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blt-gp
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blt-gp/tests/destructor_test.cpp

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/*
* <Short Description>
* 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/>.
*/
/*
* <Short Description>
* 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/>.
*/
#include <blt/gp/program.h>
#include <blt/profiling/profiler_v2.h>
#include <blt/gp/tree.h>
#include <blt/std/logging.h>
#include <iostream>
#include <atomic>
#include <type_traits>
//static constexpr long SEED = 41912;
static const unsigned long SEED = std::random_device()();
inline std::atomic_uint64_t last_value = 0;
inline std::atomic_uint64_t constructions = 0;
inline std::atomic_uint64_t destructions = 0;
class move_float
{
public:
move_float(): f(new float()), assignment(++last_value)
{
constructions++;
//BLT_TRACE("Value %ld Default Constructed", assignment);
}
explicit move_float(float f): f(new float(f)), assignment(++last_value)
{
constructions++;
//BLT_TRACE("Value %ld Constructed", assignment);
}
explicit operator float() const
{
//BLT_TRACE("Using value %ld", assignment);
return *f;
}
[[nodiscard]] float get() const
{
//BLT_TRACE("Using value %ld", assignment);
return *f;
}
float operator*() const
{
//BLT_TRACE("Using value %ld", assignment);
return *f;
}
void drop() // NOLINT
{
//BLT_TRACE("Drop Called On %ld", assignment);
delete f;
f = nullptr;
destructions++;
}
friend std::ostream& operator<<(std::ostream& stream, const move_float& e)
{
stream << *e;
return stream;
}
private:
float* f = nullptr;
blt::size_t assignment;
};
static_assert(std::is_trivially_copyable_v<move_float>);
//static_assert(std::is_standard_layout_v<move_float>);
struct context
{
float x, y;
};
std::array<context, 200> training_cases;
blt::gp::prog_config_t config = blt::gp::prog_config_t()
.set_initial_min_tree_size(2)
.set_initial_max_tree_size(6)
.set_elite_count(0)
.set_crossover_chance(0)
.set_mutation_chance(0)
.set_reproduction_chance(1.0)
.set_max_generations(5)
.set_pop_size(500)
.set_thread_count(0);
blt::gp::type_provider type_system;
blt::gp::gp_program program{type_system, SEED, config};
blt::gp::operation_t add([](const move_float& a, const move_float& b) { return move_float(*a + *b); }, "add"); // 0
blt::gp::operation_t sub([](const move_float& a, const move_float& b) { return move_float(*a - *b); }, "sub"); // 1
blt::gp::operation_t mul([](const move_float& a, const move_float& b) { return move_float(*a * *b); }, "mul"); // 2
blt::gp::operation_t pro_div([](const move_float& a, const move_float& b) { return move_float(*b == 0.0f ? 1.0f : *a / *b); }, "div"); // 3
blt::gp::operation_t op_sin([](const move_float& a) { return move_float(std::sin(*a)); }, "sin"); // 4
blt::gp::operation_t op_cos([](const move_float& a) { return move_float(std::cos(*a)); }, "cos"); // 5
blt::gp::operation_t op_exp([](const move_float& a) { return move_float(std::exp(*a)); }, "exp"); // 6
blt::gp::operation_t op_log([](const move_float& a) { return move_float(*a == 0.0f ? 0.0f : std::log(*a)); }, "log"); // 7
blt::gp::operation_t lit([]() { // 8
return move_float(program.get_random().get_float(-320.0f, 320.0f));
}, "lit");
blt::gp::operation_t op_x([](const context& context) { // 9
return move_float(context.x);
}, "x");
constexpr auto fitness_function = [](blt::gp::tree_t& current_tree, blt::gp::fitness_t& fitness, blt::size_t) {
constexpr double value_cutoff = 1.e15;
for (auto& fitness_case : training_cases)
{
auto ctx = current_tree.evaluate(&fitness_case);
auto diff = std::abs(fitness_case.y - *current_tree.get_evaluation_ref<move_float>(ctx));
// this will call the drop function.
current_tree.get_evaluation_value<move_float>(ctx);
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<blt::size_t>(fitness.hits) == training_cases.size();
};
float example_function(float x)
{
return x * x * x * x + x * x * x + x * x + x;
}
int main()
{
BLT_INFO("Starting BLT-GP Symbolic Regression Example");
BLT_START_INTERVAL("Symbolic Regression", "Main");
BLT_DEBUG("Setup Fitness cases");
for (auto& fitness_case : training_cases)
{
constexpr float range = 10;
constexpr float half_range = range / 2.0;
auto x = program.get_random().get_float(-half_range, half_range);
auto y = example_function(x);
fitness_case = {x, y};
}
BLT_DEBUG("Setup Types and Operators");
type_system.register_type<move_float>();
blt::gp::operator_builder<context> builder{type_system};
builder.add_operator(add);
builder.add_operator(sub);
builder.add_operator(mul);
builder.add_operator(pro_div);
builder.add_operator(op_sin);
builder.add_operator(op_cos);
builder.add_operator(op_exp);
builder.add_operator(op_log);
builder.add_operator(lit, true);
builder.add_operator(op_x);
program.set_operations(builder.build());
BLT_DEBUG("Generate Initial Population");
auto sel = blt::gp::select_fitness_proportionate_t{};
program.generate_population(type_system.get_type<float>().id(), fitness_function, sel, sel, sel);
BLT_DEBUG("Begin Generation Loop");
while (!program.should_terminate())
{
BLT_TRACE("------------{Begin Generation %ld}------------", program.get_current_generation());
BLT_START_INTERVAL("Symbolic Regression", "Gen");
program.create_next_generation();
BLT_END_INTERVAL("Symbolic Regression", "Gen");
BLT_TRACE("Move to next generation");
BLT_START_INTERVAL("Symbolic Regression", "Fitness");
program.next_generation();
BLT_TRACE("Evaluate Fitness");
program.evaluate_fitness();
BLT_END_INTERVAL("Symbolic Regression", "Fitness");
BLT_TRACE("----------------------------------------------");
std::cout << std::endl;
}
BLT_END_INTERVAL("Symbolic Regression", "Main");
auto best = program.get_best_individuals<1>();
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";
}
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
BLT_PRINT_PROFILE("Symbolic Regression", blt::PRINT_CYCLES | blt::PRINT_THREAD | blt::PRINT_WALL);
BLT_TRACE("Constructions %ld Destructions %ld Difference %ld", constructions.load(), destructions.load(),
std::abs(static_cast<blt::ptrdiff_t>(constructions) - static_cast<blt::ptrdiff_t>(destructions)));
return 0;
}
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