#pragma once
/*
 *  Copyright (C) 2024  Brett Terpstra
 *
 *  This program is free software: you can redistribute it and/or modify
 *  it under the terms of the GNU General Public License as published by
 *  the Free Software Foundation, either version 3 of the License, or
 *  (at your option) any later version.
 *
 *  This program is distributed in the hope that it will be useful,
 *  but WITHOUT ANY WARRANTY; without even the implied warranty of
 *  MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
 *  GNU General Public License for more details.
 *
 *  You should have received a copy of the GNU General Public License
 *  along with this program.  If not, see <https://www.gnu.org/licenses/>.
 */

#ifndef BLT_GP_EXAMPLE_SYMBOLIC_REGRESSION_H
#define BLT_GP_EXAMPLE_SYMBOLIC_REGRESSION_H

#include <blt/gp/program.h>
#include <blt/gp/tree.h>
#include <blt/std/logging.h>
#include <blt/format/format.h>
#include <iostream>

namespace blt::gp::example
{
    class symbolic_regression_t
    {
    public:
        struct context
        {
            float x, y;
        };

    private:
        bool fitness_function(const tree_t& current_tree, fitness_t& fitness, size_t) const
        {
            constexpr static double value_cutoff = 1.e15;
            for (auto& fitness_case : training_cases)
            {
                const auto diff = std::abs(fitness_case.y - current_tree.get_evaluation_value<float>(fitness_case));
                if (diff < value_cutoff)
                {
                    fitness.raw_fitness += diff;
                    if (diff <= 0.01)
                        fitness.hits++;
                }
                else
                    fitness.raw_fitness += value_cutoff;
            }
            fitness.standardized_fitness = fitness.raw_fitness;
            fitness.adjusted_fitness = (1.0 / (1.0 + fitness.standardized_fitness));
            return static_cast<size_t>(fitness.hits) == training_cases.size();
        }

        static float example_function(const float x)
        {
            return x * x * x * x + x * x * x + x * x + x;
        }

    public:
        symbolic_regression_t(const prog_config_t& config, const size_t seed): program{seed, config}
        {
            BLT_INFO("Starting BLT-GP Symbolic Regression Example");
            BLT_DEBUG("Setup Fitness cases");
            for (auto& fitness_case : training_cases)
            {
                constexpr float range = 10;
                constexpr float half_range = range / 2.0;
                const auto x = program.get_random().get_float(-half_range, half_range);
                const auto y = example_function(x);
                fitness_case = {x, y};
            }
        }

        template <typename Ctx>
        auto make_operations(operator_builder<Ctx>& builder)
        {
            static operation_t add{[](const float a, const float b) { return a + b; }, "add"};
            static operation_t sub([](const float a, const float b) { return a - b; }, "sub");
            static operation_t mul([](const float a, const float b) { return a * b; }, "mul");
            static operation_t pro_div([](const float a, const float b) { return b == 0.0f ? 1.0f : a / b; }, "div");
            static operation_t op_sin([](const float a) { return std::sin(a); }, "sin");
            static operation_t op_cos([](const float a) { return std::cos(a); }, "cos");
            static operation_t op_exp([](const float a) { return std::exp(a); }, "exp");
            static operation_t op_log([](const float a) { return a == 0.0f ? 0.0f : std::log(a); }, "log");
            static auto lit = operation_t([this]()
            {
                return program.get_random().get_float(-1.0f, 1.0f);
            }, "lit").set_ephemeral();

            static operation_t op_x([](const context& context)
            {
                return context.x;
            }, "x");

            return builder.build(add, sub, mul, pro_div, op_sin, op_cos, op_exp, op_log, lit, op_x);
        }

        void execute()
        {
            BLT_DEBUG("Setup Types and Operators");
            operator_builder<context> builder{};
            program.set_operations(make_operations(builder));

            BLT_DEBUG("Generate Initial Population");
            auto sel = select_tournament_t{};
            auto fitness = [this](const tree_t& c, fitness_t& f, const size_t i) { return fitness_function(c, f, i); };
            program.generate_population(program.get_typesystem().get_type<float>().id(), fitness, sel, sel, sel);

            BLT_DEBUG("Begin Generation Loop");
            while (!program.should_terminate())
            {
                auto cross = crossover_calls.start_measurement();
                auto mut = mutation_calls.start_measurement();
                auto repo = reproduction_calls.start_measurement();
                BLT_TRACE("------------{Begin Generation %ld}------------", program.get_current_generation());
                BLT_TRACE("Creating next generation");
                program.create_next_generation();
                BLT_TRACE("Move to next generation");
                program.next_generation();
                BLT_TRACE("Evaluate Fitness");
                program.evaluate_fitness();
                const auto& stats = program.get_population_stats();
                BLT_TRACE("Avg Fit: %lf, Best Fit: %lf, Worst Fit: %lf, Overall Fit: %lf",
                    stats.average_fitness.load(std::memory_order_relaxed), stats.best_fitness.load(std::memory_order_relaxed),
                    stats.worst_fitness.load(std::memory_order_relaxed), stats.overall_fitness.load(std::memory_order_relaxed));
                crossover_calls.stop_measurement(cross);
                mutation_calls.stop_measurement(mut);
                reproduction_calls.stop_measurement(repo);
                const auto total = (cross.get_call_difference() * 2) + mut.get_call_difference() + repo.get_call_difference();
                BLT_TRACE("Calls Crossover: %ld, Mutation %ld, Reproduction %ld; %ld", cross.get_call_difference(), mut.get_call_difference(), repo.get_call_difference(), total);
                BLT_TRACE("Value Crossover: %ld, Mutation %ld, Reproduction %ld; %ld", cross.get_value_difference(), mut.get_value_difference(), repo.get_value_difference(), (cross.get_value_difference() * 2 + mut.get_value_difference() + repo.get_value_difference()) - total);
                BLT_TRACE("----------------------------------------------");
                std::cout << std::endl;
            }

            const auto best = program.get_best_individuals<3>();

            BLT_INFO("Best approximations:");
            for (auto& i_ref : best)
            {
                auto& i = i_ref.get();
                BLT_DEBUG("Fitness: %lf, stand: %lf, raw: %lf", i.fitness.adjusted_fitness, i.fitness.standardized_fitness, i.fitness.raw_fitness);
                i.tree.print(program, std::cout);
                std::cout << "\n";
            }
            const auto& stats = program.get_population_stats();
            BLT_INFO("Stats:");
            BLT_INFO("Average fitness: %lf", stats.average_fitness.load());
            BLT_INFO("Best fitness: %lf", stats.best_fitness.load());
            BLT_INFO("Worst fitness: %lf", stats.worst_fitness.load());
            BLT_INFO("Overall fitness: %lf", stats.overall_fitness.load());
            // TODO: make stats helper
        }

    private:
        gp_program program;
        mutation_t mut;
        std::array<context, 200> training_cases{};
    };
}

#endif //BLT_GP_EXAMPLE_SYMBOLIC_REGRESSION_H