105 lines
3.9 KiB
C++
105 lines
3.9 KiB
C++
/*
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* <Short Description>
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* Copyright (C) 2025 Brett Terpstra
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*
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* This program is free software: you can redistribute it and/or modify
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* it under the terms of the GNU General Public License as published by
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* the Free Software Foundation, either version 3 of the License, or
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* (at your option) any later version.
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*
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* This program is distributed in the hope that it will be useful,
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* but WITHOUT ANY WARRANTY; without even the implied warranty of
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* MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
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* GNU General Public License for more details.
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*
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* You should have received a copy of the GNU General Public License
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* along with this program. If not, see <https://www.gnu.org/licenses/>.
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*/
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#include "../examples/symbolic_regression.h"
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#include <blt/gp/program.h>
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#include <blt/logging/logging.h>
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using namespace blt::gp;
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struct context
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{
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float x, y;
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};
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prog_config_t config = prog_config_t()
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.set_initial_min_tree_size(2)
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.set_initial_max_tree_size(6)
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.set_elite_count(2)
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.set_crossover_chance(0.8)
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.set_mutation_chance(0.1)
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.set_reproduction_chance(0.1)
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.set_max_generations(50)
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.set_pop_size(500)
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.set_thread_count(1);
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example::symbolic_regression_t regression{691ul, config};
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operation_t addf{[](const float a, const float b) { return a + b; }, "addf"};
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operation_t subf([](const float a, const float b) { return a - b; }, "subf");
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operation_t mulf([](const float a, const float b) { return a * b; }, "mulf");
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operation_t pro_divf([](const float a, const float b) { return b == 0.0f ? 0.0f : a / b; }, "divf");
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operation_t op_sinf([](const float a) { return std::sin(a); }, "sinf");
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operation_t op_cosf([](const float a) { return std::cos(a); }, "cosf");
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operation_t op_expf([](const float a) { return std::exp(a); }, "expf");
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operation_t op_logf([](const float a) { return a <= 0.0f ? 0.0f : std::log(a); }, "logf");
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auto litf = operation_t([]()
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{
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return regression.get_program().get_random().get_float(-1.0f, 1.0f);
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}, "litf").set_ephemeral();
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operation_t op_xf([](const context& context)
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{
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return context.x;
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}, "xf");
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bool fitness_function(const tree_t& current_tree, fitness_t& fitness, size_t)
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{
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constexpr static double value_cutoff = 1.e15;
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for (auto& fitness_case : regression.get_training_cases())
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{
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BLT_GP_UPDATE_CONTEXT(fitness_case);
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auto val = current_tree.get_evaluation_ref<float>(fitness_case);
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const auto diff = std::abs(fitness_case.y - val.get().value());
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if (diff < value_cutoff)
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{
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fitness.raw_fitness += diff;
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if (diff <= 0.01)
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fitness.hits++;
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}
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else
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fitness.raw_fitness += value_cutoff;
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}
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fitness.standardized_fitness = fitness.raw_fitness;
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fitness.adjusted_fitness = (1.0 / (1.0 + fitness.standardized_fitness));
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return static_cast<size_t>(fitness.hits) == regression.get_training_cases().size();
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}
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int main()
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{
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operator_builder<context> builder{};
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builder.build(addf, subf, mulf, pro_divf, op_sinf, op_cosf, op_expf, op_logf, litf, op_xf);
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regression.get_program().set_operations(builder.grab());
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auto& program = regression.get_program();
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static auto sel = select_tournament_t{};
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program.generate_initial_population(program.get_typesystem().get_type<float>().id());
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program.setup_generational_evaluation(fitness_function, sel, sel, sel);
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while (!program.should_terminate())
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{
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BLT_TRACE("---------------\\{Begin Generation {}}---------------", program.get_current_generation());
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BLT_TRACE("Creating next generation");
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program.create_next_generation();
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BLT_TRACE("Move to next generation");
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program.next_generation();
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BLT_TRACE("Evaluate Fitness");
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program.evaluate_fitness();
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}
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}
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