WEIGHT_DISTRIBUTION_TYPE_DEFAULT);
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real_t bias_initializationr();
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void bias_initializationv(Ref<MLPPVector> z);
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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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real_t performance_vec(const Ref<MLPPVector> &y_hat, const Ref<MLPPVector> &output_set);
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real_t performance_mat(const Ref<MLPPMatrix> &y_hat, const Ref<MLPPMatrix> &y);
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real_t performance_pool_int_array_vec(PoolIntArray y_hat, const Ref<MLPPVector> &output_set);
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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 print_ui_vb(Ref<MLPPVector> weights, real_t bias);
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static void print_ui_vib(Ref<MLPPVector> weights, Ref<MLPPVector> initial, real_t bias);
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static void print_ui_mb(Ref<MLPPMatrix> weights, Ref<MLPPVector> bias);
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static void CostInfo(int epoch, real_t cost_prev, real_t Cost);
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static void cost_info(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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struct CreateMiniBatchMVBatch {
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Vector<Ref<MLPPMatrix>> input_sets;
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Vector<Ref<MLPPVector>> output_sets;
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};
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struct CreateMiniBatchMMBatch {
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Vector<Ref<MLPPMatrix>> input_sets;
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Vector<Ref<MLPPMatrix>> output_sets;
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};
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static Vector<Ref<MLPPMatrix>> create_mini_batchesm(const Ref<MLPPMatrix> &input_set, int n_mini_batch);
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static CreateMiniBatchMVBatch create_mini_batchesmv(const Ref<MLPPMatrix> &input_set, const Ref<MLPPVector> &output_set, int n_mini_batch);
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static CreateMiniBatchMMBatch create_mini_batchesmm(const Ref<MLPPMatrix> &input_set, const Ref<MLPPMatrix> &output_set, int n_mini_batch);
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Array create_mini_batchesm_bind(const Ref<MLPPMatrix> &input_set, int n_mini_batch);
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Array create_mini_batchesmv_bind(const Ref<MLPPMatrix> &input_set, const Ref<MLPPVector> &output_set, int n_mini_batch);
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Array create_mini_batchesmm_bind(const Ref<MLPPMatrix> &input_set, const Ref<MLPPMatrix> &output_set, 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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protected:
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static void _bind_methods();
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};
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VARIANT_ENUM_CAST(MLPPUtilities::WeightDistributionType);
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#endif /* Utilities_hpp */
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