pmlpp/mlpp/utilities/utilities.h

#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 = 0, int layer = -1);
	void saveParameters(std::string fileName, std::vector<real_t> weights, std::vector<real_t> initial, real_t bias, bool app = 0, int layer = -1);
	void saveParameters(std::string fileName, std::vector<std::vector<real_t>> weights, std::vector<real_t> bias, bool app = 0, 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>>, std::vector<real_t> 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);

	// 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 */