#ifndef MLPP_UTILITIES_H #define MLPP_UTILITIES_H // // Utilities.hpp // // Created by Marc Melikyan on 1/16/21. // #include "core/containers/vector.h" #include "core/math/math_defs.h" #include "core/string/ustring.h" #include "core/variant/variant.h" #include "core/object/reference.h" #include "../lin_alg/mlpp_matrix.h" #include "../lin_alg/mlpp_vector.h" #include <string> #include <tuple> #include <vector> class MLPPUtilities : public Reference { GDCLASS(MLPPUtilities, Reference); public: // Weight Init static std::vector<real_t> weightInitialization(int n, std::string type = "Default"); static real_t biasInitialization(); static std::vector<std::vector<real_t>> weightInitialization(int n, int m, std::string type = "Default"); static std::vector<real_t> biasInitialization(int n); enum WeightDistributionType { WEIGHT_DISTRIBUTION_TYPE_DEFAULT = 0, WEIGHT_DISTRIBUTION_TYPE_XAVIER_NORMAL, WEIGHT_DISTRIBUTION_TYPE_XAVIER_UNIFORM, WEIGHT_DISTRIBUTION_TYPE_HE_NORMAL, WEIGHT_DISTRIBUTION_TYPE_HE_UNIFORM, WEIGHT_DISTRIBUTION_TYPE_LE_CUN_NORMAL, WEIGHT_DISTRIBUTION_TYPE_LE_CUN_UNIFORM, WEIGHT_DISTRIBUTION_TYPE_UNIFORM, }; void weight_initializationv(Ref<MLPPVector> weights, WeightDistributionType type = WEIGHT_DISTRIBUTION_TYPE_DEFAULT); void weight_initializationm(Ref<MLPPMatrix> weights, WeightDistributionType type = WEIGHT_DISTRIBUTION_TYPE_DEFAULT); real_t bias_initializationr(); void bias_initializationv(Ref<MLPPVector> z); // Cost/Performance related Functions real_t performance(std::vector<real_t> y_hat, std::vector<real_t> y); real_t performance(std::vector<std::vector<real_t>> y_hat, std::vector<std::vector<real_t>> y); real_t performance_vec(const Ref<MLPPVector> &y_hat, const Ref<MLPPVector> &output_set); real_t performance_mat(const Ref<MLPPMatrix> &y_hat, const Ref<MLPPMatrix> &y); real_t performance_pool_int_array_vec(PoolIntArray y_hat, const Ref<MLPPVector> &output_set); // Parameter Saving Functions void saveParameters(std::string fileName, std::vector<real_t> weights, real_t bias, bool app = false, int layer = -1); void saveParameters(std::string fileName, std::vector<real_t> weights, std::vector<real_t> initial, real_t bias, bool app = false, int layer = -1); void saveParameters(std::string fileName, std::vector<std::vector<real_t>> weights, std::vector<real_t> bias, bool app = false, int layer = -1); // Gradient Descent related static void UI(std::vector<real_t> weights, real_t bias); static void UI(std::vector<real_t> weights, std::vector<real_t> initial, real_t bias); static void UI(std::vector<std::vector<real_t>> weights, std::vector<real_t> bias); static void print_ui_vb(Ref<MLPPVector> weights, real_t bias); static void print_ui_vib(Ref<MLPPVector> weights, Ref<MLPPVector> initial, real_t bias); static void print_ui_mb(Ref<MLPPMatrix> weights, Ref<MLPPVector> bias); static void CostInfo(int epoch, real_t cost_prev, real_t Cost); static void cost_info(int epoch, real_t cost_prev, real_t cost); static std::vector<std::vector<std::vector<real_t>>> createMiniBatches(std::vector<std::vector<real_t>> inputSet, int n_mini_batch); 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); 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); struct CreateMiniBatchMVBatch { Vector<Ref<MLPPMatrix>> input_sets; Vector<Ref<MLPPVector>> output_sets; }; struct CreateMiniBatchMMBatch { Vector<Ref<MLPPMatrix>> input_sets; Vector<Ref<MLPPMatrix>> output_sets; }; static Vector<Ref<MLPPMatrix>> create_mini_batchesm(const Ref<MLPPMatrix> &input_set, int n_mini_batch); static CreateMiniBatchMVBatch create_mini_batchesmv(const Ref<MLPPMatrix> &input_set, const Ref<MLPPVector> &output_set, int n_mini_batch); static CreateMiniBatchMMBatch create_mini_batchesmm(const Ref<MLPPMatrix> &input_set, const Ref<MLPPMatrix> &output_set, int n_mini_batch); Array create_mini_batchesm_bind(const Ref<MLPPMatrix> &input_set, int n_mini_batch); Array create_mini_batchesmv_bind(const Ref<MLPPMatrix> &input_set, const Ref<MLPPVector> &output_set, int n_mini_batch); Array create_mini_batchesmm_bind(const Ref<MLPPMatrix> &input_set, const Ref<MLPPMatrix> &output_set, int n_mini_batch); // F1 score, Precision/Recall, TP, FP, TN, FN, etc. 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" real_t recall(std::vector<real_t> y_hat, std::vector<real_t> y); real_t precision(std::vector<real_t> y_hat, std::vector<real_t> y); real_t accuracy(std::vector<real_t> y_hat, std::vector<real_t> y); real_t f1_score(std::vector<real_t> y_hat, std::vector<real_t> y); protected: static void _bind_methods(); }; VARIANT_ENUM_CAST(MLPPUtilities::WeightDistributionType); #endif /* Utilities_hpp */