ErrorFunctions.h

//
// Created by martin on 7/15/18.
//

#ifndef INC_4NEURO_ERRORFUNCTION_H
#define INC_4NEURO_ERRORFUNCTION_H

#include "../settings.h"

#include "../Network/NeuralNetwork.h"
#include "../DataSet/DataSet.h"

//TODO HEAVY refactoring needed!

namespace lib4neuro {

    //TODO write smarter using ErrorFunction abstract class?
    enum ErrorFunctionType {
        ErrorFuncMSE
    };

    class ErrorFunction {
    public:

        /**
         *
         * @param weights
         * @return
         */
        virtual double eval(std::vector<double>* weights = nullptr, bool denormalize_data=false,
                bool verbose = false) = 0;

        /**
         *
         * @return
         */
        LIB4NEURO_API virtual size_t get_dimension();

        /**
         *
         * @param params
         * @param grad
         * @param alpha
         * @param batch
         */
        virtual void
        calculate_error_gradient(std::vector<double>& params,
                                 std::vector<double>& grad,
                                 double alpha = 1.0,
                                 size_t batch = 0) = 0;

        /**
         *
         * @param params
         * @param grad
         * @param alpha
         * @param batch
         */
        virtual void
        analyze_error_gradient(std::vector<double>& params,
                                 std::vector<double>& grad,
                                 double alpha = 1.0,
                                 size_t batch = 0) = 0;

        /**
         *
         * @return
         */
        virtual std::shared_ptr<std::vector<double>> get_parameters();

        /**
         * @return
         */
        virtual DataSet* get_dataset();

        /**
         *
         * @return
         */
        virtual DataSet* get_test_dataset();

        /**
         *
         * @return
         */
        NeuralNetwork* get_network_instance();

        /**
         *
         * @param percent_train
         * @return
         */
        void divide_data_train_test(double percent_test);

        /**
         *
         */
        void return_full_data_set_for_training();

        /**
         *
         */
        virtual double eval_on_test_data(std::vector<double>* weights = nullptr, bool verbose = false) = 0;

        /**
         *
         * @param results_file_path
         * @param weights
         * @return
         */
        virtual double eval_on_test_data(std::string results_file_path, std::vector<double>* weights = nullptr,
                bool verbose = false) = 0;

        /**
         *
         * @param results_file_path
         * @param weights
         * @return
         */
        virtual double eval_on_test_data(std::ofstream* results_file_path, std::vector<double>* weights = nullptr,
                bool verbose = false) = 0;

        /**
         *
         * @param data_set
         * @param weights
         * @return
         */
        virtual double eval_on_data_set(DataSet* data_set, std::vector<double>* weights = nullptr,
                bool verbose = false) = 0;

        /**
         *
         * @param data_set
         * @param weights
         * @param results_file_path
         * @return
         */
        virtual double
        eval_on_data_set(DataSet* data_set, std::string results_file_path, std::vector<double>* weights = nullptr,
                bool verbose = false) = 0;

        /**
         *
         * @param data_set
         * @param results_file_path
         * @param weights
         * @return
         */
        virtual double eval_on_data_set(DataSet* data_set,
                                        std::ofstream* results_file_path = nullptr,
                                        std::vector<double>* weights = nullptr,
                                        bool denormalize_data = true,
                                        bool verbose = false) = 0;

        /**
         *
         * @param i
         * @param parameter_vector
         * @param error_vector
         * @return
         */
        virtual double eval_single_item_by_idx(size_t  i, std::vector<double> *parameter_vector, std::vector<double> &error_vector) = 0;

        /**
         *
         * @param error_vector
         * @param gradient_vector
         */
        virtual void calculate_error_gradient_single(std::vector<double> &error_vector, std::vector<double> &gradient_vector) = 0;

        /**
         *
         * @param input
         * @param output
         * @param gradient
         * @param h
         */
        virtual void
        calculate_residual_gradient(std::vector<double>* input,
                                    std::vector<double>* output,
                                    std::vector<double>* gradient,
                                    double h = 1e-3) = 0;

        /**
         *
         * @param input
         * @param output
         * @param parameters
         * @return
         */
        virtual double
        calculate_single_residual(std::vector<double>* input,
                                  std::vector<double>* output,
                                  std::vector<double>* parameters = nullptr) = 0;

    protected:

        /**
         *
         */
        size_t dimension = 0;

        /**
         *
         */
        NeuralNetwork* net = nullptr;

        /**
         *
         */
        DataSet* ds = nullptr;

        /**
         *
         */
        DataSet* ds_full = nullptr;

        /**
         *
         */
        DataSet* ds_test = nullptr;
    };

    class MSE : public ErrorFunction{

    public:
        /**
         * Constructor for single neural network
         * @param net
         * @param ds
         */
        LIB4NEURO_API MSE(NeuralNetwork* net, DataSet* ds);

        /**
         *
         * @param weights
         * @return
         */
        LIB4NEURO_API double eval(std::vector<double>* weights = nullptr,
                                  bool denormalize_data = false,
                                  bool verbose = false) override;

        /**
         *
         * @param params
         * @param grad
         * @param alpha
         * @param batch
         */
        LIB4NEURO_API void
        calculate_error_gradient(std::vector<double>& params,
                                 std::vector<double>& grad,
                                 double alpha = 1.0,
                                 size_t batch = 0) override;

        /**
         *
         * @param params
         * @param grad
         * @param alpha
         * @param batch
         */
        LIB4NEURO_API void
        analyze_error_gradient(std::vector<double>& params,
                                 std::vector<double>& grad,
                                 double alpha = 1.0,
                                 size_t batch = 0) override;

        /**
         * Evaluates the function f(x) = 0 - MSE(x) for a
         * specified input x
         *
         * @param input
         * @return
         */
        LIB4NEURO_API
        virtual double calculate_single_residual(std::vector<double>* input,
                                                 std::vector<double>* output,
                                                 std::vector<double>* parameters) override;

        /**
         * Compute gradient of the residual function f(x) = 0 - MSE(x) for a specific input x.
         * The method uses the central difference method.
         *
         * @param[in] input Vector being a single input
         * @param[out] gradient Resulting gradient
         * @param[in] h Step used in the central difference
         */
        LIB4NEURO_API void
        calculate_residual_gradient(std::vector<double>* input,
                                    std::vector<double>* output,
                                    std::vector<double>* gradient,
                                    double h=1e-3) override;

        /**
         *
         * @param input
         * @return
         */
        LIB4NEURO_API double eval_on_single_input(std::vector<double>* input,
                                                  std::vector<double>* output,
                                                  std::vector<double>* weights = nullptr);

        /**
         *
         * @param weights
         * @return
         */
        LIB4NEURO_API double eval_on_test_data(std::vector<double>* weights = nullptr, bool verbose = false) override;

        /**
         *
         * @param results_file_path
         * @param weights
         * @return
         */
        LIB4NEURO_API double eval_on_test_data(std::string results_file_path = nullptr,
                                               std::vector<double>* weights = nullptr,
                                               bool verbose = false);

        /**
         *
         * @param results_file_path
         * @param weights
         * @return
         */
        LIB4NEURO_API double eval_on_test_data(std::ofstream* results_file_path,
                                               std::vector<double>* weights = nullptr,
                                               bool verbose = false) override;

        /**
         *
         * @param data_set
         * @param results_file_path
         * @param weights
         * @return
         */
        LIB4NEURO_API double eval_on_data_set(DataSet* data_set,
                                              std::ofstream* results_file_path,
                                              std::vector<double>* weights = nullptr,
                                              bool denormalize_data = false,
                                              bool verbose = false) override;

        /**
         *
         * @param data_set
         * @param weights
         * @return
         */
        LIB4NEURO_API double eval_on_data_set(DataSet* data_set,
                                              std::vector<double>* weights = nullptr,
                                              bool verbose = false) override;

        /**
         *
         * @param data_set
         * @param results_file_path
         * @param weights
         * @return
         */
        LIB4NEURO_API double eval_on_data_set(DataSet* data_set,
                                              std::string results_file_path,
                                              std::vector<double>* weights = nullptr,
                                              bool verbose = false) override;

        /**
         *
         * @param i
         * @param parameter_vector
         * @param error_vector
         * @return
         */
        LIB4NEURO_API virtual double eval_single_item_by_idx(size_t  i, std::vector<double> *parameter_vector, std::vector<double> &error_vector) override;

        /**
         *
         * @param error_vector
         * @param gradient_vector
         */
        LIB4NEURO_API virtual void calculate_error_gradient_single(std::vector<double> &error_vector, std::vector<double> &gradient_vector) override;
    };

    class ErrorSum : public ErrorFunction {
    public:
        /**
         *
         */
        LIB4NEURO_API ErrorSum();

        /**
         *
         */
        LIB4NEURO_API ~ErrorSum();

        /**
         *
         * @param weights
         * @return
         */
        LIB4NEURO_API double eval(std::vector<double>* weights = nullptr,
                                  bool denormalize_data = false,
                                  bool verbose = false);

        /**
         *
         * @param weights
         * @return
         */
        LIB4NEURO_API double eval_on_test_data(std::vector<double>* weights = nullptr, bool verbose = false) override;

        /**
         *
         * @param results_file_path
         * @param weights
         * @return
         */
        LIB4NEURO_API double eval_on_test_data(std::string results_file_path,
                                               std::vector<double>* weights = nullptr,
                                               bool verbose = false) override;

        /**
         *
         * @param results_file_path
         * @param weights
         * @return
         */
        LIB4NEURO_API double eval_on_test_data(std::ofstream* results_file_path,
                                               std::vector<double>* weights = nullptr,
                                               bool verbose = false) override;

        /**
         *
         * @param data_set
         * @param weights
         * @return
         */
        LIB4NEURO_API double eval_on_data_set(DataSet* data_set,
                                              std::vector<double>* weights = nullptr,
                                              bool verbose = false) override;

        /**
         *
         * @param data_set
         * @param results_file_path
         * @param weights
         * @return
         */
        LIB4NEURO_API double eval_on_data_set(DataSet* data_set,
                                              std::string results_file_path,
                                              std::vector<double>* weights = nullptr,
                                              bool verbose = false) override;

        /**
         *
         * @param data_set
         * @param results_file_path
         * @param weights
         * @return
         */
        LIB4NEURO_API double eval_on_data_set(DataSet* data_set,
                                              std::ofstream* results_file_path,
                                              std::vector<double>* weights = nullptr,
                                              bool denormalize_data = true,
                                              bool verbose = false) override;

        /**
         *
         * @param i
         * @param parameter_vector
         * @param error_vector
         * @return
         */
        LIB4NEURO_API virtual double eval_single_item_by_idx(size_t  i, std::vector<double> *parameter_vector, std::vector<double> &error_vector) override;

        /**
         *
         * @param error_vector
         * @param gradient_vector
         */
        LIB4NEURO_API virtual void calculate_error_gradient_single(std::vector<double> &error_vector, std::vector<double> &gradient_vector) override;

        /**
         *
         * @param F
         */
        LIB4NEURO_API void add_error_function(ErrorFunction* F, double alpha = 1.0);

        /**
         *
         * @return
         */
        LIB4NEURO_API size_t get_dimension() override;

        /**
         *
         * @param params
         * @param grad
         * @param alpha
         * @param batch
         */
        LIB4NEURO_API void
        calculate_error_gradient(std::vector<double>& params,
                                 std::vector<double>& grad,
                                 double alpha = 1.0,
                                 size_t batch = 0) override;
        /**
         *
         * @param params
         * @param grad
         * @param alpha
         * @param batch
         */
        LIB4NEURO_API void
        analyze_error_gradient(std::vector<double>& params,
                                 std::vector<double>& grad,
                                 double alpha = 1.0,
                                 size_t batch = 0) override;

        LIB4NEURO_API void
        calculate_residual_gradient(std::vector<double>* input,
                                    std::vector<double>* output,
                                    std::vector<double>* gradient,
                                    double h = 1e-3) override;

        LIB4NEURO_API double
        calculate_single_residual(std::vector<double>* input,
                                  std::vector<double>* output,
                                  std::vector<double>* parameters = nullptr) override;


        /**
         *
         * @return
         */
        LIB4NEURO_API std::shared_ptr<std::vector<double>> get_parameters() override;

        /**
         *
         * @return
         */
        LIB4NEURO_API DataSet* get_dataset() override;

    protected:
        std::vector<ErrorFunction*>* summand;
        std::vector<double>* summand_coefficient;
    };
}

#endif //INC_4NEURO_ERRORFUNCTION_H