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Neural animation tweaks

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Nikita Lisitsa 2021-01-16 15:06:39 +03:00
parent 63e2d226d2
commit a11c5d7c39
1 changed files with 5 additions and 9 deletions
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14
examples/animation_2d.cpp
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@ -682,7 +682,7 @@ struct animation_2d_app
std::size_t train_iterations = 0; std::size_t train_iterations = 0;
std::size_t const max_train_iterations = 1024*8; std::size_t const max_train_iterations = 1024*8;
float const randomize_amplitude = 10.f; float const randomize_amplitude = 10.f;
static constexpr auto mutation_amplitude = [](float t){ return 10.f * geom::lerp(1.f, 0.01f, t); }; static constexpr auto mutation_amplitude = [](float t){ return std::pow(10.f, 1.f + geom::lerp(0.f, -2.f, t)); };
float best_score = 0.f; float best_score = 0.f;
bool const warm_start = false; bool const warm_start = false;
@ -1200,18 +1200,14 @@ void animation_2d_app::do_train()
} }
std::vector<std::pair<float, std::size_t>> scores(population.size()); std::vector<std::pair<float, std::size_t>> scores(population.size());
std::atomic<std::size_t> finished_count{0}; std::vector<async::future<void>> futures;
for (std::size_t i = 0; i < population.size(); ++i) for (std::size_t i = 0; i < population.size(); ++i)
{ {
bg.dispatch([&, i, rng = rng]() mutable { futures.push_back(bg.dispatch([&, i, rng = rng]() mutable {
scores[i] = {eval_score(population[i], rng), i}; scores[i] = {eval_score(population[i], rng), i};
++finished_count; }));
});
} }
bg.wait(); bg.wait_all(futures.begin(), futures.end()).get();
if (finished_count.load() != population.size())
throw std::runtime_error("bg.wait() didn't wait for all tasks to finish");
std::sort(scores.begin(), scores.end(), [](auto const & p1, auto const & p2){ return p1.first > p2.first; }); std::sort(scores.begin(), scores.end(), [](auto const & p1, auto const & p2){ return p1.first > p2.first; });